US20030097330A1 - System and method for detecting fraudulent transactions - Google Patents

System and method for detecting fraudulent transactions Download PDF

Info

Publication number
US20030097330A1
US20030097330A1 US10/298,814 US29881402A US2003097330A1 US 20030097330 A1 US20030097330 A1 US 20030097330A1 US 29881402 A US29881402 A US 29881402A US 2003097330 A1 US2003097330 A1 US 2003097330A1
Authority
US
United States
Prior art keywords
transaction
fraud
score
vendor
plurality
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Abandoned
Application number
US10/298,814
Inventor
James Hillmer
Richard Jones
Colleen Gessner
Connie Johnston
Kelly Lewis
Sanjay Deshpande
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Amway Corp
Original Assignee
Amway Corp
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority to US53529800A priority Critical
Application filed by Amway Corp filed Critical Amway Corp
Priority to US10/298,814 priority patent/US20030097330A1/en
Publication of US20030097330A1 publication Critical patent/US20030097330A1/en
Application status is Abandoned legal-status Critical

Links

Images

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q20/00Payment architectures, schemes or protocols
    • G06Q20/38Payment protocols; Details thereof
    • G06Q20/40Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists
    • G06Q20/401Transaction verification
    • G06Q20/4016Transaction verification involving fraud or risk level assessment in transaction processing
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q20/00Payment architectures, schemes or protocols
    • G06Q20/08Payment architectures
    • G06Q20/20Point-of-sale [POS] network systems
    • G06Q20/206Point-of-sale [POS] network systems comprising security or user identification provision, e.g. password entry
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q20/00Payment architectures, schemes or protocols
    • G06Q20/22Payment schemes or models
    • G06Q20/24Credit schemes, i.e. "pay after"
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q20/00Payment architectures, schemes or protocols
    • G06Q20/38Payment protocols; Details thereof
    • G06Q20/40Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q20/00Payment architectures, schemes or protocols
    • G06Q20/38Payment protocols; Details thereof
    • G06Q20/40Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists
    • G06Q20/403Solvency checks
    • G06Q20/4037Remote solvency checks
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/02Banking, e.g. interest calculation, credit approval, mortgages, home banking or on-line banking
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/02Banking, e.g. interest calculation, credit approval, mortgages, home banking or on-line banking
    • G06Q40/025Credit processing or loan processing, e.g. risk analysis for mortgages
    • GPHYSICS
    • G07CHECKING-DEVICES
    • G07FCOIN-FREED OR LIKE APPARATUS
    • G07F7/00Mechanisms actuated by objects other than coins to free or to actuate vending, hiring, coin or paper currency dispensing or refunding apparatus
    • G07F7/08Mechanisms actuated by objects other than coins to free or to actuate vending, hiring, coin or paper currency dispensing or refunding apparatus by coded identity card or credit card or other personal identification means

Abstract

A system for detecting fraudulent transactions is disclosed. The system breaks the transaction down into its component parameters. A first score is computed depending on the propensity of the transacted commodity to be involved in fraud. A second score is computed as a function of the authentication of the remaining parameters of the transaction. A total fraud score is computed from the first score and the second score and compared against a fraud threshold to determine the likelihood of the transaction being fraudulent.

Description

    BACKGROUND
  • Everyday, millions of commercial transactions take place between customers and vendors of goods and/or services (“commodities”). Many of these transactions are consummated face to face in physical retail establishments, over the telephone, such as with catalog based vendors, through the Internet with e-commerce based vendors or through some combination thereof. [0001]
  • Each of these transactions involves the exchange of value, such as cash/currency, bank draft/check or credit, for commodities. One other commonality that all of these transactions share is the potential that the customer making the transaction is acting fraudulently to acquire the commodities from the vendor. [0002]
  • Fraud often occurs when the customer knowingly utilizes some form of value, such as a bank draft/check or credit card that has no value or does not belong to the customer. For example, a customer writes a check knowing there are no funds to back up that check or utilizes a fraudulently obtained or stolen credit card. While fraudulent transactions only make up a small percentage of the total number of transactions completed at any given time, the amount of revenue and/or resources lost due to fraud is substantial. [0003]
  • Vendors of goods and services rely on a number of different methods to detect fraudulent transactions. Some rely on the fraud detection systems of third party credit card processors or third party check verification systems to determine if the customer is trying to commit fraud. Other vendors rely on internal fraud detection systems. In either case, it is preferable to detect the fraudulent transaction before the customer receives the commodities, prevent the loss to the vendor in the first place and reduce or eliminate the expense of resources devoted to attempting to recover those commodities and/or financial compensation. [0004]
  • An important characteristic of these fraud detection systems is their error rate. Errors include false negative responses, where a fraudulent transaction goes undetected and false positive responses, where valid transactions are mis-flagged as fraudulent. A high false negative rate indicates that the system is not performing its intended function and results in continued losses to the vendor due to fraud. A high false positive rate costs the vendor in potentially lost sales and lost resources in further investigating and validating the mis-flagged transactions. [0005]
  • One typical fraud detection system functions by analyzing parameters of the transaction and attempting to identify characteristics which indicate that the transaction is fraudulent. This system breaks down the transaction into a few select component data parameters and single variable relationships. Points are assigned to each parameter/relationship based on the information it represents and whether it matches with known data. If the number of points of any one parameter/relationship exceeds a pre-determined threshold, potential fraud is indicated. Alternatively, the points of all of the parameters/relationships are summed to create a total score and then that total score is compared to a threshold. If the total score exceeds the threshold, fraudulent activity may be indicated within the transaction. An exemplary fraud detection system utilizing single parameters or relationships to identify a primary fraud characteristic is shown in more detail in U.S. Pat. No. 6,029,154, entitled “METHOD AND SYSTEM FOR DETECTING FRAUD IN A CREDIT CARD TRANSACTION OVER THE INTERNET” to Pettitt. [0006]
  • Another fraud detection system utilizes a more complex predictive model, such as a neural network or other form of model which utilizes self-learning of relationships among variables from historical transaction data. This system uses these complex models to analyze the transaction and predict whether or not the transaction is potentially fraudulent. The model is able to automatically correlate relationships among all of the parameters of the transaction to each other, and not just the single variable relationships of the above detection system. An exemplary fraud detection system utilizing these self-learning models is shown in more detail in U.S. Pat. No. 5,819,226, entitled “FRAUD DETECTION USING PREDICTIVE MODELING” to Gopinathan, et al. Self-learning based systems are complex, difficult to develop and require significant training and maintenance to maintain accuracy. [0007]
  • SUMMARY
  • The present invention is defined by the following claims, and nothing in this section should be taken as a limitation on those claims. By way of introduction, the preferred embodiments described below relate to a method for detecting fraudulent transactions between a customer and a vendor. The method includes receiving a plurality of transaction parameters from the vendor, where the plurality of transaction parameters represents at least one transaction for at least one commodity between the customer and the vendor. A first score is computed as a function of each commodity involved in the transaction. A second score is computed as a function of each of a first one or more of the plurality of transaction parameters. A fraud score is computed based on the first and second scores. The transaction is indicated to be potentially fraudulent if the fraud score exceeds a first pre-determined threshold. [0008]
  • The preferred embodiments further relate to a fraud detection processor. The processor includes a receiver operative to receive a plurality of transaction parameters from a vendor, where the plurality of transaction parameters represents at least one transaction for one or more commodities between a customer and the vendor. The processor also includes a first score processor coupled with the receiver and operative to compute a first score as a function of each of the one or more commodities. In addition, the processor includes a second score processor coupled with the receiver and operative to compute a second score as a function of each of a first one or more of the plurality of transaction parameters. Further, the processor includes a fraud score processor coupled with the first and second score processors and operative to compute a fraud score based on the first and second scores. Finally, the processor includes fraud determination logic coupled with the fraud score processor and operative to compute a determination of whether the at least one transaction is potentially fraudulent based on a comparison of the fraud score and a first pre-determined threshold and further operative to indicate this determination to the vendor.[0009]
  • BRIEF DESCRIPTION OF THE DRAWINGS
  • FIG. 1 depicts a block diagram of an exemplary commercial transaction utilizing the preferred fraud detection system. [0010]
  • FIGS. 2A & 2B depicts a flow chart detailing a first embodiment of a preferred fraud detection system. [0011]
  • FIGS. 3A & 3B depicts a flow chart detailing a second embodiment of a preferred fraud detection system.[0012]
  • DETAILED DESCRIPTION OF THE PRESENTLY PREFERRED EMBODIMENTS
  • The disclosed fraud detection system is easily maintained and updated and has a low error rate. Further, the disclosed system is modular, being able to be sold and/or provided, in whole or in part, as a service or product to one or more vendors. It will be appreciated that the system, as a service or product, may also be embodied as hardware and/or software and packaged, in whole or in part, as a fraud detection processor. The preferred fraud detection system or processor is comprised of a number of component parts as described below. One or more components of the system or processor may be deployed as part of a vendor's internal transaction processing system while the remainder of the system or processor is operated by an entity, external to, and in communication with the vendor. [0013]
  • In one embodiment, the entire system resides internally as a part of, or coupled with, the vendor's order processing system in compliance with all of its interfaces and gateways. Herein, the phrase “coupled with” is defined to mean directly connected to or indirectly connected with through one or more intermediate components. Such intermediate components may include both hardware and software based components. [0014]
  • Alternatively, most of the fraud detection processing is offloaded to a consumer information provider. Where the consumer information provider is an entity external to the vendor, costs, maintenance, storage and processing considerations are alleviated for the vendor. In addition, centralization of the fraud detection process allows multiple vendors to pool information resources thereby enhancing the detection of fraud. [0015]
  • Further, in yet another alternative embodiment, the entire system resides externally to the vendor and in communication with the vendor's order processing system and is provided, as a “black box” service for example, by the external entity (i.e. transaction parameters are transmitted to the external entity and the external entity returns a fraud indication result). It will be appreciated that the alternative preferred embodiments disclosed herein relate to alternate divisions of the system's components between the vendor and the external fraud processing entity and that all embodiments of the disclosed fraud detection system are contemplated no matter how the division of its component parts is made. [0016]
  • To support multiple vendors, the system is configurable to account for the varying transactional characteristics of each participating vendor, allows each participating vendor the ability to individually re-configure the system to reduce error rates and permits each participating vendor to provide internal statistical, demographic and historical transaction data and/or have access to such data from other vendors (participating or not) to enhance the detection of fraud. [0017]
  • FIG. 1 shows an exemplary transaction [0018] 100 between a customer 102 and a vendor 106. The transaction 100 comprises the purchase of a commodity 112 from the vendor 106. Herein, a commodity 112 is a good and/or service or anything else purchased or traded for value. The customer 102 desires the commodities 112 and the vendor 106 provides the commodities 112. In one embodiment, the customer 102 is another vendor.
  • To complete the transaction [0019] 100, the customer 102 interfaces with the order processing component 104 of the vendor 106, orders 110 a commodity 112 and provides payment information. The vendor 106 processes the order 110, accepts the payment and delivers 114 the commodity 112 to the customer 102.
  • The transaction [0020] 100 can occur in one or more of many different environments. For example, the transaction 100 occurs in a retail establishment between a customer 102 and a cashier (the order processor 104) working for the vendor 106. Alternatively, the transaction occurs between a customer 102 and a catalog based vendor 106 using a telephone order processing system 104 (live operator, automated or a combination thereof) or between a customer 102 and an electronic commerce (“e-commerce”) based vendor 106 using an Internet, network or wireless based order processing system 104. The order processor 104 may even be a vending machine, including a machine used to dispense gasoline. The order processing system 104 may be manual, automated or a combination thereof.
  • Where the customer [0021] 102 attempts to use a credit form of payment, such as a credit card, debit card or bank draft/check, there is an opportunity for defrauding the vendor 106. In such cases, it is preferable to detect such fraudulent activity before the commodity 112 is delivered to the customer 102. Early detection prevents loss of revenue or resources by the vendor 106.
  • FIG. 1 further shows a fraud detection system [0022] 108 which interfaces with the order processing system 104 of the vendor 106 in order to detect fraudulent activity on the part of the customer 102. The system 108 is capable of determining whether or not the transaction 100 is potentially fraudulent while the transaction 100 is still pending, before the commodities 112 have been delivered to the customer 102. Alternatively, the system 108 is capable of analyzing the transaction 100 at a later time to determine whether the payment was potentially fraudulent. Both real-time processing and batch processing are contemplated.
  • The fraud detection system [0023] 108 receives transaction parameters 116 from the vendor's 106 order processing system 104. The transaction parameters 116 represent the pieces of information which make up the transaction 100. Exemplary transaction parameters 116 include the customer's name, address and telephone number, the identity of the commodity being transacted (also known as a “Stock Keeping Unit” or “SKU” number) and the information related to the form of payment being used such as a credit card number or checking account number. A SKU is unique identifier code which identifies a particular commodity. Other parameters 116 may include an alternate shipping address to account for a customer 102 who places an order and has it shipped to an alternate address, such as for a gift.
  • These transaction parameters [0024] 116 may be vendor 106 specific or generic to all vendors 106. Some transaction parameters 116 are based on the nature of the order processing system's 104 requirements to process a given order. For example, a vendor 106 which transacts in products which are not delivered (such as a service), may not collect a shipping address and therefore would not collect this particular transaction parameter 116. Further, e-commerce based vendors 106 may have access to additional parameters 116 such as Internet Protocol addresses, domain addresses or electronic mail addresses. The preferred embodiments disclosed herein are designed to account for the different transaction parameters 116 collected by or available from different vendors 106 and are capable of being configured to account for the availability or unavailability of transaction parameters 116 in the determination of fraud. Table 1.0 (See below) gives a non-exhaustive list of the transaction parameters 116 of the preferred embodiments.
  • The fraud detection system [0025] 108 accepts the transaction parameters 116 from the order processing system 104 and analyzes those parameters 116, as discussed below in more detail. The fraud detection system 108 provides a result or signal 118 to the order processing system 104 signifying whether or not the analyzed transaction parameters 116 indicate that the transaction 100 is likely fraudulent. In one embodiment the result or signal 118 is a binary indicator of fraud or no fraud. Alternatively, the result or signal 118 may be numeric or other data indicating the likelihood of fraud, for example, as compared to a fraudulent value scale. The order processing system 104 is then able to account for that result or signal 118 in the processing of the transaction 100. If the transaction 100 is still pending, the order processing system 104 may pass off the transaction 100 to a manual or automated investigation system, terminate the transaction 100 before the commodities 112 are shipped or delivered 114 or demand further information or an alternative form of payment from the customer before shipping or delivering the commodities 112. Alternatively, the order processing system 104 may utilize internal fraud detection systems to double-check or override the fraud indication signal 118 from the fraud detection system 108. If the transaction 100 has already been completed and the commodities 112 have been shipped to the customer 102, the fraud indicator signal 118 may be used by the order processing system 104 to institute loss mitigation or recovery measures such as recalling the shipped commodities 112 back from the shipping agent or handing over the transaction to an internal or external investigation/collections entity. Transactions 100 determined to be potentially fraudulent or non-fraudulent may also be updated into the appropriate negative (described in more detail below and in FIG. 2B) or positive database (described in more detail below and in FIG. 2A) for use in processing and preventing losses from later transactions 100 by the same customer 102.
  • First Embodiment [0026]
  • Referring now to FIG. 2A, there is shown a flow chart depicting a first embodiment of a fraud detection system [0027] 108. Internally to the vendor 106, the order processing system 104 receives a transaction 100 and initiates the fraud detection process. The fraud detection system 108 receives initial transaction parameters 116 from the order processing system 104 (Block 202).
  • The system performs a payment authorization check (Block [0028] 204). This check involves contacting either an external credit card issuing company or third party check verification service. Alternatively, internal checks are performed for vendor issued credit accounts. The external issuing company or authorization service performs an authorization process on the transaction 100, as is known in the art. This authorization process typically catches stolen credit cards or checks which have already been reported to the issuing company. Further, the authorization process indicates whether the customer has exceeded a credit limit or available funds.
  • Alternatively, for credit card transactions, the authorization check includes Address Verification checking (“AVS”), Card Verification Value Checking (“CVV2”), other fraud detection processes now or later developed by credit card issuing or authorization companies, or combinations thereof. AVS checking performs an additional check, beyond verifying funds and credit card status, to ensure that elements of the address supplied by the customer [0029] 102 match those on record with the issuing company. AVS checks can return results indicating that data is unavailable, or that there is a mismatch or a total or partial match. AVS checking can be repeated with manually tweaked addresses and/or names to correct misspellings, incorrectly specified billing addresses, and other customer service/order entry mistakes.
  • CVV2 checking determines whether or not the customer [0030] 102 is actually in possession of the physical credit card by asking for an identification code imprinted only on the card in a manner which does not get reproduced by credit card imprinting or swipe machines. If the authorization check fails, the transaction is stopped immediately. Otherwise, the processing further continues. However, customers who commit fraud often utilize methods which avoid being detected by standard payment authorization techniques. For example, the customer 102 intercepts credit cards along with the associated address information, in the mail and uses the credit cards before the cards are reported stolen to the issuing company. Further, these standard payment authorization techniques may fail to work with internationally originated transactions 100.
  • The transaction [0031] 100 is also checked to see if the value of the commodities 112 being purchased is greater than a predetermined threshold amount 208 (Block 206). A vendor 106 may determine that transactions 100 whose total value is less a particular threshold are less likely to be fraudulent. Low value transactions 100 are bypassed from further fraud processing to avoid wasting resources, and the transaction 100 is accepted (Block 230). The pre-determined threshold 208 is vendor 106 specific or generalized for multiple vendors 106 and vendors 106 may adjust their threshold 208 periodically. Each vendor 106 may experience more or less fraud for commodities with differing values. In addition, the pre-determined threshold may be dynamically adjusted to account for other transaction parameters 116 and the results of other fraud detection algorithms (as discussed below). An exemplary pre-determined threshold value is $ 100.00.
  • If the value is greater than the pre-determined transaction amount threshold, transaction parameters [0032] 116 of the transaction 100 are compared against a positive database 212 (Block 210). The positive database 212 maintains customer 102 identity information, including one or more of the transaction parameters from past transactions which have been determined to be non-fraudulent. Such customer identity information may include the customer name, credit card number, checking account number, address or combinations thereof. This database 212 accounts for customers 102 who have a known good relationship with a vendor 106 but often have their transactions 100 flagged as fraudulent by fraud detection system 108 for other reasons such as a high transaction amount, shipping to a high risk zip code or an unusually high frequency of orders (“velocity”). As the nature of such positive information is likely highly confidential, such positive databases 212 and comparisons (Block 210) are preferably completed internally to said vendor 106. In addition, this positive data tends to be vendor specific, i.e. just because a customer has not committed fraud at one vendor does not necessarily mean they are not likely to commit fraud at another vendor. If the current customer 102 is found in the positive database 212, the transaction 100 is bypassed from further processing, and the transaction accepted (Block 230). Otherwise the fraud detection system continues its analysis (Block 214). In alternative embodiments, the positive identification is used to decrease the likelihood of a false positive result from the fraud detection system.
  • In order to further analyze the parameters [0033] 116, the form of payment which the customer 102 is using is determined (Blocks 218, 220). Credit card based transactions 100 have different parameters 116 than bank draft or check based transactions 100. In alternative embodiments, the transaction 100 environment is determined, i.e. whether the transaction 100 is originating from a retail establishment, a telephone ordering vendor or an e-commerce based vendor.
  • In one embodiment, the transaction parameters [0034] 116 are formatted and transmitted to a consumer information provider 224 who completes the fraud detection process (Block 222). The consumer information provider 224 is preferably an entity external to the vendor 106 and is preferably a fraud detection service provider who provides fraud detection services to multiple vendors 106. Alternatively, the consumer information provider 224 is another vendor 106 which sells excess fraud processing capacity as a service to other vendors 106 or the consumer information provider 224 is an internal part of the vendor 106 (e.g. part of or coupled with the order processing system 104). The consumer information provider 224 returns a signal 118 indicating whether or not the transaction 100 is likely fraudulent (Block 226). If the transaction is likely non-fraudulent (Block 228), the transaction is accepted (Block 230) and the commodities 112 are delivered. If the transaction 100 is likely fraudulent (Block 228), the system performs the desired vendor specific action to combat this potential fraud (Block 232). For example, the system cancels the transaction and prevents delivery of the commodities 112 to the customer 102, or the transaction is flagged for further review.
  • Referring now to FIG. 2B, there is shown a flow chart depicting the fraud detection processing undertaken by the consumer information provider [0035] 224. The consumer information provider 224 receives the request to determine the fraudulent status of a transaction 100 and the accompanying transaction parameters from the vendor 106 (Block 302). This request is transmitted by telephone, network, Internet or other communication medium. Initially, the transaction 100 is assumed to be non-fraudulent and subsequent processing determines if the transaction is likely fraudulent (Block 304). In alternative embodiments, the initial assumption is that the transaction is likely fraudulent and subsequent processing attempts to disprove this.
  • The consumer information provider [0036] 224 first computes a fraud multiplier for the transaction 100 (Block 306). The fraud multiplier is a score based on the value of the transaction parameters 116 and whether the transaction parameters authenticate against databases 308, 310, 312 of known information. These databases 308, 310, 312 include a customer information database 308, a negative account database 310 and a negative address database 312. Generally, these databases 308, 310, 312 contain specific customer and consumer information, statistical data and historical transaction data known to the participating vendors 106, 316, 318.
  • The customer information database [0037] 308 is a collection of information about the customers 102 of each of the vendors 106, 316, 318. There will be a separate database 308 for each vendor 106, 316, 318 since such customer information is typically only relevant to the particular vendor that the customer belongs to. Alternatively, by collecting the information from each participating vendor 106, 316, 318 together, a broad database is provided to all of the vendors 106, 316, 318, enhancing the accuracy of the fraud detection system and reducing error rates. Further, where the consumer information provider 224 is an internal function of the vendor 106, or due to privacy issues, this data may represent only one vendor's customer information. The customer information database 308 preferably includes data representing information about existing customers such as the customer name, address, how long the customer has been a customer and information about one or more of their past transactions. In the e-commerce environment, this database 308 may further contain information such as the customer's Internet Protocol Address, e-mail address or domain address.
  • The negative account database [0038] 310 and the negative address database 312 include information about past transactions that are known to have been fraudulent. The negative account database 310 includes credit card account numbers and/or checking account numbers used in past fraudulent transactions. The negative address database 312 includes customer, card holder, or shipping addresses previously involved in fraudulent transactions. The data in the databases 310, 312 is provided by the participating vendors 106, 316, 318, financial institutions or other entities. The negative databases 310, 312 are provided as a recognition of the fact that a fraud committed or attempted against one vendor 106, 316, 318 is generally relevant information to all vendors 106, 316, 318. In this way, one vendor 106, 316, 318 benefits from the negative experience of another vendor 106, 316, 318.
  • The databases [0039] 308, 310, 312 may further include the positive databases 212 used in Block 210 of FIG. 2A so that this comparison may be performed by the fraud detection system 108. Alternatively, each participating vendor 106, 316, 318 has access to the other vendors' positive transaction history data where it is relevant across vendors 106, 316, 318. Where positive databases are included, the positive data is used by the fraud detection system 108 as a factor in the fraud multiplier computation and not to override the fraud determination process as is shown in FIG. 2A. Alternatively, where such positive data is relevant across vendors, it may be used to bypass further fraud processing and approve the transaction. Additional databases comprising publicly available data such as credit histories or bankruptcy information, whether internal to the fraud detection system or provided by third party entities, may also be used to augment one or more of the databases 308, 310, 312. Additional databases containing non-public information such as proprietary internal data from other vendors or other private entities, whether internally provided or provided by a third party entity, may also be included.
  • In addition to the above databases [0040] 308, 310, 312, a fraud multiplier point database 314 is also provided. The fraud multiplier point database 314 includes data representing the transaction parameters 116 that a participating vendor 106 provides with each fraud determination request. This data may be different or specific for each participating vendor 106, 316, 318 depending upon each vendors 106 transaction environment and fraud experience. In one embodiment, the data is stored in a common database 314 or alternatively, each vendor 106, 316, 318 may have their own database 314. For example, the customer status transaction parameter can be defined by each vendor 106, 316, 318. For vendors 106, 316, 318 that sell exclusive memberships but still allow non-members to transact, the customer status parameter may indicate a member or non-member status. Alternatively, the customer status parameter indicates a preferred status or how good of a customer that vendor 106, 316, 318 feels the customer is, poor, fair, good or excellent depending on the amount they buy or their frequency of returns, etc. For each transaction parameter 116, a corresponding point value is provided which represents the number of points to be computed into the fraud calculation (described in more detail below) if that particular transaction parameter authenticates or doesn't authenticate in one of the databases 308, 310, 312. The point values are determined at the discretion of each vendor 106 depending on their individual business environments and point values for the same transaction parameters 116 may vary from vendor 106 to vendor 106. For example, some vendors 106, 316, 318 may experience more fraud from particular geographic areas than other vendors 106, 316, 318. These vendors 106, 316, 318 then may assign higher point values to address matches with the particular zip codes covering those geographic areas then might be assigned by another vendor 106, 316, 318.
  • Some transaction parameters [0041] 116 are essentially Boolean variables which have a True value if the value of the parameter can be authenticated against one of the databases 308, 310, 312. In this case, the parameter 116 is assigned points based on whether it is true or false. In addition, some transaction parameters 116 are assigned points based on the value of that parameter 116. For example, the transaction parameter 116 representing the total value of the transaction 100 is assigned a specific number of points as a function of this value (e.g. 0.5 points per $100 of value).
  • Alternatively, the points assigned to a particular transaction parameter [0042] 116 are dependent on the values of one or more other transaction parameters. For example, a transaction parameter 116 representing the credit card number velocity is assigned points based on the frequency of use of that credit card number (itself another transaction parameter 116). A velocity is a frequency of use over a specified period of time or a specified number of transactions. Velocity checks are done for transaction 100 counts (“count velocity”) as well as amounts (“amount velocity”), i.e. the frequency of transactions 100 of a particular value. The velocity checks are performed against customer 102 identities, credit card account numbers, checking account numbers and addresses, cardholder, ship-to or otherwise (e.g., the frequency of address changes). Other relationships can also be captured by the point assignments. For example, depending on the value of one or more transaction parameters 116, one or more other transaction parameters 116 may be further computed with a weighting to increase or decrease significance to the overall fraud determination. Generally, points or point and parameter relationships are assigned and/or computed for a particular transaction parameter 116 as a function of that parameter's 116 relationship to fraudulent transactions. (i.e. whether a particular value of a parameter 116, or relationship among parameters 116, is likely to indicate fraud or not). For example, in one embodiment, the point values for the transaction velocity parameter 116 are adjusted based on geographic dispersion of the locations from which each of the transactions, included in the velocity calculation, were initiated. This accounts for the likelihood of the customer 102 physically being present at all of those locations at the times when the transactions 100 were initiated. An exemplary fraud multiplier point database 314, corresponding transaction parameters 116 and points for a particular vendor 106, is shown in Table 1.0.
    TABLE 1.0
    TRANSACTION PARAMETER POINTS
    Positive Database match −5.00
    Negative Database (this vendor) match +10.00
    Negative Database (other vendor) match +8.50
    Not shipping to card holder address +0.50
    Shipping to card holder address −1.00
    Not shipping to customer address +0.50
    Shipping to customer address −1.00
    Shipping to Freight forwarder +1.00
    Third party address verification (AVS) ok −1.00
    Third party address verification (AVS) partial ok +2.50
    Third party address verification (AVS) not ok +5.00
    Customer Svc. Rep. Suspects fraud +5.00
    High risk zip code +1.50
    Telephone order +0.25
    Total transaction amount +0.50/$100
    Air shipment +0.50
    Customer duration −0.05/year
    Customer status = excellent −1.00
    Customer status = good −0.25
    Customer status = fair +0.25
    Customer status = poor +1.00
    Credit card amount velocity exceeded +0.50
    Credit card count velocity exceeded +0.50
    Customer amount velocity exceeded +0.25
    Customer count velocity exceeded +0.25
    Ship-to address amount velocity exceeded +0.50
    Ship-to address count velocity exceeded +0.50
    Card Verification Value (CVV2) mismatch +0.50
    Card Verification Value (CVV2) match −1.00
  • The databases [0043] 308, 310, 312, 314 are preferably updated periodically. For example, a batch update process is used from each participating vendor 106. Alternatively, the databases 308, 310, 312, 314 are updated in real time as each participating vendor 106 acquires the relevant data. These updates occur automatically or manually. The negative databases 310, 312 are updated by the participating vendors 106, 316, 318 with transactions 100 which are verified by the vendor 106, 316, 319 as being fraudulent. Records are removed from the negative databases 310, 312 when it is determined that the fraudulent indicativeness of the stored information is no longer accurate. In one embodiment, this occurs only after a manual review indicates that the record was entered into the negative databases 310, 312 in error or the customer's or address' fraudulent status has changed. The customer's fraudulent status may change, for example, after a specified time period has elapsed in which there has been no further fraudulent activity by that customer. An address' fraudulent status may change if it is determined that the previous occupants, who perpetrated the original fraud, have moved and new customers now live at the address. In alternative embodiments, records are removed from the negative databases 310, 312 automatically based on pre-determined rules such as a defined period of non-fraudulent activity or through a combination of automated processes and manual review.
  • The fraud multiplier for the transaction is preferably computed by first determining the points for each transaction parameter [0044] 116, as discussed above. To compute the fraud multiplier, the points determined for each transaction parameter 116 are summed to compute a total score (Block 306). Alternatively, other mathematical formulae may be used to compute the fraud multiplier. It is preferable that a mathematical computation be used which is statistically designed to minimize the overall error rate for the fraud detection process.
  • Once the fraud multiplier has been computed, the fraud detection system next computes the SKU points for the transaction (Block [0045] 320). As was discussed above, a SKU is a unique identifier code which identifies particular commodities involved in transactions 100. Each commodity has a unique SKU. Each SKU is associated with a point value indicating that particular commodity's propensity to be the subject of fraud. For each participating vendor 106, 316, 318, an associated SKU point database 322 contains points for the SKU's of the commodities which the particular vendor trades in. The SKU points for the transaction 100 is computed by summing the individual SKU points for each commodity involved in the transaction. The sum of the SKU points (i.e. the minimum SKU point sum) is at least 1. This prevents a transaction 100 with a high fraud multiplier but involving SKU's with no associated points (i.e., SKU's with very low fraud potential) from attaining a fraud score of 0 (e.g., where a customer purchases 1000 tubes of lipstick). Where more than one of the same commodity is being purchased, the SKU points for each is included or alternatively, that commodity may only be counted once. Alternative mathematical formulae may also be used such as diminishing or increasing point values or dynamic point values. For example, the larger the quantity ordered of any one product, the higher the points for that product (i.e., where 500 lipsticks are ordered, the first 100 each have 0 points, the next 100 each have 10 points, the next 100 each have 20 points, etc.) to allow for the possibility that orders of large quantities of any one product are more likely to be fraudulent. However, the mathematical computation used is statistically designed to minimize the overall error rate for the fraud detection process. An exemplary SKU points database 322 showing SKU's and associated point values for a particular vendor 106 is shown in Table 2.0.
    TABLE 2.0
    SKU POINTS DESCRIPTION
    XX001 +10 OFFICE CHAIR-LEATHER
    XX002 +10 FAX MACHINE
    XX003 +10 CORDLESS PHONE-900/DK/CID
    XX004 +10 PLAIN PAPER FAX
    XX005 +10 CORDLESS PHONE-900/2LINE/CID
    XX006 +10 CORDLESS PH-900/2LINE/CID/TA
    XX007 +10 COMM. BREWER W/COFFEE
    XX008 +40 STEREO SYSTEM
    XX009 +20 CD CHANGER/12 CAP
    XX010 +40 CAMCORDER VHS-C
    XX011 +10 BUTCHER BLOCK CART
    XX012 +10 RED DEVIL GRILL
    XX013 +10 PRESSURE COOKER
    XX014 +10 SECURITY AUTO-DIALER
    XX015 +10 CD RACK
    XX016 +15 SECURITY CAMERA
    XX017 +10 COCKTAIL TABLE
    XX018 +10 END TABLE
    XX019 +10 SOFA TABLE
    XX020 +10 LOVESEAT-FLORAL
    XX021 +10 SOFA-FLORAL
    XX022 +15 COMPACT STEREO KEVLAR SPE
    XX023 +15 COMPACT COMPONENT SYSTEM
    XX024 +15 COMPACT COMPONENT DOLBY
    XX025 +40 DVD PLAYER
    XX026 +10 TREADMILL/0-10MPH
    XX027 +20 TV 1.6″
    XX028 +10 CAMERA
    XX029 +15 RACK SYSTEM
    XX030 +20 4HD VCR
  • A total fraud score is computed from the fraud multiplier and the SKU points computed for the transaction (Block [0046] 324). The total fraud score is computed by multiplying the SKU points by the fraud multiplier. Alternatively, other mathematical computations may be used. The fraud score computation balances the SKU points vs. the other transaction parameters and is statistically designed to minimize the overall error rate for the fraud detection process. Since points can be subtracted from the fraud multiplier for positive transaction characteristics, the fraud score can be a negative number indicating that fraud is extremely unlikely.
  • The total fraud score is compared with a fraud score threshold [0047] 328 (Block 326). The fraud score threshold indicates the degree of potentially fraudulent behavior that indicates that the transaction 100 is likely fraudulent. Each participating vendor 106, 316, 318 may provide a fraud score threshold. An exemplary fraud score threshold is 50 points. Alternatively, multiple fraud score thresholds or a fraud score range are provided indicating a degree or range of fraudulently indicative behavior. In addition, the fraud score threshold may be a dynamic value, automatically adjusted based on the values of one or more of the transaction parameters 116 or SKU's of the commodities 112 involved. The dynamic fraud score threshold compensates for those parameters which do or do not indicate a substantial likelihood of fraud and may unbalance the computation.
  • If the fraud score is less than or equal (or alternatively just less than) to the fraud score threshold (Block [0048] 326), the consumer information provider 224 returns an indication back to the vendor 106 that the transaction is likely non-fraudulent (Block 338). If the fraud score is above the threshold, the consumer information provider 224 returns an indication that the transaction 100 is likely fraudulent. The returned indication may be a Boolean flag (i.e. likely fraudulent or not) or a confidence score related to the likelihood of the transaction 100 being fraudulent (e.g., “0”, meaning fraud unlikely, to “9”, meaning fraud extremely likely)
  • Alternatively, subsequent processing ensures that non-fraudulent transactions [0049] 100 are not mistakenly indicated as fraudulent. The transaction 100 is further checked against other consumer information databases 332 (Block 330). These other consumer information databases 332 are typically fee based and include proprietary databases of the consumer information provider 224 as well as third party public and non-public information sources. Access charges to these databases 332 are usually based on the nature of the database 332 and the nature of the authentication query or lookup being performed. For example, there may be a fixed fee charged for each address verification query. These databases 332 are alternate databases to the databases 308, 310, 312 used in the fraud multiplier computation (Block 306). Matches among the transaction parameters 116 and these alternative databases 332 can be used to diminish the likelihood that the current transaction 100 is fraudulent. It is preferred that these databases 332 be used to supplement the fraud determination process to minimize the external costs in accessing fee based data. In addition, some vendors 106 may prefer to supplement their fraud determination with more or fewer checks against the databases 332, depending on the amount of access charges they are comfortable incurring versus the added benefit to the determination of fraud.
  • If it is determined that the check of the alternate databases [0050] 332 has made it unlikely that the current transaction is fraudulent (Block 334), a non-fraudulent indicator is returned to the vendor 106 for further processing as described above (Block 338). Otherwise an indicator indicating that the current transaction 100 is likely fraudulent is returned to the vendor 106 (Block 338). In one embodiment, the customer address, card holder address and ship-to address are all verified against databases 332. If all three match, the fraud score is overridden and the transaction is approved. Alternatively, a less stringent match is required or other parameters are verified in place of or in addition to the addresses. In yet another alternative embodiment, the authentication results from the databases 332 are, themselves, assigned point values. Utilizing these point values, the fraud score is recomputed and re-compared against the fraud score threshold. Alternatively, the authentication results can be used to weight the point values of one or more of the transaction parameters 116 in the fraud multiplier computation and the fraud score is recomputed.
  • As the fraud detection system [0051] 108 operates, there may be errors (i.e. false positive and false negative responses from the system which are later determined by the vendor 106 or consumer information provider 224). In one embodiment, as these errors occur, the mis-flagged transactions 100 may be further reviewed or analyzed, either manually or automatically, by fraud investigators, to determine why the system failed. Where it is determined that a particular transaction parameter 116 or SKU is not accounted for or has an unbalanced effect on the fraud determination process, the databases 308, 310, 312, 314 are adjusted to correct the error. Such adjustments include altering point values, defining new or refining existing inter-parameter relationships or defining additional transaction parameters to be considered.
  • Further, the disclosed fraud detection system is capable of detecting fraud generally and not just specific instances of fraud such as a bad check or a stolen credit card. For example, the disclosed fraud detection system detects fraudulent use of coupons or discounts, fraudulent use of insurance for medical, dental or automobile related vendors [0052] 106 or fraudulent use of prescriptions for pharmaceutical related vendors 106. All of the characteristics of the transaction are balanced so that factors which are more likely to indicate fraud have a larger impact on the determination but other factors and relationships among factors are still significantly considered.
  • In addition, while the above processes, comparisons and computations are disclosed as being performed in a particular order, in alternative embodiments the performance order may be different and all orderings are contemplated. For example, in one embodiment, the SKU points are computed prior to the computation of the fraud multiplier. [0053]
  • The disclosed processes, comparisons and computations are preferably implemented in software as computer programs written in the RPG language. The software and databases are preferably executed on an AS/400 computer system manufactured by IBM Corporation, located in Armonk, N.Y. The computer systems are preferably executing the OS/400 4.0 or higher version operating system provided by IBM. The databases are preferably implemented using the AS/400's Integrated File System. Alternatively, the software and databases are implemented on a mainframe computer system complying with the IBM 390 architecture. In still another embodiment, the software and databases are implemented on an RS/6000 computer system manufactured by IBM Corporation utilizing the UNIX operating system. In still another alternative embodiment, the software and databases are implemented using the Structured Query Language and executed on a computer system having a processor equivalent to a Pentium III or better, manufactured by Intel Corporation, located in Santa Clara, Calif. and utilizing the Microsoft Windows NT 4.0 Operating System and Microsoft SQL server manufactured by Microsoft Corporation located in Redmond, Wash. In further embodiments, one or more of the component parts of the fraud detection system are implemented directly in hardware. It will be appreciated that the implementation details will vary depending on the hardware and software environments of the participating vendors [0054] 106, 316, 318 and the consumer information provider 224.
  • In the first embodiment, most of the fraud processing is external to the vendor [0055] 106 and can therefore be implemented in the single computing environment of the consumer information provider. This contains development of the fraud detection system to a single computing platform and single programming language, etc. easing development and maintenance complexity.
  • Second Embodiment [0056]
  • FIGS. 3A and 3B show a second embodiment of the fraud detection system [0057] 108. This embodiment differs from the first embodiment only in the division of processing between the vendor 106 and the consumer information provider 446 which may or may not be external to the vendor 106, as described above.
  • Referring to FIG. 3A, most of the processing remains internal to the vendor [0058] 106, including the computation of the fraud multiplier (Block 416), computation of the SKU points (Block 426), and computation of the total fraud score (Block 430) as well as the associated databases 418, 420, 422, 424, 428, 434 In this embodiment, however, the negative information databases 418, 422 only contain negative information collected by or imparted to this particular vendor 106. The secondary checking that occurs when the fraud score has exceeded the fraud score threshold is performed by the consumer information provider 446 (Blocks 432, 436). Typically, this secondary checking involves access to fee-based databases (as described above). In one embodiment, the decision to perform secondary checking by the consumer information provider 446 is manually determined after a manual review of the transaction 100. Alternatively, the secondary checking can be automatically performed for all transactions 100 whose fraud score exceeds the fraud score threshold or the decision to perform secondary checking can be based on one or more transaction parameters 116 and resultant point values. In yet another alternative embodiment, secondary checking with the consumer information provider 446 is done for specific transaction parameters 116 such as the customer, cardholder and ship-to addresses with subsequent secondary checking of other transaction parameters determined at the discretion of a manual reviewer.
  • Referring to FIG. 3B, the consumer information provider [0059] 446 checks the transaction 100 against negative databases 508, 510 which contain negative transaction information from other participating vendors 106, 512, 514 or sources (Block 506). From this information, it is determined whether or not there is a history of related fraud (Block 518). Further secondary checking is performed as described above against other proprietary, other public and non-public databases to further confirm the fraudulent status of the transaction 100 (Blocks 520, 524, 526). The fraud history, if any, and the results of the secondary checking are returned to the vendor 106 (Block 528).
  • Referring back to FIG. 3A, the fraud detection system determines the likelihood of fraud based on the total fraud score and the data returned by the consumer information provider [0060] 446. In one embodiment, the fraud score and the data returned by the consumer information provider 446 are manually reviewed and reconciled to determine the likelihood of fraud (Block 440). Alternatively, the data returned by the consumer information provider 446 and the fraud score are automatically reconciled to determine the fraudulent status. For example, the data returned by the consumer information provider is used to add or subtract points from the fraud multiplier and the fraud score is recomputed and re-compared with the fraud threshold. If it is determined that the transaction 100 is non-fraudulent, the transaction is accepted (Block 442). If it is determined that the transaction is fraudulent, further action can be taken as described above (Block 444).
  • In addition to the advantages noted above for the first embodiment, the second embodiment offers easy data management and lower bandwidth requirements between the vendor [0061] 106 and the consumer information provider 446. The main databases 418, 420, 422, 424, 428, 434 reside internally to the vendor 106 making updates and adjustments simpler and faster. Further, the amount of data needed to pass to the consumer information provider 446 is reduced since the consumer information provider 446 performs less of the overall fraud detection process. Further, the second embodiment provides a high reliability system which is not subject to communications problems between the vendor 106 and the consumer information provider 446. In the event of a communications failure, the system still detects fraudulent transactions 100. Finally, an internal system for detecting fraud operates and responds faster than a system which relies more heavily on external processing.
  • It is therefore intended that the foregoing detailed description be regarded as illustrative rather than limiting, and that it be understood that it is the following claims, including all equivalents, that are intended to define the spirit and scope of this invention. [0062]

Claims (55)

We claim:
1. A method for detecting fraudulent transactions between a customer and a vendor comprising:
(a) receiving a plurality of transaction parameters from said vendor, said plurality of transaction parameters representing at least one transaction of at least one commodity between said customer and said vendor;
(b) computing a first score as a function of said at least one commodity;
(c) computing a second score as a function of a first one or more of said plurality of transaction parameters;
(d) computing a fraud score based on said first and second scores; and
(e) indicating to said vendor that said at least one transaction is fraudulent if said fraud score exceeds a first pre-determined threshold.
2. The method of claim 1 further comprising:
(f) comparing a second one or more of said plurality of transaction parameters to a positive database; and
(g) indicating that said at least one transaction is non-fraudulent if a match results, said positive database comprising past non-fraudulent transactions known to said vendor.
3. The method of claim 2, wherein a subset of (a)-(g) are performed by said vendor and the remaining are performed by an entity external to said vendor.
4. The method of claim 2, further comprising:
(h) adding one or more of said plurality of transaction parameters to said positive database if said comparing fails to match and said fraud score does not exceed said first pre-determined threshold.
5. The method of claim 1 further comprising:
(f) comparing a second one or more of said plurality of transaction parameters to a negative database; and
(g) indicating that said at least one transaction is fraudulent if a match results, said negative database comprising known past fraudulent transactions.
6. The method of claim 5, wherein a subset of (a)-(g) are performed by said vendor and the remaining are performed by an entity external to said vendor.
7. The method of claim 5, further comprising:
(h) adding one or more of said plurality of transaction parameters to said negative database if said fraud score exceeds said first pre-determined threshold.
8. The method of claim 1 further comprising:
(f) computing a total value of all of said at least one commodity; and
(g) indicating that said at least one transaction is non-fraudulent if said total value is less than a second predetermined threshold.
9. The method of claim 8, wherein said second pre-determined threshold is approximately $100.
10. The method of claim 8, wherein (g) further comprises dynamically adjusting said second pre-determined threshold.
11. The method of claim 8, wherein a subset of (a)-(g) are performed by said vendor and the remaining are performed by an entity external to said vendor.
12. The method of claim 8, wherein (c) further comprises adjusting said second score as a function of said total value.
13. The method of claim 8, wherein (e) further comprises adjusting said first pre-determined threshold as a function of said total value.
14. The method of claim 1 wherein a subset of (a)-(e) are performed by said vendor and the remaining are performed by an entity external to said vendor.
15. The method of claim 1, wherein said at least one commodity is characterized by a likelihood of fraud and further wherein said first score is a function of said likelihood.
16. The method of claim 1, wherein said plurality of transaction parameters are received after said at least one transaction has been completed.
17. The method of claim 1, wherein (a)-(e) are completed while said at least one transaction is pending.
18. The method of claim 1, wherein (b) further comprises referencing a database, said database including a plurality of commodities and corresponding scores.
19. The method of claim 1, wherein (c) further comprises a summation function.
20. The method of claim 1, wherein (d) further comprises referencing a database, said database comprising data corresponding to a plurality of statistical data, a plurality of consumer data and a plurality of historical transaction data, and further wherein said second score is computed as a function of a pre-determined point value for each of said plurality of transaction parameters and whether said first one or more of said plurality of transaction parameters is authenticated by said data.
21. The method of claim 20, wherein said pre-determined point value is less than zero.
22. The method of claim 20, further comprising:
(f) adjusting said second score based on said pre-determined point values of a subset of said first one or more of said plurality of transaction parameters.
23. The method of claim 1, wherein (e) further comprises adjusting said first pre-determined threshold as a function of said second score.
24. The method of claim 1, wherein (d) further comprises a summation function.
25. The method of claim 1, wherein (e) further comprises multiplying said first score by said second score.
26. The method of claim 1, wherein said vendor is a catalog based merchant.
27. The method of claim 1, wherein said vendor is an electronic commerce based merchant.
28. The method of claim 1, wherein said vendor is a store based retail merchant.
29. A fraud detection processor comprising:
a receiver operative to receive a plurality of transaction parameters from a vendor, said plurality of transaction parameters representing at least one transaction of one or more commodities between a customer and said vendor;
a first score processor coupled with said receiver and operative to compute a first score as a function of each of said one or more commodities;
a second score processor coupled with said receiver and operative to compute a second score as a function of each of a first one or more of said plurality of transaction parameters;
a fraud score processor coupled with said first and second score processors and operative to compute a fraud score as a function of said first and second scores;
fraud determination logic coupled with said fraud score processor and operative to compute a determination of whether said at least one transaction is fraudulent as a function of a comparison of said fraud score and a first pre-determined threshold and further operative to indicate said determination to said vendor.
30. The fraud detection processor of claim 29 further comprising:
a positive database comprising past non-fraudulent transactions known to said vendor; and
a parameter processor coupled with said receiver and said fraud determination logic and operative to compare a second one or more of said plurality of transaction parameters to said positive database and indicate a result of said comparison to said fraud determination logic;
wherein said fraud determination logic is further operative to determine that said at least one transaction is not fraudulent when said parameter processor indicates a match result.
31. The fraud detection processor of claim 30, wherein said parameter processor is further operative to add one or more said plurality of transaction parameters to said positive database if said comparison fails to match and said fraud determination logic determines that said at least one transaction is non-fraudulent.
32. The fraud detection processor of claim 29 further comprising:
a negative database comprising data related to known past fraudulent transactions; and
a parameter processor coupled with said receiver and said fraud determination logic and operative to compare a second one or more of said plurality of transaction parameters to said negative database and indicate a result of said comparison to said fraud determination logic;
wherein said fraud determination logic is further operative to determine that said at least one transaction is fraudulent when said parameter processor indicates a match result.
33. The fraud detection processor of claim 32, wherein said parameter processor is further operative to add one or more of said plurality of transaction parameters to said negative database if said comparison fails to match and said fraud determination logic determines that said at least one transaction is fraudulent.
34. The fraud detection processor of claim 29, further comprising
a valuation processor coupled with said receiver and fraud determination logic and operative to compute a total value of all of said at least one commodity and indicate to said fraud determination logic if said total value is less than a second pre-determined threshold;
wherein said fraud determination logic is further operative to determine that said at least one transaction is non-fraudulent when said valuation processor indicates that said total value is less than said second pre-determined threshold.
35. The fraud detection processor of claim 34, wherein said second pre-determined threshold is approximately $100.
36. The fraud detection processor of claim 34, wherein said second pre-determined threshold is dynamic.
37. The fraud detection processor of claim 34, wherein said valuation processor is further coupled with said second score processor and further wherein said second score processor is further operative to adjust said second score based upon said total value.
38. The fraud detection processor of claim 34, wherein said fraud determination logic is further operative to adjust said first pre-determined threshold as a function of said total value.
39. The fraud detection processor of claim 29, wherein said at least one commodity is characterized by a probability of fraud and further wherein said first score is as a function of said probability.
40. The fraud detection processor of claim 29, wherein said plurality of transaction parameters are received after said at least one transaction has been completed.
41. The fraud detection processor of claim 29, wherein said fraud determination logic is further operative to make-said determination while said at least one transaction is pending.
42. The fraud detection processor of claim 29, wherein said first score processor further comprises a commodity reference database, said commodity reference database including a plurality of commodities and corresponding scores wherein said scores are a function of the probability of fraudulent activity related to a corresponding commodity.
43. The fraud detection processor of claim 29, wherein said second score processor further comprises a transactional reference database, said transactional reference database comprising data corresponding to a plurality of statistical data, a plurality of consumer data and a plurality of historical transaction data, and further wherein said second score processor is further operative to compute said second score as a function of a pre-determined point value for each of said plurality of transaction parameters and whether said first one or more of said plurality of transaction parameters is authenticated by said data in said transactional reference database.
44. The fraud detection processor of claim 43, wherein said pre-determined point value is less than zero.
45. The fraud detection processor of claim 43, wherein said second score processor is further operative to adjust said second score as a function of said pre-determined point values of a sub-set of said plurality of transaction parameters.
46. The fraud detection processor of claim 29, wherein said fraud determination logic is further operative to adjust said first pre-determined threshold based on said second score.
47. The fraud detection processor of claim 29, wherein said vendor is a catalog based merchant.
48. The fraud detection processor of claim 29, wherein said vendor is an electronic commerce based merchant.
49. The fraud detection processor of claim 29, wherein said vendor is a store based retail merchant.
50. A computer implemented system for detecting fraudulent transactions between a customer and a vendor comprising:
a positive database comprising data representing past non-fraudulent transactions known to said vendor;
a negative database comprising data representing past known fraudulent transactions;
one or more transaction databases comprising consumer and geographic transaction information;
a commodity database comprising data representing valuation and propensity to incite fraudulent activity for a plurality of commodities;
transaction logic operative to receive a transaction of a commodity from a vendor and decompose said transaction into a plurality of transaction parameters;
result logic operative to send an indication to said vendor whether said transaction is fraudulent and non-fraudulent;
valuation logic coupled with said transaction logic, said result logic and said commodity database and operative to compute the total value of said transaction and cause said result logic to indicate that said transaction is non-fraudulent if said total value is less than a first pre-determined threshold;
comparison logic coupled with said transaction logic, said result logic and said positive database and operative to compare said transaction with said positive database and cause said result logic to indicate that said transaction is non-fraudulent if said transaction matches in said positive database; and
fraud logic coupled with said transaction logic, said result logic and said negative database, said one or more transaction databases and said commodity database and operative to compute a first score as a function of said past fraudulent history and said propensity to incite fraudulent activity of said commodity, a second score as a function of the authenticity of said plurality of transaction parameters and a fraud score as a function of said first and second scores, said fraud logic being further operative to cause said result logic to indicate that said transaction is fraudulent if said fraud score exceeds a second pre-determined threshold.
51. The system of claim 50, wherein one or more of said positive database, said negative database, said one or more transaction databases, said commodity database, said transaction logic, said result logic, said valuation logic, said comparison logic and said fraud logic operates on a computer system internal to said vendor and the remaining operates on a computer system external to said vendor, said internal and external computer systems in communication with each other.
52. The system of claim 50, wherein said fraud logic is further coupled with said valuation logic and further wherein said second score is dynamically adjusted as a function of said total value.
53. The system of claim 50, wherein said fraud logic is further coupled with said valuation logic and further wherein said second predetermined threshold is dynamically adjusted as a function of said total value.
54. The system of claim 50, wherein said system is operative to process said transaction in real time.
55. The system of claim 50, wherein said system is operative to batch process said transaction.
US10/298,814 2000-03-24 2002-11-18 System and method for detecting fraudulent transactions Abandoned US20030097330A1 (en)

Priority Applications (2)

Application Number Priority Date Filing Date Title
US53529800A true 2000-03-24 2000-03-24
US10/298,814 US20030097330A1 (en) 2000-03-24 2002-11-18 System and method for detecting fraudulent transactions

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
US10/298,814 US20030097330A1 (en) 2000-03-24 2002-11-18 System and method for detecting fraudulent transactions

Related Parent Applications (1)

Application Number Title Priority Date Filing Date
US53529800A Division 2000-03-24 2000-03-24

Publications (1)

Publication Number Publication Date
US20030097330A1 true US20030097330A1 (en) 2003-05-22

Family

ID=24133602

Family Applications (2)

Application Number Title Priority Date Filing Date
US10/298,814 Abandoned US20030097330A1 (en) 2000-03-24 2002-11-18 System and method for detecting fraudulent transactions
US10/298,625 Active US6714918B2 (en) 2000-03-24 2002-11-18 System and method for detecting fraudulent transactions

Family Applications After (1)

Application Number Title Priority Date Filing Date
US10/298,625 Active US6714918B2 (en) 2000-03-24 2002-11-18 System and method for detecting fraudulent transactions

Country Status (7)

Country Link
US (2) US20030097330A1 (en)
EP (1) EP1279125A4 (en)
JP (1) JP2003529160A (en)
KR (1) KR20020086695A (en)
CN (1) CN1439139A (en)
AU (2) AU4927601A (en)
WO (1) WO2001073652A1 (en)

Cited By (148)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20030055714A1 (en) * 2001-09-20 2003-03-20 Bobby Thompson System and method for monitoring irregular sales activity
WO2004025540A2 (en) * 2002-09-13 2004-03-25 United States Postal Services Method for detecting suspicious transactions
US20040210519A1 (en) * 2002-05-31 2004-10-21 Carole Oppenlander System and method for authorizing transactions
US20050276401A1 (en) * 2003-11-05 2005-12-15 Madill Robert P Jr Systems and methods for assessing the potential for fraud in business transactions
US20060041464A1 (en) * 2004-08-19 2006-02-23 Transunion Llc. System and method for developing an analytic fraud model
US20060121968A1 (en) * 2003-02-11 2006-06-08 Waterleaf Limited Collusion detection
US20060189381A1 (en) * 2002-12-04 2006-08-24 Daniel David A Collusion detection and control
US20070233603A1 (en) * 2006-03-30 2007-10-04 Schmidgall Matthew M Flexible routing of electronic-based transactions
US20070232398A1 (en) * 2006-03-31 2007-10-04 Aikin Jeffrey C System and method for detecting collusion in online gaming via conditional behavior
WO2007044763A3 (en) * 2005-10-11 2007-11-22 Rsa Security Inc System and method for detecting fraudulent transactions
US20080103966A1 (en) * 2006-10-31 2008-05-01 Chuck Foster System and/or method for dynamic determination of transaction processing fees
US20080114885A1 (en) * 2006-11-14 2008-05-15 Fmr Corp. Detecting Fraudulent Activity on a Network
US20080114888A1 (en) * 2006-11-14 2008-05-15 Fmr Corp. Subscribing to Data Feeds on a Network
US20080114684A1 (en) * 2006-10-31 2008-05-15 Chuck Foster Termination of transactions
US20080114886A1 (en) * 2006-11-14 2008-05-15 Fmr Corp. Detecting and Interdicting Fraudulent Activity on a Network
US20080147448A1 (en) * 2006-12-19 2008-06-19 Hartford Fire Insurance Company System and method for predicting and responding to likelihood of volatility
US20080154651A1 (en) * 2006-12-22 2008-06-26 Hartford Fire Insurance Company System and method for utilizing interrelated computerized predictive models
US20080270206A1 (en) * 2003-09-13 2008-10-30 United States Postal Service Method for detecting suspicious transactions
US20090019434A1 (en) * 2007-07-10 2009-01-15 Oracle International Corporation Installed base data hub
US20090043615A1 (en) * 2007-08-07 2009-02-12 Hartford Fire Insurance Company Systems and methods for predictive data analysis
US20090112759A1 (en) * 2007-10-30 2009-04-30 Chuck Foster Accumulated transactions
US20090192957A1 (en) * 2006-03-24 2009-07-30 Revathi Subramanian Computer-Implemented Data Storage Systems And Methods For Use With Predictive Model Systems
US20090198610A1 (en) * 2008-01-31 2009-08-06 Mingyang Wu Credit Risk Prediction And Bank Card Customer Management By Integrating Disparate Data Sources
US20090222308A1 (en) * 2008-03-03 2009-09-03 Zoldi Scott M Detecting first party fraud abuse
US7630924B1 (en) * 2005-04-20 2009-12-08 Authorize.Net Llc Transaction velocity counting for fraud detection
US20100179891A1 (en) * 2009-01-12 2010-07-15 Visa U.S.A. Inc. Non-financial transactions in a financial transaction network
US7815106B1 (en) * 2005-12-29 2010-10-19 Verizon Corporate Services Group Inc. Multidimensional transaction fraud detection system and method
US20100274720A1 (en) * 2009-04-28 2010-10-28 Mark Carlson Fraud and reputation protection using advanced authorization and rules engine
US20100287099A1 (en) * 2009-05-07 2010-11-11 Frederick Liu Risk assessment rule set application for fraud prevention
US20110022518A1 (en) * 2009-07-22 2011-01-27 Ayman Hammad Apparatus including data bearing medium for seasoning a device using data obtained from multiple transaction environments
US20110022483A1 (en) * 2009-07-22 2011-01-27 Ayman Hammad Apparatus including data bearing medium for reducing fraud in payment transactions using a black list
US20110022517A1 (en) * 2009-07-22 2011-01-27 Ayman Hammad Apparatus including data bearing medium for authorizing a payment transaction using seasoned data
US20110066505A1 (en) * 2009-08-28 2011-03-17 Ayman Hammad Secure Alert System and Method
US20110066493A1 (en) * 2009-09-11 2011-03-17 Faith Patrick L System and Method Using Predicted Consumer Behavior to Reduce Use of Transaction Risk Analysis and Transaction Denials
US20110087606A1 (en) * 2009-10-07 2011-04-14 Hammond Mark S Systems and methods for processing merchandise returns
US20110125672A1 (en) * 2004-06-08 2011-05-26 Rosenthal Collins Group, L.L.C. Method and system for providing electronic information for risk assesement and management via dynamic total net worth for multi-market electronic trading
US20110184986A1 (en) * 2002-12-31 2011-07-28 American Express Travel Related Service Company, Inc. Method and system for implementing and managing an enterprise identity management for distributed security in a computer system
US20110184766A1 (en) * 2010-01-25 2011-07-28 Hartford Fire Insurance Company Systems and methods for prospecting and rounding business insurance customers
US20110184845A1 (en) * 2002-12-31 2011-07-28 American Express Travel Related Services Company, Inc. Method and system for implementing and managing an enterprise identity management for distributed security in a computer system
US8015133B1 (en) 2007-02-20 2011-09-06 Sas Institute Inc. Computer-implemented modeling systems and methods for analyzing and predicting computer network intrusions
US20110238564A1 (en) * 2010-03-26 2011-09-29 Kwang Hyun Lim System and Method for Early Detection of Fraudulent Transactions
US8060437B2 (en) 2006-10-31 2011-11-15 International Funding Partners Llc Automatic termination of electronic transactions
US8190512B1 (en) 2007-02-20 2012-05-29 Sas Institute Inc. Computer-implemented clustering systems and methods for action determination
CN102546602A (en) * 2011-12-21 2012-07-04 中国科学技术大学苏州研究院 Network transaction method based on privacy protection trust evaluation
US20120317013A1 (en) * 2011-06-13 2012-12-13 Ho Ming Luk Computer-Implemented Systems And Methods For Scoring Stored Enterprise Data
US8346691B1 (en) 2007-02-20 2013-01-01 Sas Institute Inc. Computer-implemented semi-supervised learning systems and methods
US20130024361A1 (en) * 2011-07-21 2013-01-24 Bank Of America Corporation Capacity customization for fraud filtering
US20130024358A1 (en) * 2011-07-21 2013-01-24 Bank Of America Corporation Filtering transactions to prevent false positive fraud alerts
US20130036037A1 (en) * 2010-02-25 2013-02-07 At&T Mobility Ii Llc Fraud analysis for a location aware transaction
US20130124296A1 (en) * 2005-04-18 2013-05-16 The Retail Equation, Inc. Systems and methods for determining whether to offer a reward at a point of return
US8494557B2 (en) 2010-02-25 2013-07-23 At&T Mobility Ii Llc Timed fingerprint locating in wireless networks
US8498931B2 (en) 2006-01-10 2013-07-30 Sas Institute Inc. Computer-implemented risk evaluation systems and methods
US8509806B2 (en) 2010-12-14 2013-08-13 At&T Intellectual Property I, L.P. Classifying the position of a wireless device
US20130211872A1 (en) * 2011-08-13 2013-08-15 William Jay Cherry Assessing Risk Associated with a Vendor
US8515862B2 (en) 2008-05-29 2013-08-20 Sas Institute Inc. Computer-implemented systems and methods for integrated model validation for compliance and credit risk
US8612410B2 (en) 2011-06-30 2013-12-17 At&T Mobility Ii Llc Dynamic content selection through timed fingerprint location data
US8620350B2 (en) 2010-02-25 2013-12-31 At&T Mobility Ii Llc Timed fingerprint locating for idle-state user equipment in wireless networks
US8666390B2 (en) 2011-08-29 2014-03-04 At&T Mobility Ii Llc Ticketing mobile call failures based on geolocated event data
US8762048B2 (en) 2011-10-28 2014-06-24 At&T Mobility Ii Llc Automatic travel time and routing determinations in a wireless network
US8761799B2 (en) 2011-07-21 2014-06-24 At&T Mobility Ii Llc Location analytics employing timed fingerprint location information
US8768866B2 (en) 2011-10-21 2014-07-01 Sas Institute Inc. Computer-implemented systems and methods for forecasting and estimation using grid regression
US8788407B1 (en) 2013-03-15 2014-07-22 Palantir Technologies Inc. Malware data clustering
US8799122B1 (en) * 2008-03-31 2014-08-05 Intuit Inc. Method and system for user contributed aggregated fraud identification
US8805737B1 (en) * 2009-11-02 2014-08-12 Sas Institute Inc. Computer-implemented multiple entity dynamic summarization systems and methods
US8855999B1 (en) 2013-03-15 2014-10-07 Palantir Technologies Inc. Method and system for generating a parser and parsing complex data
US8892112B2 (en) 2011-07-21 2014-11-18 At&T Mobility Ii Llc Selection of a radio access bearer resource based on radio access bearer resource historical information
US8892054B2 (en) 2012-07-17 2014-11-18 At&T Mobility Ii Llc Facilitation of delay error correction in timing-based location systems
US8897805B2 (en) 2012-06-15 2014-11-25 At&T Intellectual Property I, L.P. Geographic redundancy determination for time based location information in a wireless radio network
US8897802B2 (en) 2011-07-21 2014-11-25 At&T Mobility Ii Llc Selection of a radio access technology resource based on radio access technology resource historical information
US8909247B2 (en) 2011-11-08 2014-12-09 At&T Mobility Ii Llc Location based sharing of a network access credential
US8925104B2 (en) 2012-04-13 2014-12-30 At&T Mobility Ii Llc Event driven permissive sharing of information
US8923134B2 (en) 2011-08-29 2014-12-30 At&T Mobility Ii Llc Prioritizing network failure tickets using mobile location data
US8929914B2 (en) 2009-01-23 2015-01-06 At&T Mobility Ii Llc Compensation of propagation delays of wireless signals
US8929827B2 (en) 2012-06-04 2015-01-06 At&T Mobility Ii Llc Adaptive calibration of measurements for a wireless radio network
US8930897B2 (en) 2013-03-15 2015-01-06 Palantir Technologies Inc. Data integration tool
US8938258B2 (en) 2012-06-14 2015-01-20 At&T Mobility Ii Llc Reference based location information for a wireless network
US8970432B2 (en) 2011-11-28 2015-03-03 At&T Mobility Ii Llc Femtocell calibration for timing based locating systems
EP2840542A3 (en) * 2013-08-19 2015-03-25 Compass Plus (GB) Limited Method and system for detection of fraudulent transactions
US8996031B2 (en) 2010-08-27 2015-03-31 At&T Mobility Ii Llc Location estimation of a mobile device in a UMTS network
US9008684B2 (en) 2010-02-25 2015-04-14 At&T Mobility Ii Llc Sharing timed fingerprint location information
US9009629B2 (en) 2010-12-01 2015-04-14 At&T Mobility Ii Llc Motion-based user interface feature subsets
US9009827B1 (en) 2014-02-20 2015-04-14 Palantir Technologies Inc. Security sharing system
US9021260B1 (en) 2014-07-03 2015-04-28 Palantir Technologies Inc. Malware data item analysis
US9026133B2 (en) 2011-11-28 2015-05-05 At&T Mobility Ii Llc Handset agent calibration for timing based locating systems
US9043894B1 (en) 2014-11-06 2015-05-26 Palantir Technologies Inc. Malicious software detection in a computing system
US9046592B2 (en) 2012-06-13 2015-06-02 At&T Mobility Ii Llc Timed fingerprint locating at user equipment
US9094929B2 (en) 2012-06-12 2015-07-28 At&T Mobility Ii Llc Event tagging for mobile networks
US9171306B1 (en) 2010-03-29 2015-10-27 Bank Of America Corporation Risk-based transaction authentication
US9196157B2 (en) 2010-02-25 2015-11-24 AT&T Mobolity II LLC Transportation analytics employing timed fingerprint location information
US9202249B1 (en) 2014-07-03 2015-12-01 Palantir Technologies Inc. Data item clustering and analysis
US9230280B1 (en) 2013-03-15 2016-01-05 Palantir Technologies Inc. Clustering data based on indications of financial malfeasance
US9231979B2 (en) 2013-03-14 2016-01-05 Sas Institute Inc. Rule optimization for classification and detection
US9311672B2 (en) * 2012-08-09 2016-04-12 American Express Travel Related Services Company, Inc. Systems and methods for fraud detection using a cooperative data exchange
US9326263B2 (en) 2012-06-13 2016-04-26 At&T Mobility Ii Llc Site location determination using crowd sourced propagation delay and location data
US9351223B2 (en) 2012-07-25 2016-05-24 At&T Mobility Ii Llc Assignment of hierarchical cell structures employing geolocation techniques
US9351111B1 (en) 2015-03-06 2016-05-24 At&T Mobility Ii Llc Access to mobile location related information
US9367872B1 (en) 2014-12-22 2016-06-14 Palantir Technologies Inc. Systems and user interfaces for dynamic and interactive investigation of bad actor behavior based on automatic clustering of related data in various data structures
US9408174B2 (en) 2012-06-19 2016-08-02 At&T Mobility Ii Llc Facilitation of timed fingerprint mobile device locating
US9454785B1 (en) 2015-07-30 2016-09-27 Palantir Technologies Inc. Systems and user interfaces for holistic, data-driven investigation of bad actor behavior based on clustering and scoring of related data
US9462497B2 (en) 2011-07-01 2016-10-04 At&T Mobility Ii Llc Subscriber data analysis and graphical rendering
US9519043B2 (en) 2011-07-21 2016-12-13 At&T Mobility Ii Llc Estimating network based locating error in wireless networks
US9535974B1 (en) 2014-06-30 2017-01-03 Palantir Technologies Inc. Systems and methods for identifying key phrase clusters within documents
US9552615B2 (en) 2013-12-20 2017-01-24 Palantir Technologies Inc. Automated database analysis to detect malfeasance
US9594907B2 (en) 2013-03-14 2017-03-14 Sas Institute Inc. Unauthorized activity detection and classification
US9635046B2 (en) 2015-08-06 2017-04-25 Palantir Technologies Inc. Systems, methods, user interfaces, and computer-readable media for investigating potential malicious communications
US20170163817A1 (en) * 2012-07-24 2017-06-08 Twilio, Inc. Method and system for preventing illicit use of a telephony platform
US9720987B2 (en) 2011-06-14 2017-08-01 Iii Holdings 1, Llc Systems and methods for cooperative data exchange
US9774687B2 (en) 2014-07-07 2017-09-26 Twilio, Inc. System and method for managing media and signaling in a communication platform
US9785773B2 (en) 2014-07-03 2017-10-10 Palantir Technologies Inc. Malware data item analysis
US9805399B2 (en) 2015-02-03 2017-10-31 Twilio, Inc. System and method for a media intelligence platform
US9807244B2 (en) 2008-10-01 2017-10-31 Twilio, Inc. Telephony web event system and method
US9811398B2 (en) 2013-09-17 2017-11-07 Twilio, Inc. System and method for tagging and tracking events of an application platform
US9817563B1 (en) 2014-12-29 2017-11-14 Palantir Technologies Inc. System and method of generating data points from one or more data stores of data items for chart creation and manipulation
US9853872B2 (en) 2013-09-17 2017-12-26 Twilio, Inc. System and method for providing communication platform metadata
US9858279B2 (en) 2014-07-07 2018-01-02 Twilio, Inc. Method and system for applying data retention policies in a computing platform
US9875293B2 (en) 2014-07-03 2018-01-23 Palanter Technologies Inc. System and method for news events detection and visualization
US9882942B2 (en) 2011-02-04 2018-01-30 Twilio, Inc. Method for processing telephony sessions of a network
US9894212B2 (en) 2009-03-02 2018-02-13 Twilio, Inc. Method and system for a multitenancy telephone network
US9898528B2 (en) 2014-12-22 2018-02-20 Palantir Technologies Inc. Concept indexing among database of documents using machine learning techniques
US9898509B2 (en) 2015-08-28 2018-02-20 Palantir Technologies Inc. Malicious activity detection system capable of efficiently processing data accessed from databases and generating alerts for display in interactive user interfaces
US9906651B2 (en) 2008-04-02 2018-02-27 Twilio, Inc. System and method for processing media requests during telephony sessions
US9906571B2 (en) 2008-04-02 2018-02-27 Twilio, Inc. System and method for processing telephony sessions
US9906607B2 (en) 2014-10-21 2018-02-27 Twilio, Inc. System and method for providing a micro-services communication platform
US9907010B2 (en) 2014-04-17 2018-02-27 Twilio, Inc. System and method for enabling multi-modal communication
US9916582B2 (en) 2011-07-28 2018-03-13 Iii Holdings 1, Llc Systems and methods for generating and using a digital pass
US9942394B2 (en) 2011-09-21 2018-04-10 Twilio, Inc. System and method for determining and communicating presence information
US9948703B2 (en) 2015-05-14 2018-04-17 Twilio, Inc. System and method for signaling through data storage
US9967224B2 (en) 2010-06-25 2018-05-08 Twilio, Inc. System and method for enabling real-time eventing
US9965937B2 (en) 2013-03-15 2018-05-08 Palantir Technologies Inc. External malware data item clustering and analysis
US9992608B2 (en) 2013-06-19 2018-06-05 Twilio, Inc. System and method for providing a communication endpoint information service
US9996839B2 (en) 2005-04-18 2018-06-12 The Retail Equation, Inc. Systems and methods for data collection and providing coupons at a point of return
US10003693B2 (en) 2014-03-14 2018-06-19 Twilio, Inc. System and method for a work distribution service
US10033617B2 (en) 2012-10-15 2018-07-24 Twilio, Inc. System and method for triggering on platform usage
US10051011B2 (en) 2013-03-14 2018-08-14 Twilio, Inc. System and method for integrating session initiation protocol communication in a telecommunications platform
US10057734B2 (en) 2013-06-19 2018-08-21 Twilio Inc. System and method for transmitting and receiving media messages
US10063713B2 (en) 2016-05-23 2018-08-28 Twilio Inc. System and method for programmatic device connectivity
US10063461B2 (en) 2013-11-12 2018-08-28 Twilio, Inc. System and method for client communication in a distributed telephony network
US10069773B2 (en) 2013-11-12 2018-09-04 Twilio, Inc. System and method for enabling dynamic multi-modal communication
US10103953B1 (en) 2015-05-12 2018-10-16 Palantir Technologies Inc. Methods and systems for analyzing entity performance
US10116733B2 (en) 2014-07-07 2018-10-30 Twilio, Inc. System and method for collecting feedback in a multi-tenant communication platform
US10120857B2 (en) 2013-03-15 2018-11-06 Palantir Technologies Inc. Method and system for generating a parser and parsing complex data
US10122763B2 (en) 2011-05-23 2018-11-06 Twilio, Inc. System and method for connecting a communication to a client
US10165015B2 (en) 2011-05-23 2018-12-25 Twilio Inc. System and method for real-time communication by using a client application communication protocol
US10162887B2 (en) 2014-06-30 2018-12-25 Palantir Technologies Inc. Systems and methods for key phrase characterization of documents
US10200458B2 (en) 2012-05-09 2019-02-05 Twilio, Inc. System and method for managing media in a distributed communication network
US10230746B2 (en) 2014-01-03 2019-03-12 Palantir Technologies Inc. System and method for evaluating network threats and usage
US10235461B2 (en) 2017-05-02 2019-03-19 Palantir Technologies Inc. Automated assistance for generating relevant and valuable search results for an entity of interest
US10275778B1 (en) 2015-12-30 2019-04-30 Palantir Technologies Inc. Systems and user interfaces for dynamic and interactive investigation based on automatic malfeasance clustering of related data in various data structures

Families Citing this family (170)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7403922B1 (en) * 1997-07-28 2008-07-22 Cybersource Corporation Method and apparatus for evaluating fraud risk in an electronic commerce transaction
US7096192B1 (en) * 1997-07-28 2006-08-22 Cybersource Corporation Method and system for detecting fraud in a credit card transaction over a computer network
US8706630B2 (en) 1999-08-19 2014-04-22 E2Interactive, Inc. System and method for securely authorizing and distributing stored-value card data
CA2330266C (en) * 2000-01-05 2006-04-11 Procure.Com Inc. Secure electronic procurement system and method
US8046288B1 (en) * 2000-06-13 2011-10-25 Paymentech, Llc System and method for payment data processing
US8209246B2 (en) 2001-03-20 2012-06-26 Goldman, Sachs & Co. Proprietary risk management clearinghouse
US20030233319A1 (en) * 2001-03-20 2003-12-18 David Lawrence Electronic fund transfer participant risk management clearing
US20110131136A1 (en) * 2001-03-20 2011-06-02 David Lawrence Risk Management Customer Registry
US7899722B1 (en) * 2001-03-20 2011-03-01 Goldman Sachs & Co. Correspondent bank registry
US20040193532A1 (en) * 2001-03-20 2004-09-30 David Lawrence Insider trading risk management
US20040006532A1 (en) * 2001-03-20 2004-01-08 David Lawrence Network access risk management
US7958027B2 (en) * 2001-03-20 2011-06-07 Goldman, Sachs & Co. Systems and methods for managing risk associated with a geo-political area
US20030225687A1 (en) * 2001-03-20 2003-12-04 David Lawrence Travel related risk management clearinghouse
US8140415B2 (en) * 2001-03-20 2012-03-20 Goldman Sachs & Co. Automated global risk management
US8121937B2 (en) 2001-03-20 2012-02-21 Goldman Sachs & Co. Gaming industry risk management clearinghouse
US20020143562A1 (en) * 2001-04-02 2002-10-03 David Lawrence Automated legal action risk management
US7865427B2 (en) * 2001-05-30 2011-01-04 Cybersource Corporation Method and apparatus for evaluating fraud risk in an electronic commerce transaction
WO2003032219A1 (en) * 2001-10-05 2003-04-17 Cyber Area Research, Inc. Settlement authentication server system using ai authentication
WO2003098401A2 (en) * 2002-05-16 2003-11-27 Ndchealth Corporation Systems and methods for verifying and editing electronically transmitted claim content
US7716068B2 (en) 2002-09-25 2010-05-11 Mckesson Financial Holdings Limited Systems and methods for look-alike sound-alike medication error messaging
US20040138975A1 (en) * 2002-12-17 2004-07-15 Lisa Engel System and method for preventing fraud in check orders
US20040148256A1 (en) * 2003-01-23 2004-07-29 International Business Machines Corporation Fraud detection within an electronic payment system
US20040230448A1 (en) * 2003-02-14 2004-11-18 William Schaich System for managing and reporting financial account activity
US20040215579A1 (en) * 2003-04-24 2004-10-28 George Redenbaugh Supplemental address verification
US20050038701A1 (en) * 2003-08-13 2005-02-17 Alan Matthew Computer system for card in connection with, but not to carry out, a transaction
US7740168B2 (en) 2003-08-18 2010-06-22 Visa U.S.A. Inc. Method and system for generating a dynamic verification value
US7761374B2 (en) * 2003-08-18 2010-07-20 Visa International Service Association Method and system for generating a dynamic verification value
WO2005033830A2 (en) * 2003-10-03 2005-04-14 Npx Technologies Ltd. Methods and systems for determining the reliability of transactions
US20050075836A1 (en) * 2003-10-03 2005-04-07 Jason Arthur Taylor Forensic person tracking method and apparatus
US8055584B2 (en) * 2003-10-20 2011-11-08 First Data Corporation Systems and methods for fraud management in relation to stored value cards
US20050097051A1 (en) * 2003-11-05 2005-05-05 Madill Robert P.Jr. Fraud potential indicator graphical interface
US8655309B2 (en) 2003-11-14 2014-02-18 E2Interactive, Inc. Systems and methods for electronic device point-of-sale activation
US20050108178A1 (en) * 2003-11-17 2005-05-19 Richard York Order risk determination
US20050144100A1 (en) * 2003-12-30 2005-06-30 Craig Shapiro Payment systems and methods for earning incentives using at least two financial instruments
US20050216397A1 (en) * 2004-03-26 2005-09-29 Clearcommerce, Inc. Method, system, and computer program product for processing a financial transaction request
US8510300B2 (en) 2004-07-02 2013-08-13 Goldman, Sachs & Co. Systems and methods for managing information associated with legal, compliance and regulatory risk
US8996481B2 (en) * 2004-07-02 2015-03-31 Goldman, Sach & Co. Method, system, apparatus, program code and means for identifying and extracting information
US8762191B2 (en) * 2004-07-02 2014-06-24 Goldman, Sachs & Co. Systems, methods, apparatus, and schema for storing, managing and retrieving information
US8442953B2 (en) 2004-07-02 2013-05-14 Goldman, Sachs & Co. Method, system, apparatus, program code and means for determining a redundancy of information
US7574382B1 (en) * 2004-08-03 2009-08-11 Amazon Technologies, Inc. Automated detection of anomalous user activity associated with specific items in an electronic catalog
US7185805B1 (en) 2004-08-10 2007-03-06 Transmodus, Inc. Wireless check authorization
US7543740B2 (en) * 2004-09-17 2009-06-09 Digital Envoy, Inc. Fraud analyst smart cookie
US20060064374A1 (en) * 2004-09-17 2006-03-23 David Helsper Fraud risk advisor
US7497374B2 (en) * 2004-09-17 2009-03-03 Digital Envoy, Inc. Fraud risk advisor
US7797199B2 (en) * 2004-10-15 2010-09-14 Rearden Commerce, Inc. Fraudulent address database
US20060217996A1 (en) * 2005-03-23 2006-09-28 E2Interactive, Inc. D/B/A E2Interactive, Inc. Point-of-sale activation of media device account
US7739143B1 (en) 2005-03-24 2010-06-15 Amazon Technologies, Inc. Robust forecasting techniques with reduced sensitivity to anomalous data
US7610214B1 (en) 2005-03-24 2009-10-27 Amazon Technologies, Inc. Robust forecasting techniques with reduced sensitivity to anomalous data
US20060271457A1 (en) * 2005-05-26 2006-11-30 Romain Martin R Identity theft monitoring and prevention
US20060271456A1 (en) * 2005-05-26 2006-11-30 Romain Martin R Debit-based identity theft monitoring and prevention
US8321283B2 (en) * 2005-05-27 2012-11-27 Per-Se Technologies Systems and methods for alerting pharmacies of formulary alternatives
US20080021801A1 (en) * 2005-05-31 2008-01-24 Yuh-Shen Song Dynamic multidimensional risk-weighted suspicious activities detector
US7543741B2 (en) * 2005-06-13 2009-06-09 Robert Lovett System, method and program product for credit card transaction validation
US7347361B2 (en) * 2005-06-13 2008-03-25 Robert Lovett System, method and program product for account transaction validation
JP2009501979A (en) 2005-07-15 2009-01-22 レボリューション マネー,インコーポレイテッド System and method for setting the terms for defining a child account
US20070016521A1 (en) * 2005-07-15 2007-01-18 Zhiping Wang Data processing system for a billing address-based credit watch
US20070043577A1 (en) * 2005-08-16 2007-02-22 Sheldon Kasower Apparatus and method of enabling a victim of identity theft to resolve and prevent fraud
US9065643B2 (en) 2006-04-05 2015-06-23 Visa U.S.A. Inc. System and method for account identifier obfuscation
JP2007115141A (en) * 2005-10-21 2007-05-10 Kyotofu Menshingu Shoko Kyodo Kumiai Card payment authentication server and card payment authentication method
US20070162303A1 (en) * 2005-12-08 2007-07-12 Ndchealth Corporation Systems and Methods for Shifting Prescription Market Share by Presenting Pricing Differentials for Therapeutic Alternatives
US20090012896A1 (en) * 2005-12-16 2009-01-08 Arnold James B Systems and methods for automated vendor risk analysis
EP1816595A1 (en) * 2006-02-06 2007-08-08 MediaKey Ltd. A method and a system for identifying potentially fraudulent customers in relation to network based commerce activities, in particular involving payment, and a computer program for performing said method
US10127554B2 (en) * 2006-02-15 2018-11-13 Citibank, N.A. Fraud early warning system and method
US20070204033A1 (en) * 2006-02-24 2007-08-30 James Bookbinder Methods and systems to detect abuse of network services
WO2007139786A2 (en) * 2006-05-22 2007-12-06 American Express Travel Related Services Company, Inc. Kiosk and method for vending stored value cards
US9195985B2 (en) 2006-06-08 2015-11-24 Iii Holdings 1, Llc Method, system, and computer program product for customer-level data verification
US7818264B2 (en) * 2006-06-19 2010-10-19 Visa U.S.A. Inc. Track data encryption
JP4921875B2 (en) * 2006-07-13 2012-04-25 ヤフー株式会社 Network transaction fraud detection method
US7783564B2 (en) * 2006-07-25 2010-08-24 Visa U.S.A. Inc. Compliance control in a card based program
US7698220B2 (en) 2006-09-14 2010-04-13 E2Interactive, Inc. Virtual terminal for payment processing
US8036979B1 (en) 2006-10-05 2011-10-11 Experian Information Solutions, Inc. System and method for generating a finance attribute from tradeline data
US20080091445A1 (en) * 2006-10-16 2008-04-17 Matthew Mihic Method and system for dynamic social networking based on similar travel itineraries
WO2008059465A2 (en) * 2006-11-16 2008-05-22 Net 1 Ueps Technologies, Inc. Secure financial transactions
US20080126261A1 (en) * 2006-11-25 2008-05-29 Robert Lovett Cashless vending system and method
US7657569B1 (en) 2006-11-28 2010-02-02 Lower My Bills, Inc. System and method of removing duplicate leads
US7778885B1 (en) 2006-12-04 2010-08-17 Lower My Bills, Inc. System and method of enhancing leads
US8290838B1 (en) * 2006-12-29 2012-10-16 Amazon Technologies, Inc. Indicating irregularities in online financial transactions
US20080162202A1 (en) * 2006-12-29 2008-07-03 Richendra Khanna Detecting inappropriate activity by analysis of user interactions
US8788419B2 (en) 2006-12-30 2014-07-22 First Data Corporation Method and system for mitigating risk of fraud in internet banking
US20080183618A1 (en) * 2007-01-26 2008-07-31 First Data Corporation Global government sanctions systems and methods
US8108266B2 (en) * 2007-01-29 2012-01-31 Hewlett-Packard Development Company, L.P. Methods for providing secure eCommerce transactions
US8606666B1 (en) 2007-01-31 2013-12-10 Experian Information Solutions, Inc. System and method for providing an aggregation tool
US8606626B1 (en) 2007-01-31 2013-12-10 Experian Information Solutions, Inc. Systems and methods for providing a direct marketing campaign planning environment
US20080191007A1 (en) * 2007-02-13 2008-08-14 First Data Corporation Methods and Systems for Identifying Fraudulent Transactions Across Multiple Accounts
US7975299B1 (en) 2007-04-05 2011-07-05 Consumerinfo.Com, Inc. Child identity monitor
US8191053B2 (en) * 2007-04-12 2012-05-29 Ingenix, Inc. Computerized data warehousing
US20080262961A1 (en) * 2007-04-17 2008-10-23 First Data Corporation Merchant Credit Risk Monitoring
US20090044279A1 (en) * 2007-05-11 2009-02-12 Fair Isaac Corporation Systems and methods for fraud detection via interactive link analysis
WO2008141168A1 (en) * 2007-05-11 2008-11-20 Fair Isaac Corporation Systems and methods for fraud detection via interactive link analysis
CN101743560A (en) * 2007-05-18 2010-06-16 国际签证服务协会 Method and system for analyzing financial data to determine the cost of fraud
US20080288405A1 (en) * 2007-05-20 2008-11-20 Michael Sasha John Systems and Methods for Automatic and Transparent Client Authentication and Online Transaction Verification
WO2008147918A2 (en) 2007-05-25 2008-12-04 Experian Information Solutions, Inc. System and method for automated detection of never-pay data sets
US8165938B2 (en) * 2007-06-04 2012-04-24 Visa U.S.A. Inc. Prepaid card fraud and risk management
US7739169B2 (en) * 2007-06-25 2010-06-15 Visa U.S.A. Inc. Restricting access to compromised account information
US8121942B2 (en) 2007-06-25 2012-02-21 Visa U.S.A. Inc. Systems and methods for secure and transparent cardless transactions
WO2009010950A1 (en) * 2007-07-18 2009-01-22 Seq.U.R. Ltd System and method for predicting a measure of anomalousness and similarity of records in relation to a set of reference records
US8600872B1 (en) 2007-07-27 2013-12-03 Wells Fargo Bank, N.A. System and method for detecting account compromises
US8275110B2 (en) 2007-09-28 2012-09-25 Ringcentral, Inc. Active call filtering, screening and dispatching
US8666841B1 (en) 2007-10-09 2014-03-04 Convergys Information Management Group, Inc. Fraud detection engine and method of using the same
US20090125369A1 (en) * 2007-10-26 2009-05-14 Crowe Horwath Llp System and method for analyzing and dispositioning money laundering suspicious activity alerts
US7440915B1 (en) 2007-11-16 2008-10-21 U.S. Bancorp Licensing, Inc. Method, system, and computer-readable medium for reducing payee fraud
US20090164376A1 (en) * 2007-12-20 2009-06-25 Mckesson Financial Holdings Limited Systems and Methods for Controlled Substance Prescription Monitoring Via Real Time Claims Network
US7653577B2 (en) * 2008-02-19 2010-01-26 The Go Daddy Group, Inc. Validating e-commerce transactions
US8141780B2 (en) 2008-02-23 2012-03-27 Cedar Ridge Research Llc System and method for data card emulation
US7707089B1 (en) * 2008-03-12 2010-04-27 Jpmorgan Chase, N.A. Method and system for automating fraud authorization strategies
US20090248465A1 (en) * 2008-03-28 2009-10-01 Fortent Americas Inc. Assessment of risk associated with doing business with a party
US8635083B1 (en) 2008-04-02 2014-01-21 Mckesson Financial Holdings Systems and methods for facilitating the establishment of pharmaceutical rebate agreements
US9378527B2 (en) * 2008-04-08 2016-06-28 Hartford Fire Insurance Company Computer system for applying predictive model to determine and indeterminate data
US8626525B2 (en) * 2008-06-23 2014-01-07 Mckesson Financial Holdings Systems and methods for real-time monitoring and analysis of prescription claim rejections
US8538777B1 (en) 2008-06-30 2013-09-17 Mckesson Financial Holdings Limited Systems and methods for providing patient medication history
US20090326977A1 (en) * 2008-06-30 2009-12-31 Mckesson Financial Holding Limited Systems and Methods for Providing Drug Samples to Patients
US7991689B1 (en) 2008-07-23 2011-08-02 Experian Information Solutions, Inc. Systems and methods for detecting bust out fraud using credit data
US7925559B2 (en) * 2008-08-22 2011-04-12 Hartford Fire Insurance Company Computer system for applying proactive referral model to long term disability claims
CA2742963A1 (en) 2008-11-06 2010-05-14 Visa International Service Association Online challenge-response
US8588744B2 (en) 2008-11-26 2013-11-19 Ringcentral, Inc. Fraud prevention techniques
US8838082B2 (en) 2008-11-26 2014-09-16 Ringcentral, Inc. Centralized status server for call management of location-aware mobile devices
US20100174638A1 (en) * 2009-01-06 2010-07-08 ConsumerInfo.com Report existence monitoring
US8046242B1 (en) 2009-01-22 2011-10-25 Mckesson Financial Holdings Limited Systems and methods for verifying prescription dosages
US20100235908A1 (en) * 2009-03-13 2010-09-16 Silver Tail Systems System and Method for Detection of a Change in Behavior in the Use of a Website Through Vector Analysis
US20100235909A1 (en) * 2009-03-13 2010-09-16 Silver Tail Systems System and Method for Detection of a Change in Behavior in the Use of a Website Through Vector Velocity Analysis
US8600873B2 (en) * 2009-05-28 2013-12-03 Visa International Service Association Managed real-time transaction fraud analysis and decisioning
US8489415B1 (en) 2009-09-30 2013-07-16 Mckesson Financial Holdings Limited Systems and methods for the coordination of benefits in healthcare claim transactions
US20110137740A1 (en) 2009-12-04 2011-06-09 Ashmit Bhattacharya Processing value-ascertainable items
US20110153441A1 (en) * 2009-12-23 2011-06-23 Merrill Brooks Smith Systems and Methods for Authorizing Use of Validly Sold Merchandise
US8788296B1 (en) 2010-01-29 2014-07-22 Mckesson Financial Holdings Systems and methods for providing notifications of availability of generic drugs or products
US10089683B2 (en) * 2010-02-08 2018-10-02 Visa International Service Association Fraud reduction system for transactions
US8386276B1 (en) 2010-02-11 2013-02-26 Mckesson Financial Holdings Limited Systems and methods for determining prescribing physician activity levels
US8321243B1 (en) 2010-02-15 2012-11-27 Mckesson Financial Holdings Limited Systems and methods for the intelligent coordination of benefits in healthcare transactions
US8626663B2 (en) * 2010-03-23 2014-01-07 Visa International Service Association Merchant fraud risk score
US9652802B1 (en) 2010-03-24 2017-05-16 Consumerinfo.Com, Inc. Indirect monitoring and reporting of a user's credit data
US8548824B1 (en) 2010-03-26 2013-10-01 Mckesson Financial Holdings Limited Systems and methods for notifying of duplicate product prescriptions
US8688468B1 (en) 2010-03-30 2014-04-01 Mckesson Financial Holdings Systems and methods for verifying dosages associated with healthcare transactions
US8725613B1 (en) 2010-04-27 2014-05-13 Experian Information Solutions, Inc. Systems and methods for early account score and notification
US8473415B2 (en) 2010-05-04 2013-06-25 Kevin Paul Siegel System and method for identifying a point of compromise in a payment transaction processing system
WO2012054868A2 (en) * 2010-10-21 2012-04-26 Visa International Service Association Software and methods for risk and fraud mitigation
US20120158586A1 (en) * 2010-12-16 2012-06-21 Verizon Patent And Licensing, Inc. Aggregating transaction information to detect fraud
US20120158540A1 (en) * 2010-12-16 2012-06-21 Verizon Patent And Licensing, Inc. Flagging suspect transactions based on selective application and analysis of rules
CA2830797A1 (en) * 2011-03-23 2012-09-27 Detica Patent Limited An automated fraud detection method and system
US9449288B2 (en) 2011-05-20 2016-09-20 Deem, Inc. Travel services search
US8655773B1 (en) * 2012-01-26 2014-02-18 Intuit Inc. Geo-location based underwriting
ITRM20120159A1 (en) * 2012-04-16 2013-10-17 Luca Bellati Apparatus for monitoring payment transactions and related procedure.
US9635067B2 (en) 2012-04-23 2017-04-25 Verint Americas Inc. Tracing and asynchronous communication network and routing method
US20130282844A1 (en) 2012-04-23 2013-10-24 Contact Solutions LLC Apparatus and methods for multi-mode asynchronous communication
WO2017024248A1 (en) 2015-08-06 2017-02-09 Contact Solutions LLC Tracing and asynchronous communication network and routing method
US20130282562A1 (en) * 2012-04-23 2013-10-24 Erika Gallo Systems and methods for preventing fraudulent purchases
US20130291099A1 (en) * 2012-04-25 2013-10-31 Verizon Patent And Licensing, Inc. Notification services with anomaly detection
US8762266B2 (en) * 2012-05-08 2014-06-24 Vantiv, Llc Systems and methods for performing funds freeze and/or funds seizure with respect to prepaid payment cards
US8725532B1 (en) 2012-06-29 2014-05-13 Mckesson Financial Holdings Systems and methods for monitoring controlled substance distribution
US8856923B1 (en) * 2012-06-29 2014-10-07 Emc Corporation Similarity-based fraud detection in adaptive authentication systems
US9576262B2 (en) 2012-12-05 2017-02-21 Microsoft Technology Licensing, Llc Self learning adaptive modeling system
US10255598B1 (en) 2012-12-06 2019-04-09 Consumerinfo.Com, Inc. Credit card account data extraction
US20140244528A1 (en) * 2013-02-22 2014-08-28 Palo Alto Research Center Incorporated Method and apparatus for combining multi-dimensional fraud measurements for anomaly detection
US9311351B2 (en) 2013-03-11 2016-04-12 Sas Institute Inc. Techniques to block records for matching
US9633322B1 (en) 2013-03-15 2017-04-25 Consumerinfo.Com, Inc. Adjustment of knowledge-based authentication
US9094389B2 (en) * 2013-09-04 2015-07-28 Facebook, Inc. Systems and methods for authenticating nodes
US8966074B1 (en) * 2013-09-13 2015-02-24 Network Kinetix, LLC System and method for real-time analysis of network traffic
US20150088613A1 (en) * 2013-09-23 2015-03-26 Comdata Network, Inc. Systems, methods, and computer program products for managing fuel costs
JP5853125B2 (en) * 2013-11-13 2016-02-09 楽天株式会社 The information processing apparatus, a method of controlling an information processing apparatus, and program
US20150142628A1 (en) * 2013-11-20 2015-05-21 Bank Of America Corporation Detecting structured transactions
US9892402B2 (en) * 2013-12-18 2018-02-13 Verizon Patent And Licensing Inc. Financial authorization of an online transaction based on a location and an identifier of a user device
US9218410B2 (en) 2014-02-06 2015-12-22 Contact Solutions LLC Systems, apparatuses and methods for communication flow modification
US10262362B1 (en) 2014-02-14 2019-04-16 Experian Information Solutions, Inc. Automatic generation of code for attributes
US20160012544A1 (en) * 2014-05-28 2016-01-14 Sridevi Ramaswamy Insurance claim validation and anomaly detection based on modus operandi analysis
KR101673526B1 (en) * 2014-08-13 2016-11-17 (주)베인스 Methods and system of managing point using a receipt
US9396472B2 (en) * 2014-10-31 2016-07-19 Facebook, Inc. Systems and methods for dynamically identifying illegitimate accounts based on rules
US9858575B2 (en) 2014-12-16 2018-01-02 At&T Mobility Ii Llc Fraud detection via mobile device location tracking
US10242019B1 (en) 2014-12-19 2019-03-26 Experian Information Solutions, Inc. User behavior segmentation using latent topic detection
US10204374B1 (en) * 2015-06-15 2019-02-12 Amazon Technologies, Inc. Parallel fraud check
KR20180059203A (en) * 2016-11-25 2018-06-04 공주대학교 산학협력단 Method and program for predicting chargeback fraud user
US10078839B1 (en) * 2017-08-30 2018-09-18 Square, Inc. Centralized system for data retrieval

Family Cites Families (36)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
FR418144A (en) 1910-07-11 1910-12-01 Eugene Marie Alexandre Henri G ink tank applicable to the feathers of all kinds
GB421808A (en) 1933-04-01 1934-01-01 Richard Oldham Improvements in fuel injection pumps
DE891068C (en) 1951-08-12 1953-09-24 Atlas Werke Ag Device for measuring the distance of reflecting objects by the acoustic echo method
JPH0637177B2 (en) 1985-09-27 1994-05-18 カヤバ工業株式会社 Rear wheel neutral apparatus of the front and rear wheel steering vehicle
JPS63184870A (en) 1987-01-28 1988-07-30 Omron Tateisi Electronics Co Transaction processing device
US5025372A (en) 1987-09-17 1991-06-18 Meridian Enterprises, Inc. System and method for administration of incentive award program through use of credit
FR2625343B1 (en) 1987-12-29 1990-05-04 Michaud Andre Improvements in signal processing devices
EP0468229A3 (en) 1990-07-27 1994-01-26 Hnc Inc A neural network with expert system functionality
JP3012297B2 (en) 1990-09-03 2000-02-21 株式会社日立製作所 Abnormal event identification method and apparatus
JPH04220758A (en) 1990-12-20 1992-08-11 Fujitsu Ltd Time sequence data prediction and prediction recognition method
US5163086A (en) * 1990-12-31 1992-11-10 At&T Bell Laboratories Telephone network credit card calling apparatus and method of operation to determine validation and fraudulent use of credit cards in placing telephone calls
JP2901767B2 (en) * 1991-02-08 1999-06-07 株式会社東芝 Cryptographic communication system and a portable electronic device
US5416067A (en) 1992-07-10 1995-05-16 Pbi - Gordon Corporation Dry, water-soluble, substituted phenoxy and/or benzoic acid herbicides and method of preparing same
US5819226A (en) * 1992-09-08 1998-10-06 Hnc Software Inc. Fraud detection using predictive modeling
US5267314A (en) * 1992-11-17 1993-11-30 Leon Stambler Secure transaction system and method utilized therein
US5832068A (en) 1994-06-01 1998-11-03 Davox Corporation Data processing system with real time priority updating of data records and dynamic record exclusion
US5950121A (en) * 1993-06-29 1999-09-07 Airtouch Communications, Inc. Method and apparatus for fraud control in cellular telephone systems
US5627886A (en) * 1994-09-22 1997-05-06 Electronic Data Systems Corporation System and method for detecting fraudulent network usage patterns using real-time network monitoring
US5774525A (en) * 1995-01-23 1998-06-30 International Business Machines Corporation Method and apparatus utilizing dynamic questioning to provide secure access control
US5884289A (en) 1995-06-16 1999-03-16 Card Alert Services, Inc. Debit card fraud detection and control system
US6154146A (en) * 1995-11-02 2000-11-28 Siemens Aktiengesellschaft Method of rendering misuse of communication services more difficult
US5805686A (en) 1995-12-22 1998-09-08 Mci Corporation Telephone fraud detection system
US5745654A (en) 1996-02-13 1998-04-28 Hnc Software, Inc. Fast explanations of scored observations
US5790645A (en) 1996-08-01 1998-08-04 Nynex Science & Technology, Inc. Automatic design of fraud detection systems
US5895453A (en) 1996-08-27 1999-04-20 Sts Systems, Ltd. Method and system for the detection, management and prevention of losses in retail and other environments
JP2982702B2 (en) * 1996-08-30 1999-11-29 日本電気株式会社 Disk device
US6018723A (en) * 1997-05-27 2000-01-25 Visa International Service Association Method and apparatus for pattern generation
US6029154A (en) * 1997-07-28 2000-02-22 Internet Commerce Services Corporation Method and system for detecting fraud in a credit card transaction over the internet
US6163771A (en) * 1997-08-28 2000-12-19 Walker Digital, Llc Method and device for generating a single-use financial account number
US5936221A (en) * 1997-10-02 1999-08-10 Bridgepoint Systems, Inc. Smart card system and method for transferring value
US6095413A (en) * 1997-11-17 2000-08-01 Automated Transaction Corporation System and method for enhanced fraud detection in automated electronic credit card processing
EP1040456B1 (en) * 1997-12-19 2004-09-15 Visa International Service Association Card activation at point of distribution
JP4113274B2 (en) * 1998-02-05 2008-07-09 富士ゼロックス株式会社 Authentication device and method
US6163604A (en) * 1998-04-03 2000-12-19 Lucent Technologies Automated fraud management in transaction-based networks
US6464134B1 (en) * 1999-12-10 2002-10-15 Terri Page System and method for verifying the authenticity of a check and authorizing payment thereof
US6418436B1 (en) * 1999-12-20 2002-07-09 First Data Corporation Scoring methodology for purchasing card fraud detection

Cited By (251)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20030055714A1 (en) * 2001-09-20 2003-03-20 Bobby Thompson System and method for monitoring irregular sales activity
US20040210519A1 (en) * 2002-05-31 2004-10-21 Carole Oppenlander System and method for authorizing transactions
WO2004025540A2 (en) * 2002-09-13 2004-03-25 United States Postal Services Method for detecting suspicious transactions
WO2004025540A3 (en) * 2002-09-13 2004-05-13 Us Postal Services Method for detecting suspicious transactions
US20040117316A1 (en) * 2002-09-13 2004-06-17 Gillum Alben Joseph Method for detecting suspicious transactions
US20060189381A1 (en) * 2002-12-04 2006-08-24 Daniel David A Collusion detection and control
US7883412B2 (en) 2002-12-04 2011-02-08 Waterleaf Limited Collusion detection and control
US20110184861A1 (en) * 2002-12-31 2011-07-28 American Express Travel Related Services Company, Inc. Method and system for implementing and managing an enterprise identity management for distributed security in a computer system
US20110184845A1 (en) * 2002-12-31 2011-07-28 American Express Travel Related Services Company, Inc. Method and system for implementing and managing an enterprise identity management for distributed security in a computer system
US20110289569A1 (en) * 2002-12-31 2011-11-24 American Express Travel Related Services Company, Inc. Method and system for implementing and managing an enterprise identity management for distributed security
US20110184986A1 (en) * 2002-12-31 2011-07-28 American Express Travel Related Service Company, Inc. Method and system for implementing and managing an enterprise identity management for distributed security in a computer system
US20110202565A1 (en) * 2002-12-31 2011-08-18 American Express Travel Related Services Company, Inc. Method and system for implementing and managing an enterprise identity management for distributed security in a computer system
US20110184987A1 (en) * 2002-12-31 2011-07-28 American Express Travel Related Services Company, Inc. Method and system for implementing and managing an enterprise identity management for distributed security in a computer system
US20110184985A1 (en) * 2002-12-31 2011-07-28 American Express Travel Related Services Company, Inc. Method and system for implementing and managing an enterprise identity management for distributed security in a computer system
US20110184988A1 (en) * 2002-12-31 2011-07-28 American Express Travel Related Services Company, Method and system for implementing and managing an enterprise identity management for distributed security in a computer system
US20110184860A1 (en) * 2002-12-31 2011-07-28 American Express Travel Related Services Company, Inc. Method and system for implementing and managing an enterprise identity management for distributed security in a computer system
US20060121968A1 (en) * 2003-02-11 2006-06-08 Waterleaf Limited Collusion detection
US7699702B2 (en) * 2003-02-11 2010-04-20 Waterleaf Limited Collusion detection
US20080270206A1 (en) * 2003-09-13 2008-10-30 United States Postal Service Method for detecting suspicious transactions
US20050276401A1 (en) * 2003-11-05 2005-12-15 Madill Robert P Jr Systems and methods for assessing the potential for fraud in business transactions
US7827045B2 (en) * 2003-11-05 2010-11-02 Computer Sciences Corporation Systems and methods for assessing the potential for fraud in business transactions
US20110125672A1 (en) * 2004-06-08 2011-05-26 Rosenthal Collins Group, L.L.C. Method and system for providing electronic information for risk assesement and management via dynamic total net worth for multi-market electronic trading
US20060041464A1 (en) * 2004-08-19 2006-02-23 Transunion Llc. System and method for developing an analytic fraud model
US20140067504A1 (en) * 2005-04-18 2014-03-06 The Retail Equation, Inc. Systems and methods for determining whether to offer a reward at a point of return
US9996839B2 (en) 2005-04-18 2018-06-12 The Retail Equation, Inc. Systems and methods for data collection and providing coupons at a point of return
US9646319B2 (en) * 2005-04-18 2017-05-09 The Retail Equation, Inc. Systems and methods for determining whether to offer a reward at a point of return
US20130124296A1 (en) * 2005-04-18 2013-05-16 The Retail Equation, Inc. Systems and methods for determining whether to offer a reward at a point of return
US8583478B2 (en) * 2005-04-18 2013-11-12 The Retail Equation, Inc. Systems and methods for determining whether to offer a reward at a point of return
US8041620B2 (en) * 2005-04-20 2011-10-18 Authorize.Net Llc Transaction velocity counting for fraud detection
US7630924B1 (en) * 2005-04-20 2009-12-08 Authorize.Net Llc Transaction velocity counting for fraud detection
US20090327008A1 (en) * 2005-04-20 2009-12-31 Jim Collins Transaction Velocity Counting For Fraud Detection
US8165945B2 (en) * 2005-04-20 2012-04-24 Visa International Service Association Transaction velocity counting for fraud detection
US20110218909A1 (en) * 2005-04-20 2011-09-08 Authorize.Net Llc Transaction velocity counting for fraud detection
US20090182652A1 (en) * 2005-10-11 2009-07-16 Amit Klein System and method for detecting fraudulent transactions
US8311907B2 (en) 2005-10-11 2012-11-13 Emc Corporation System and method for detecting fraudulent transactions
WO2007044763A3 (en) * 2005-10-11 2007-11-22 Rsa Security Inc System and method for detecting fraudulent transactions
US7815106B1 (en) * 2005-12-29 2010-10-19 Verizon Corporate Services Group Inc. Multidimensional transaction fraud detection system and method
US8498931B2 (en) 2006-01-10 2013-07-30 Sas Institute Inc. Computer-implemented risk evaluation systems and methods
US20090192855A1 (en) * 2006-03-24 2009-07-30 Revathi Subramanian Computer-Implemented Data Storage Systems And Methods For Use With Predictive Model Systems
US20090192957A1 (en) * 2006-03-24 2009-07-30 Revathi Subramanian Computer-Implemented Data Storage Systems And Methods For Use With Predictive Model Systems
US7912773B1 (en) 2006-03-24 2011-03-22 Sas Institute Inc. Computer-implemented data storage systems and methods for use with predictive model systems
US20070233603A1 (en) * 2006-03-30 2007-10-04 Schmidgall Matthew M Flexible routing of electronic-based transactions
US20070232398A1 (en) * 2006-03-31 2007-10-04 Aikin Jeffrey C System and method for detecting collusion in online gaming via conditional behavior
US7604541B2 (en) * 2006-03-31 2009-10-20 Information Extraction Transport, Inc. System and method for detecting collusion in online gaming via conditional behavior
US20080114684A1 (en) * 2006-10-31 2008-05-15 Chuck Foster Termination of transactions
US8060437B2 (en) 2006-10-31 2011-11-15 International Funding Partners Llc Automatic termination of electronic transactions
US20080103966A1 (en) * 2006-10-31 2008-05-01 Chuck Foster System and/or method for dynamic determination of transaction processing fees
US20080114885A1 (en) * 2006-11-14 2008-05-15 Fmr Corp. Detecting Fraudulent Activity on a Network
US8145560B2 (en) * 2006-11-14 2012-03-27 Fmr Llc Detecting fraudulent activity on a network
US20080114888A1 (en) * 2006-11-14 2008-05-15 Fmr Corp. Subscribing to Data Feeds on a Network
US8180873B2 (en) 2006-11-14 2012-05-15 Fmr Llc Detecting fraudulent activity
US7856494B2 (en) * 2006-11-14 2010-12-21 Fmr Llc Detecting and interdicting fraudulent activity on a network
US20080114886A1 (en) * 2006-11-14 2008-05-15 Fmr Corp. Detecting and Interdicting Fraudulent Activity on a Network
US20080147448A1 (en) * 2006-12-19 2008-06-19 Hartford Fire Insurance Company System and method for predicting and responding to likelihood of volatility
US8798987B2 (en) 2006-12-19 2014-08-05 Hartford Fire Insurance Company System and method for processing data relating to insurance claim volatility
US8571900B2 (en) 2006-12-19 2013-10-29 Hartford Fire Insurance Company System and method for processing data relating to insurance claim stability indicator
US8359209B2 (en) * 2006-12-19 2013-01-22 Hartford Fire Insurance Company System and method for predicting and responding to likelihood of volatility
US20110218827A1 (en) * 2006-12-22 2011-09-08 Hartford Fire Insurance Company System and method for utilizing interrelated computerized predictive models
US20080154651A1 (en) * 2006-12-22 2008-06-26 Hartford Fire Insurance Company System and method for utilizing interrelated computerized predictive models
US7945497B2 (en) 2006-12-22 2011-05-17 Hartford Fire Insurance Company System and method for utilizing interrelated computerized predictive models
US9881340B2 (en) 2006-12-22 2018-01-30 Hartford Fire Insurance Company Feedback loop linked models for interface generation
US8346691B1 (en) 2007-02-20 2013-01-01 Sas Institute Inc. Computer-implemented semi-supervised learning systems and methods
US8190512B1 (en) 2007-02-20 2012-05-29 Sas Institute Inc. Computer-implemented clustering systems and methods for action determination
US8015133B1 (en) 2007-02-20 2011-09-06 Sas Institute Inc. Computer-implemented modeling systems and methods for analyzing and predicting computer network intrusions
US20090019434A1 (en) * 2007-07-10 2009-01-15 Oracle International Corporation Installed base data hub
US7747643B2 (en) * 2007-07-10 2010-06-29 Oracle International Corporation Installed base data hub
US20090043615A1 (en) * 2007-08-07 2009-02-12 Hartford Fire Insurance Company Systems and methods for predictive data analysis
US20090112759A1 (en) * 2007-10-30 2009-04-30 Chuck Foster Accumulated transactions
US20090198610A1 (en) * 2008-01-31 2009-08-06 Mingyang Wu Credit Risk Prediction And Bank Card Customer Management By Integrating Disparate Data Sources
US20090222308A1 (en) * 2008-03-03 2009-09-03 Zoldi Scott M Detecting first party fraud abuse
US8799122B1 (en) * 2008-03-31 2014-08-05 Intuit Inc. Method and system for user contributed aggregated fraud identification
US9906651B2 (en) 2008-04-02 2018-02-27 Twilio, Inc. System and method for processing media requests during telephony sessions
US9906571B2 (en) 2008-04-02 2018-02-27 Twilio, Inc. System and method for processing telephony sessions
US8515862B2 (en) 2008-05-29 2013-08-20 Sas Institute Inc. Computer-implemented systems and methods for integrated model validation for compliance and credit risk
US8521631B2 (en) 2008-05-29 2013-08-27 Sas Institute Inc. Computer-implemented systems and methods for loan evaluation using a credit assessment framework
US10187530B2 (en) 2008-10-01 2019-01-22 Twilio, Inc. Telephony web event system and method
US9807244B2 (en) 2008-10-01 2017-10-31 Twilio, Inc. Telephony web event system and method
US8762239B2 (en) 2009-01-12 2014-06-24 Visa U.S.A. Inc. Non-financial transactions in a financial transaction network
US20100179891A1 (en) * 2009-01-12 2010-07-15 Visa U.S.A. Inc. Non-financial transactions in a financial transaction network
US8929914B2 (en) 2009-01-23 2015-01-06 At&T Mobility Ii Llc Compensation of propagation delays of wireless signals
US9894212B2 (en) 2009-03-02 2018-02-13 Twilio, Inc. Method and system for a multitenancy telephone network
US20100274720A1 (en) * 2009-04-28 2010-10-28 Mark Carlson Fraud and reputation protection using advanced authorization and rules engine
US10140616B2 (en) 2009-05-07 2018-11-27 Visa International Service Association Risk assessment rule set application for fraud prevention
US20100287099A1 (en) * 2009-05-07 2010-11-11 Frederick Liu Risk assessment rule set application for fraud prevention
US8924279B2 (en) 2009-05-07 2014-12-30 Visa U.S.A. Inc. Risk assessment rule set application for fraud prevention
US20110022517A1 (en) * 2009-07-22 2011-01-27 Ayman Hammad Apparatus including data bearing medium for authorizing a payment transaction using seasoned data
US20110022518A1 (en) * 2009-07-22 2011-01-27 Ayman Hammad Apparatus including data bearing medium for seasoning a device using data obtained from multiple transaction environments
US9396465B2 (en) 2009-07-22 2016-07-19 Visa International Service Association Apparatus including data bearing medium for reducing fraud in payment transactions using a black list
US20110022483A1 (en) * 2009-07-22 2011-01-27 Ayman Hammad Apparatus including data bearing medium for reducing fraud in payment transactions using a black list
US20110066505A1 (en) * 2009-08-28 2011-03-17 Ayman Hammad Secure Alert System and Method
US20110066493A1 (en) * 2009-09-11 2011-03-17 Faith Patrick L System and Method Using Predicted Consumer Behavior to Reduce Use of Transaction Risk Analysis and Transaction Denials
US8620798B2 (en) 2009-09-11 2013-12-31 Visa International Service Association System and method using predicted consumer behavior to reduce use of transaction risk analysis and transaction denials
US20110087606A1 (en) * 2009-10-07 2011-04-14 Hammond Mark S Systems and methods for processing merchandise returns
US8805737B1 (en) * 2009-11-02 2014-08-12 Sas Institute Inc. Computer-implemented multiple entity dynamic summarization systems and methods
US8355934B2 (en) 2010-01-25 2013-01-15 Hartford Fire Insurance Company Systems and methods for prospecting business insurance customers
US20110184766A1 (en) * 2010-01-25 2011-07-28 Hartford Fire Insurance Company Systems and methods for prospecting and rounding business insurance customers
US8892452B2 (en) * 2010-01-25 2014-11-18 Hartford Fire Insurance Company Systems and methods for adjusting insurance workflow
US9196157B2 (en) 2010-02-25 2015-11-24 AT&T Mobolity II LLC Transportation analytics employing timed fingerprint location information
US8620350B2 (en) 2010-02-25 2013-12-31 At&T Mobility Ii Llc Timed fingerprint locating for idle-state user equipment in wireless networks
US8494557B2 (en) 2010-02-25 2013-07-23 At&T Mobility Ii Llc Timed fingerprint locating in wireless networks
US20130036037A1 (en) * 2010-02-25 2013-02-07 At&T Mobility Ii Llc Fraud analysis for a location aware transaction
US9008684B2 (en) 2010-02-25 2015-04-14 At&T Mobility Ii Llc Sharing timed fingerprint location information
US9053513B2 (en) * 2010-02-25 2015-06-09 At&T Mobility Ii Llc Fraud analysis for a location aware transaction
US8886219B2 (en) 2010-02-25 2014-11-11 At&T Mobility Ii Llc Timed fingerprint locating in wireless networks
US20110238564A1 (en) * 2010-03-26 2011-09-29 Kwang Hyun Lim System and Method for Early Detection of Fraudulent Transactions
US9171306B1 (en) 2010-03-29 2015-10-27 Bank Of America Corporation Risk-based transaction authentication
US9967224B2 (en) 2010-06-25 2018-05-08 Twilio, Inc. System and method for enabling real-time eventing
US8996031B2 (en) 2010-08-27 2015-03-31 At&T Mobility Ii Llc Location estimation of a mobile device in a UMTS network
US9813900B2 (en) 2010-12-01 2017-11-07 At&T Mobility Ii Llc Motion-based user interface feature subsets
US9009629B2 (en) 2010-12-01 2015-04-14 At&T Mobility Ii Llc Motion-based user interface feature subsets
US8509806B2 (en) 2010-12-14 2013-08-13 At&T Intellectual Property I, L.P. Classifying the position of a wireless device
US10230772B2 (en) 2011-02-04 2019-03-12 Twilio, Inc. Method for processing telephony sessions of a network
US9882942B2 (en) 2011-02-04 2018-01-30 Twilio, Inc. Method for processing telephony sessions of a network
US10165015B2 (en) 2011-05-23 2018-12-25 Twilio Inc. System and method for real-time communication by using a client application communication protocol
US10122763B2 (en) 2011-05-23 2018-11-06 Twilio, Inc. System and method for connecting a communication to a client
US20120317013A1 (en) * 2011-06-13 2012-12-13 Ho Ming Luk Computer-Implemented Systems And Methods For Scoring Stored Enterprise Data
US9720987B2 (en) 2011-06-14 2017-08-01 Iii Holdings 1, Llc Systems and methods for cooperative data exchange
US8612410B2 (en) 2011-06-30 2013-12-17 At&T Mobility Ii Llc Dynamic content selection through timed fingerprint location data
US9462497B2 (en) 2011-07-01 2016-10-04 At&T Mobility Ii Llc Subscriber data analysis and graphical rendering
US10091678B2 (en) 2011-07-01 2018-10-02 At&T Mobility Ii Llc Subscriber data analysis and graphical rendering
US8892112B2 (en) 2011-07-21 2014-11-18 At&T Mobility Ii Llc Selection of a radio access bearer resource based on radio access bearer resource historical information
US8761799B2 (en) 2011-07-21 2014-06-24 At&T Mobility Ii Llc Location analytics employing timed fingerprint location information
US9232525B2 (en) 2011-07-21 2016-01-05 At&T Mobility Ii Llc Selection of a radio access technology resource based on radio access technology resource historical information
US8571982B2 (en) * 2011-07-21 2013-10-29 Bank Of America Corporation Capacity customization for fraud filtering
US9008698B2 (en) 2011-07-21 2015-04-14 At&T Mobility Ii Llc Location analytics employing timed fingerprint location information
US20130024358A1 (en) * 2011-07-21 2013-01-24 Bank Of America Corporation Filtering transactions to prevent false positive fraud alerts
US8897802B2 (en) 2011-07-21 2014-11-25 At&T Mobility Ii Llc Selection of a radio access technology resource based on radio access technology resource historical information
US20130024361A1 (en) * 2011-07-21 2013-01-24 Bank Of America Corporation Capacity customization for fraud filtering
US9519043B2 (en) 2011-07-21 2016-12-13 At&T Mobility Ii Llc Estimating network based locating error in wireless networks
US9510355B2 (en) 2011-07-21 2016-11-29 At&T Mobility Ii Llc Selection of a radio access technology resource based on radio access technology resource historical information
US10085270B2 (en) 2011-07-21 2018-09-25 At&T Mobility Ii Llc Selection of a radio access technology resource based on radio access technology resource historical information
US9916582B2 (en) 2011-07-28 2018-03-13 Iii Holdings 1, Llc Systems and methods for generating and using a digital pass
US10229411B2 (en) * 2011-08-05 2019-03-12 At&T Mobility Ii Llc Fraud analysis for a location aware transaction
US20130211872A1 (en) * 2011-08-13 2013-08-15 William Jay Cherry Assessing Risk Associated with a Vendor
US8923134B2 (en) 2011-08-29 2014-12-30 At&T Mobility Ii Llc Prioritizing network failure tickets using mobile location data
US8666390B2 (en) 2011-08-29 2014-03-04 At&T Mobility Ii Llc Ticketing mobile call failures based on geolocated event data
US9942394B2 (en) 2011-09-21 2018-04-10 Twilio, Inc. System and method for determining and communicating presence information
US10212275B2 (en) 2011-09-21 2019-02-19 Twilio, Inc. System and method for determining and communicating presence information
US10182147B2 (en) 2011-09-21 2019-01-15 Twilio Inc. System and method for determining and communicating presence information
US8768866B2 (en) 2011-10-21 2014-07-01 Sas Institute Inc. Computer-implemented systems and methods for forecasting and estimation using grid regression
US9103690B2 (en) 2011-10-28 2015-08-11 At&T Mobility Ii Llc Automatic travel time and routing determinations in a wireless network
US10206113B2 (en) 2011-10-28 2019-02-12 At&T Mobility Ii Llc Sharing timed fingerprint location information
US9681300B2 (en) 2011-10-28 2017-06-13 At&T Mobility Ii Llc Sharing timed fingerprint location information
US9191821B2 (en) 2011-10-28 2015-11-17 At&T Mobility Ii Llc Sharing timed fingerprint location information
US8762048B2 (en) 2011-10-28 2014-06-24 At&T Mobility Ii Llc Automatic travel time and routing determinations in a wireless network
US9232399B2 (en) 2011-11-08 2016-01-05 At&T Intellectual Property I, L.P. Location based sharing of a network access credential
US8909247B2 (en) 2011-11-08 2014-12-09 At&T Mobility Ii Llc Location based sharing of a network access credential
US9667660B2 (en) 2011-11-08 2017-05-30 At&T Intellectual Property I, L.P. Location based sharing of a network access credential
US10084824B2 (en) 2011-11-08 2018-09-25 At&T Intellectual Property I, L.P. Location based sharing of a network access credential
US9810765B2 (en) 2011-11-28 2017-11-07 At&T Mobility Ii Llc Femtocell calibration for timing based locating systems
US8970432B2 (en) 2011-11-28 2015-03-03 At&T Mobility Ii Llc Femtocell calibration for timing based locating systems
US9026133B2 (en) 2011-11-28 2015-05-05 At&T Mobility Ii Llc Handset agent calibration for timing based locating systems
US9743369B2 (en) 2011-11-28 2017-08-22 At&T Mobility Ii Llc Handset agent calibration for timing based locating systems
CN102546602A (en) * 2011-12-21 2012-07-04 中国科学技术大学苏州研究院 Network transaction method based on privacy protection trust evaluation
US8925104B2 (en) 2012-04-13 2014-12-30 At&T Mobility Ii Llc Event driven permissive sharing of information
US9563784B2 (en) 2012-04-13 2017-02-07 At&T Mobility Ii Llc Event driven permissive sharing of information
US9864875B2 (en) 2012-04-13 2018-01-09 At&T Mobility Ii Llc Event driven permissive sharing of information
US10200458B2 (en) 2012-05-09 2019-02-05 Twilio, Inc. System and method for managing media in a distributed communication network
US8929827B2 (en) 2012-06-04 2015-01-06 At&T Mobility Ii Llc Adaptive calibration of measurements for a wireless radio network
US9094929B2 (en) 2012-06-12 2015-07-28 At&T Mobility Ii Llc Event tagging for mobile networks
US9955451B2 (en) 2012-06-12 2018-04-24 At&T Mobility Ii Llc Event tagging for mobile networks
US9596671B2 (en) 2012-06-12 2017-03-14 At&T Mobility Ii Llc Event tagging for mobile networks
US9521647B2 (en) 2012-06-13 2016-12-13 At&T Mobility Ii Llc Site location determination using crowd sourced propagation delay and location data
US9046592B2 (en) 2012-06-13 2015-06-02 At&T Mobility Ii Llc Timed fingerprint locating at user equipment
US9723446B2 (en) 2012-06-13 2017-08-01 At&T Mobility Ii Llc Site location determination using crowd sourced propagation delay and location data
US9326263B2 (en) 2012-06-13 2016-04-26 At&T Mobility Ii Llc Site location determination using crowd sourced propagation delay and location data
US8938258B2 (en) 2012-06-14 2015-01-20 At&T Mobility Ii Llc Reference based location information for a wireless network
US9769623B2 (en) 2012-06-14 2017-09-19 At&T Mobility Ii Llc Reference based location information for a wireless network
US9473897B2 (en) 2012-06-14 2016-10-18 At&T Mobility Ii Llc Reference based location information for a wireless network
US8897805B2 (en) 2012-06-15 2014-11-25 At&T Intellectual Property I, L.P. Geographic redundancy determination for time based location information in a wireless radio network
US9615349B2 (en) 2012-06-15 2017-04-04 At&T Intellectual Property I, L.P. Geographic redundancy determination for time based location information in a wireless radio network
US9769615B2 (en) 2012-06-15 2017-09-19 At&T Intellectual Property I, L.P. Geographic redundancy determination for time based location information in a wireless radio network
US9398556B2 (en) 2012-06-15 2016-07-19 At&T Intellectual Property I, L.P. Geographic redundancy determination for time based location information in a wireless radio network
US10225816B2 (en) 2012-06-19 2019-03-05 At&T Mobility Ii Llc Facilitation of timed fingerprint mobile device locating
US9408174B2 (en) 2012-06-19 2016-08-02 At&T Mobility Ii Llc Facilitation of timed fingerprint mobile device locating
US8892054B2 (en) 2012-07-17 2014-11-18 At&T Mobility Ii Llc Facilitation of delay error correction in timing-based location systems
US9591495B2 (en) 2012-07-17 2017-03-07 At&T Mobility Ii Llc Facilitation of delay error correction in timing-based location systems
US9247441B2 (en) 2012-07-17 2016-01-26 At&T Mobility Ii Llc Facilitation of delay error correction in timing-based location systems
US20170163817A1 (en) * 2012-07-24 2017-06-08 Twilio, Inc. Method and system for preventing illicit use of a telephony platform
US9948788B2 (en) * 2012-07-24 2018-04-17 Twilio, Inc. Method and system for preventing illicit use of a telephony platform
US9351223B2 (en) 2012-07-25 2016-05-24 At&T Mobility Ii Llc Assignment of hierarchical cell structures employing geolocation techniques
US10039111B2 (en) 2012-07-25 2018-07-31 At&T Mobility Ii Llc Assignment of hierarchical cell structures employing geolocation techniques
US9311672B2 (en) * 2012-08-09 2016-04-12 American Express Travel Related Services Company, Inc. Systems and methods for fraud detection using a cooperative data exchange
US10257674B2 (en) 2012-10-15 2019-04-09 Twilio, Inc. System and method for triggering on platform usage
US10033617B2 (en) 2012-10-15 2018-07-24 Twilio, Inc. System and method for triggering on platform usage
US9231979B2 (en) 2013-03-14 2016-01-05 Sas Institute Inc. Rule optimization for classification and detection
US9594907B2 (en) 2013-03-14 2017-03-14 Sas Institute Inc. Unauthorized activity detection and classification
US10051011B2 (en) 2013-03-14 2018-08-14 Twilio, Inc. System and method for integrating session initiation protocol communication in a telecommunications platform
US8855999B1 (en) 2013-03-15 2014-10-07 Palantir Technologies Inc. Method and system for generating a parser and parsing complex data
US8788405B1 (en) * 2013-03-15 2014-07-22 Palantir Technologies, Inc. Generating data clusters with customizable analysis strategies
US10120857B2 (en) 2013-03-15 2018-11-06 Palantir Technologies Inc. Method and system for generating a parser and parsing complex data
US9135658B2 (en) 2013-03-15 2015-09-15 Palantir Technologies Inc. Generating data clusters
US9165299B1 (en) 2013-03-15 2015-10-20 Palantir Technologies Inc. User-agent data clustering
US9171334B1 (en) 2013-03-15 2015-10-27 Palantir Technologies Inc. Tax data clustering
US8788407B1 (en) 2013-03-15 2014-07-22 Palantir Technologies Inc. Malware data clustering
US10264014B2 (en) 2013-03-15 2019-04-16 Palantir Technologies Inc. Systems and user interfaces for dynamic and interactive investigation based on automatic clustering of related data in various data structures
US10216801B2 (en) 2013-03-15 2019-02-26 Palantir Technologies Inc. Generating data clusters
US9177344B1 (en) 2013-03-15 2015-11-03 Palantir Technologies Inc. Trend data clustering
US8818892B1 (en) * 2013-03-15 2014-08-26 Palantir Technologies, Inc. Prioritizing data clusters with customizable scoring strategies
US9230280B1 (en) 2013-03-15 2016-01-05 Palantir Technologies Inc. Clustering data based on indications of financial malfeasance
US9965937B2 (en) 2013-03-15 2018-05-08 Palantir Technologies Inc. External malware data item clustering and analysis
US8930897B2 (en) 2013-03-15 2015-01-06 Palantir Technologies Inc. Data integration tool
US9992608B2 (en) 2013-06-19 2018-06-05 Twilio, Inc. System and method for providing a communication endpoint information service
US10057734B2 (en) 2013-06-19 2018-08-21 Twilio Inc. System and method for transmitting and receiving media messages
EP2840542A3 (en) * 2013-08-19 2015-03-25 Compass Plus (GB) Limited Method and system for detection of fraudulent transactions
US9853872B2 (en) 2013-09-17 2017-12-26 Twilio, Inc. System and method for providing communication platform metadata
US9811398B2 (en) 2013-09-17 2017-11-07 Twilio, Inc. System and method for tagging and tracking events of an application platform
US9959151B2 (en) 2013-09-17 2018-05-01 Twilio, Inc. System and method for tagging and tracking events of an application platform
US10063461B2 (en) 2013-11-12 2018-08-28 Twilio, Inc. System and method for client communication in a distributed telephony network
US10069773B2 (en) 2013-11-12 2018-09-04 Twilio, Inc. System and method for enabling dynamic multi-modal communication
US9552615B2 (en) 2013-12-20 2017-01-24 Palantir Technologies Inc. Automated database analysis to detect malfeasance
US10230746B2 (en) 2014-01-03 2019-03-12 Palantir Technologies Inc. System and method for evaluating network threats and usage
US9923925B2 (en) 2014-02-20 2018-03-20 Palantir Technologies Inc. Cyber security sharing and identification system
US9009827B1 (en) 2014-02-20 2015-04-14 Palantir Technologies Inc. Security sharing system
US10003693B2 (en) 2014-03-14 2018-06-19 Twilio, Inc. System and method for a work distribution service
US9907010B2 (en) 2014-04-17 2018-02-27 Twilio, Inc. System and method for enabling multi-modal communication
US10162887B2 (en) 2014-06-30 2018-12-25 Palantir Technologies Inc. Systems and methods for key phrase characterization of documents
US10180929B1 (en) 2014-06-30 2019-01-15 Palantir Technologies, Inc. Systems and methods for identifying key phrase clusters within documents
US9535974B1 (en) 2014-06-30 2017-01-03 Palantir Technologies Inc. Systems and methods for identifying key phrase clusters within documents
US9785773B2 (en) 2014-07-03 2017-10-10 Palantir Technologies Inc. Malware data item analysis
US9881074B2 (en) 2014-07-03 2018-01-30 Palantir Technologies Inc. System and method for news events detection and visualization
US9021260B1 (en) 2014-07-03 2015-04-28 Palantir Technologies Inc. Malware data item analysis
US9344447B2 (en) 2014-07-03 2016-05-17 Palantir Technologies Inc. Internal malware data item clustering and analysis
US9875293B2 (en) 2014-07-03 2018-01-23 Palanter Technologies Inc. System and method for news events detection and visualization
US9998485B2 (en) 2014-07-03 2018-06-12 Palantir Technologies, Inc. Network intrusion data item clustering and analysis
US9202249B1 (en) 2014-07-03 2015-12-01 Palantir Technologies Inc. Data item clustering and analysis
US10229126B2 (en) 2014-07-07 2019-03-12 Twilio, Inc. Method and system for applying data retention policies in a computing platform
US10212237B2 (en) 2014-07-07 2019-02-19 Twilio, Inc. System and method for managing media and signaling in a communication platform
US10116733B2 (en) 2014-07-07 2018-10-30 Twilio, Inc. System and method for collecting feedback in a multi-tenant communication platform
US9858279B2 (en) 2014-07-07 2018-01-02 Twilio, Inc. Method and system for applying data retention policies in a computing platform
US9774687B2 (en) 2014-07-07 2017-09-26 Twilio, Inc. System and method for managing media and signaling in a communication platform
US9906607B2 (en) 2014-10-21 2018-02-27 Twilio, Inc. System and method for providing a micro-services communication platform
US9043894B1 (en) 2014-11-06 2015-05-26 Palantir Technologies Inc. Malicious software detection in a computing system
US9558352B1 (en) 2014-11-06 2017-01-31 Palantir Technologies Inc. Malicious software detection in a computing system
US10135863B2 (en) 2014-11-06 2018-11-20 Palantir Technologies Inc. Malicious software detection in a computing system
US9367872B1 (en) 2014-12-22 2016-06-14 Palantir Technologies Inc. Systems and user interfaces for dynamic and interactive investigation of bad actor behavior based on automatic clustering of related data in various data structures
US9589299B2 (en) 2014-12-22 2017-03-07 Palantir Technologies Inc. Systems and user interfaces for dynamic and interactive investigation of bad actor behavior based on automatic clustering of related data in various data structures
US9898528B2 (en) 2014-12-22 2018-02-20 Palantir Technologies Inc. Concept indexing among database of documents using machine learning techniques
US9817563B1 (en) 2014-12-29 2017-11-14 Palantir Technologies Inc. System and method of generating data points from one or more data stores of data items for chart creation and manipulation
US9805399B2 (en) 2015-02-03 2017-10-31 Twilio, Inc. System and method for a media intelligence platform
US9351111B1 (en) 2015-03-06 2016-05-24 At&T Mobility Ii Llc Access to mobile location related information
US10206056B2 (en) 2015-03-06 2019-02-12 At&T Mobility Ii Llc Access to mobile location related information
US10103953B1 (en) 2015-05-12 2018-10-16 Palantir Technologies Inc. Methods and systems for analyzing entity performance
US9948703B2 (en) 2015-05-14 2018-04-17 Twilio, Inc. System and method for signaling through data storage
US10223748B2 (en) 2015-07-30 2019-03-05 Palantir Technologies Inc. Systems and user interfaces for holistic, data-driven investigation of bad actor behavior based on clustering and scoring of related data
US9454785B1 (en) 2015-07-30 2016-09-27 Palantir Technologies Inc. Systems and user interfaces for holistic, data-driven investigation of bad actor behavior based on clustering and scoring of related data
US9635046B2 (en) 2015-08-06 2017-04-25 Palantir Technologies Inc. Systems, methods, user interfaces, and computer-readable media for investigating potential malicious communications
US9898509B2 (en) 2015-08-28 2018-02-20 Palantir Technologies Inc. Malicious activity detection system capable of efficiently processing data accessed from databases and generating alerts for display in interactive user interfaces
US10275778B1 (en) 2015-12-30 2019-04-30 Palantir Technologies Inc. Systems and user interfaces for dynamic and interactive investigation based on automatic malfeasance clustering of related data in various data structures
US10063713B2 (en) 2016-05-23 2018-08-28 Twilio Inc. System and method for programmatic device connectivity
US10235461B2 (en) 2017-05-02 2019-03-19 Palantir Technologies Inc. Automated assistance for generating relevant and valuable search results for an entity of interest

Also Published As

Publication number Publication date
AU4927601A (en) 2001-10-08
WO2001073652A1 (en) 2001-10-04
AU2001249276A2 (en) 2001-10-08
CN1439139A (en) 2003-08-27
EP1279125A4 (en) 2006-01-04
US20030069820A1 (en) 2003-04-10
KR20020086695A (en) 2002-11-18
EP1279125A1 (en) 2003-01-29
JP2003529160A (en) 2003-09-30
US6714918B2 (en) 2004-03-30

Similar Documents

Publication Publication Date Title
US7620596B2 (en) Systems and methods for evaluating financial transaction risk
US5426281A (en) Transaction protection system
US8560440B2 (en) System and method for providing a credit card with multiple credit lines
US6193155B1 (en) Method and apparatus for issuing and managing gift certificates
US6327577B1 (en) Electronic bill payment system with account-number scheming
US8099360B2 (en) Method and apparatus for issuing and managing gift certificates
US7082416B2 (en) Method of using prepaid cash card for making purchases on the world wide web
US8540150B2 (en) Anonymous transaction card
KR100230455B1 (en) Accounting apparatus and method of management automation system
US6018721A (en) Method and system for improved collateral monitoring and control
US7752132B2 (en) Electronic payment and authentication system with debit and identification data verification and electronic check capabilities
US7581674B2 (en) Financial transaction system and method
US8326758B2 (en) Proxy card representing many monetary sources from a plurality of vendors
US7451114B1 (en) Conducting commerce between individuals
US8612339B2 (en) System and method for business online account opening
CA2044372C (en) Account transaction system
US7870078B2 (en) System, method and computer program product for assessing risk of identity theft
US6332134B1 (en) Financial transaction system
US20070136192A1 (en) Method and system for facilitating payment of an online auction transaction
US8046260B2 (en) Method and system for authorising returns
US20030009382A1 (en) Customer identification, loyalty and merchant payment gateway
US7356506B2 (en) Methods and apparatus for evaluating a credit application
US7505938B2 (en) Interactive voice response quick credit system and associated methods
US20150178734A1 (en) System for accepting a first account for making payment for a transaction and paying for the transaction with funds from a second account
US20090222308A1 (en) Detecting first party fraud abuse