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System and method of detecting fraud

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US20100145836A1
US20100145836A1 US12710228 US71022810A US20100145836A1 US 20100145836 A1 US20100145836 A1 US 20100145836A1 US 12710228 US12710228 US 12710228 US 71022810 A US71022810 A US 71022810A US 20100145836 A1 US20100145836 A1 US 20100145836A1
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data
models
fraud
model
transaction
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US12710228
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James C. Baker
Jacob Spoelstra
Yuansong Liao
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Corelogic Information Solutions Inc
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Basepoint Analytics LLC
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    • 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
    • G06Q10/00Administration; Management
    • G06Q10/06Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
    • G06Q10/067Business modelling
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    • 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
    • G06Q20/04Payment circuits
    • 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
    • G06Q20/10Payment architectures specially adapted for electronic funds transfer [EFT] systems; specially adapted for home banking systems
    • 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
    • G06Q20/10Payment architectures specially adapted for electronic funds transfer [EFT] systems; specially adapted for home banking systems
    • G06Q20/108Remote banking, e.g. home 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
    • 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
    • G06Q20/401Transaction verification
    • G06Q20/4016Transaction verification involving fraud or risk level assessment in transaction processing
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    • 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
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    • 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
    • G06Q30/00Commerce, e.g. shopping or e-commerce
    • G06Q30/01Customer relationship, e.g. warranty
    • G06Q30/018Business or product certification or verification
    • G06Q30/0185Product, service or business identity fraud
    • 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
    • G06Q30/00Commerce, e.g. shopping or e-commerce
    • G06Q30/06Buying, selling or leasing transactions
    • 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
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    • 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
    • G06Q40/08Insurance, e.g. risk analysis or pensions
    • 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
    • G06Q40/12Accounting

Abstract

Embodiments include systems and methods of detecting fraud. In particular, one embodiment includes a system and method of detecting fraud in transaction data such as payment card transaction data. For example, one embodiment includes a computerized method of detecting that comprises receiving data associated with a financial transaction and at least one transacting entity, wherein the data associated with the transacting entity comprises at least a portion of each of a plurality of historical transactions of the transacting entity, applying the data to at least one first model, generating a score based on the first model, and generating data indicative of fraud based at least partly on the score. Other embodiments include systems and methods of generating models for use in fraud detection systems.

Description

    RELATED APPLICATIONS
  • [0001]
    This application is a continuation of U.S. application Ser. No. 11/543,271, filed Oct. 3, 2006, which will issue on Feb. 23, 2010 as U.S. Pat. No. 7,668,769, and which claims the benefit of U.S. Provisional Patent Application No. 60/723,405, filed Oct. 4, 2005, the disclosures of which are incorporated herein by reference in their entireties.
  • BACKGROUND OF THE INVENTION
  • [0002]
    1. Field of the Invention
  • [0003]
    The present invention relates to detecting fraud in financial transactions.
  • [0004]
    2. Description of the Related Technology
  • [0005]
    Fraud detection systems detect fraud in financial transactions. For example, a payment card transaction fraud detection system may be configured to analyze transaction data to identify transactions that are fraudulent.
  • [0006]
    Existing payment card fraud detection systems may use transaction data in addition to data related to the transacting entities to identify fraud. Such systems may operate in either batch (processing transactions as a group of files at periodic times during the day) or real time mode (processing transactions one at a time, as they enter the system). However, the fraud detection capabilities of existing systems have not kept pace with either the types of payment card fraudulent activity that have evolved or increasing processing and storage capabilities of computing systems.
  • [0007]
    Financial transactions and payment card transaction data may refer to transactions, authorization of transactions, external data and other activities such as non-monetary transactions, payments, postings or a voice response unit (VRU) events. Moreover, payment card transaction data may include data derived from transactions using a physical payment card, e.g., with a magnetic stripe, and electronic transactions in which payment card data is used without the payment card being physically read or presented. Financial transactions can include credit or debit based transactions associated with, for example, a point of sale (POS) terminal or an automated teller machine (ATM). These transactions are often aggregated into databases from which an analysis can be performed for fraud.
  • [0008]
    However, existing fraud detection systems have failed to keep pace with the dynamic nature of financial transactions and payment card fraud. Moreover, such systems have failed to take advantage of the increased capabilities of computer systems. Thus, a need exists for improved systems and methods of detecting payment card fraud.
  • SUMMARY OF CERTAIN INVENTIVE ASPECTS
  • [0009]
    The system, method, and devices of the invention each have several aspects, no single one of which is solely responsible for its desirable attributes. Without limiting the scope of this invention as expressed by the claims which follow, its more prominent features will now be discussed briefly. After considering this discussion, and particularly after reading the section entitled “Detailed Description of Certain Embodiments” one will understand how the features of this invention provide advantages that include improved and more accurate payment card fraud detection.
  • [0010]
    One embodiment includes a computerized method of detecting fraud. The method includes receiving data associated with a financial transaction and at least one transacting entity. The data associated with the transacting entity comprises at least a portion of each of a plurality of historical transactions of the transacting entity. The method further includes applying the data to at least one first model. The method further includes generating a score based on the first model. The method further includes generating data indicative of fraud based at least partly on the score.
  • [0011]
    One embodiment includes a system for detecting fraud. The system includes a storage configured to receive data associated with at least one transacting entity. The data associated with the transacting entity comprises at least a portion of each of a plurality of historical transactions of the transacting entity. The system further includes a processor configured to apply transaction data and the data associated with the at least one transacting entity to at least one first model, generate a score based on the first model, and generate data indicative of fraud based at least partly on the score.
  • [0012]
    One embodiment includes a system for detecting fraud. The system includes means for receiving data associated with at least one transacting entity. The data associated with the transacting entity comprises at least a portion of each of a plurality of historical transactions of the transacting entity. The system further includes means for processing transaction data. The processing means is configured to apply transaction data and the data associated with the at least one transacting entity to at least one first model, generate a score based on the first model, and generate data indicative of fraud based at least partly on the score.
  • BRIEF DESCRIPTION OF THE DRAWINGS
  • [0013]
    FIG. 1 is a functional block diagram illustrating a fraud detection system such as for use with a payment card transaction processing or authorization system.
  • [0014]
    FIG. 2 is a high level flowchart illustrating an example method of model generation and use according to one embodiment of the payment card fraud detection system of FIG. 1.
  • [0015]
    FIG. 3 is a functional block diagram illustrating in further detail portions of the payment card fraud detection system of FIG. 1.
  • [0016]
    FIG. 4 is a flowchart illustrating an example of a method of processing transaction data according to one embodiment of the payment card fraud detection system of FIG. 1.
  • [0017]
    FIG. 5 is a flowchart illustrating in more detail one embodiment of a portion of the method illustrated in FIG. 4.
  • [0018]
    FIG. 6 is a flowchart illustrating in more detail one embodiment of a portion of the method illustrated in FIG. 4.
  • [0019]
    FIG. 7 is a functional block diagram illustrating portions of one embodiment of the payment card fraud detection system of FIG. 1.
  • DETAILED DESCRIPTION OF CERTAIN EMBODIMENTS
  • [0020]
    The following detailed description is directed to certain specific embodiments of the invention. However, the invention can be embodied in a multitude of different ways as defined and covered by the claims. In this description, reference is made to the drawings wherein like parts are designated with like numerals throughout.
  • [0021]
    For example, it has been found that, as discussed below with reference to certain embodiments, payment card fraud detection can be improved by using stored past transaction data in place of, or in addition to, summarized forms of past transaction data. In addition, in one embodiment, payment card fraud detection is improved by using statistical information that is stored according to groups of individuals that form clusters. In one such embodiment, fraud is identified with reference to deviation from identified clusters. In one embodiment, in addition to data associated with the direct parties to a transaction (e.g., a credit or debit card user and a merchant), embodiments of fraud detection systems may use data that is stored in association with one or more entities associated with the processing of the transaction such as a point of sale terminal, or an automated teller machine (ATM). The entities may be real persons, particular instances of processing equipment, a particular merchant, or a chain or brand associated with the merchant. Fraud generally refers to any material misrepresentation associated with a transaction (or authorization of the transaction). For example, fraud may refer to misrepresentations associated with the identity of the parties, misrepresentations associated with the authenticity or validity of a credit or debit card, misrepresentations associated with obtaining credit used in the transaction or misrepresentations regarding a purchaser's ability or likelihood of payment. In addition to data associated with transactions and the entities, various patterns may be detected from external sources, such as data available from a credit bureau or other data aggregator.
  • [0022]
    FIG. 1 is a functional block diagram illustrating a fraud detection system 100. The fraud detection system 100 may be configured to detect fraud in a “real time” mode, e.g., prior to, or as part of, the authorization process for the transaction. In one embodiment, authorization of the transaction is based on data indicative of fraud provided by the fraud detection system 100. Alternatively, or in addition, the fraud detection system 100 may be configured to detect fraud in a batch mode, e.g., after the transaction has been completed. The data indicative of fraud generated by the system 100 may be used as part of the authorization process of subsequent transactions by the entities or account, to associate an indicator of fraud with one or more of the entities or an account associated with the transaction. The indicator of fraud may indicate that the entity or account is in a state indicative of fraud that can be used to indicate the need to take one or more actions such as contacting the entity, lock the account, point of sale terminal, or ATM associated with the transaction, or take any other suitable action.
  • [0023]
    The system 100 includes an authorization/scoring driver 102 that provides transaction data for processing by a scorer 104 such as during authorization of the corresponding transaction. In one embodiment, the transaction data is payment card transaction data. The scorer 104 applies the transaction data to one or more models 106 to generate data indicative of fraud associated with the transaction data. A batch driver 108 may also provide transaction data in a batch mode for processing by the scorer 104. The batch driver 108 may also receive other transaction related data such as postings, payments, and non-monetary data from one or more databases 110 and provide that data to the scorer 104 for scoring of the associated entity or account, and/or for use in scoring transaction data.
  • [0024]
    In one embodiment, a risk triggers module 112 provides triggers to a triggers database 114 of the system 100. Triggers may include relationships (e.g., a recent payment is more than the balance due) or rules that have been identified as being indicative of fraud. In one embodiment, a third party or card issuer periodically provides triggers to the triggers database 114. One or more models 106 of the scorer 104 may generate data indicative of fraud based on the triggers from the triggers database 114. A credit bureau data module 116 receives data from one or more credit bureaus and provides that data to the scorer 104.
  • [0025]
    In one embodiment, one or more of the models 106 provides the data indicative of fraud based on historical transactions of one or more transacting entities. The historical transactions associated with entities may include transaction records and integrated trend summaries (TRAITS). In one embodiment, a database 118 is configured to maintain the historical transactions of one or more transacting entities, including TRAITS data and provide that data to the scorer 104.
  • [0026]
    One embodiment includes a model updater 120 that generates new or updated models 106 and provides those models 106 to the scorer 104. The model updater 120 may be configured to update one or more of the models 106 periodically, e.g., nightly or weekly. The model updater 120 may also update one or more of the models 106 to provide the system 100 with new models 106 as part of new system or software version deployments.
  • [0027]
    FIG. 2 is a high level flowchart illustrating an example method 150 of model generation and use according to one embodiment of the fraud detection system 100. The method 150 begins at a block 152 in which the models 106 are generated. In one embodiment, the models 106 include supervised models that are generated based on training or data analysis that is based on historical transactions or applications that have been identified as fraudulent or non-fraudulent. The models 106 may include other types of learning based models including, for example, unsupervised learning models such as clustering (e.g., K-means or Mahalanobis distance based models), anomaly (or outlier) detection (e.g., principal components or compression neural networks), competitive Learning (e.g., Kohonen self-organizing maps) and one-class support vector machine (SVM). Moving to a block 154, the system 100 applies transaction data to models 106. The functions of block 154 may be repeated for each transaction, approval, or other unit of data that is to be processed. Further detail of applying data to the models is described with reference to FIG. 4. Proceeding to a block 156, the model updater 120 updates models and related data at intervals that may vary depending upon the particular model 106. For example, in one embodiment, TRAITS data in the database 118 may be updated in real time as new data arrives, while clustering based models are updated periodically, e.g., daily. Certain models may be updated manually or when new versions of software of the system 100 are released.
  • [0028]
    FIG. 3 is a functional block diagram illustrating in further detail portions of the scorer 104, including data flow between certain portions of the system 100. The scorer 104 includes a data input manager 202 that receives transactions and data from various databases and applies that data to the models 106, a model expert selector 204 that selects one or more of the models 106 to which transaction data is applied to generate data indicative of fraud, and a score summarizer 206 that receives data indicative of fraud, including scores, from one or more models and generates a combined data or score to associate with the transaction data. The scorer 104 may provide the score(s) to a real time authorization/decision referral module 212 that may be part of the system 100 or external to the system 100. Depending on the mode of operation, e.g., real time or batch operation, the scorer 104 may provide the score or other data indicative of fraud to a case management module 214 that manages a card issuer or other party's actions in response to the data indicative of fraud.
  • [0029]
    The data input manager 202 may interface with a variety of data sources to provide data for use by the models 106 via an I/O interface modules 220. In order to make the entire system 100 as platform and site independent as possible I/O calls may be performed through site-specific I/O modules 220 without customizing other system components. For example, in addition to credit bureau data 116 (shown on FIG. 1), the data input manager 202 may interface with expert model requirements data 222, file format definitions 224, historical transactions (e.g., TRAITS) data 118, triggers 114, and one or more client databases 226 that provides account and other entity related data. It is to be recognized that the client databases 226 may be external to the system 100. The operation of the data input manager 202 may be data driven. For example, the data input manager 202 may select data to provide to particular models 106 based on data from the expert model requirements database 222.
  • [0030]
    The data input manager 202 may also be configured to access new or additional databases such as the client databases 226 based on file format definitions 224. Each file format record has a first field that defines the format of the record (e.g., A101 for an authorization record, N101 for a nonmonetary record, P101 for a payment record, and so forth). When new fields or record types are added, the new format template with a new ID is added to the format database 224.
  • [0031]
    In one embodiment, the scorer 104 may include a model bus 216 that provides a configurable component interface to the models 106 that allows new models to be inserted or removed from the system independently (of the other models 106). Desirably, the system 100 is easily modified to account for new types of fraud, additional models to better identify fraud, and models configured to identify fraud in particular types of transactions or contexts. Moreover, new models 106 can be added and old models replaced without need to retrain or update existing models 106. For example, if debit cards or business cards processing is added to a particular embodiment of the system 100, models 106 can be trained on these types of cards, rather than having to rebuild and retrain the entire system 100 as might be the case if a single large monolithic model was used. In one embodiment, the models 106 include one or more specialized models 106 that are directed to specific types of transactions or specific fraud patterns (e.g., foreign transactions, ATM transactions, fraud type specialists) to improve overall fraud detection performance. The models may also include “local expert” models that focus on particular risk factors such as high amounts or foreign transactions, particular fraud patterns such as application fraud, or one or more triggers such as a payment being larger than the balance due.
  • [0032]
    Such component models 106 can be more compact, efficient, and focused, with, e.g. around 40 input values each, than large, monolithic models with hundreds of inputs. Desirably, use of a set of the models 106 has been found to reduce the amount of calculations needed to process a data set by comparison with typical large monolithic models configured to perform similar scoring. Such reduction in calculations by the models 106 tends to improve system throughput and reduce system latency.
  • [0033]
    The use of transaction records and integrated trend summaries (TRAITS) data 118 that includes at least a portion of the data associated with one or more transactions desirably has been found to provide better predictive performance than merely using summarized or averaged data. In one embodiment, TRAITS data for an entity or account comprises one or more fields from the actual transaction history for the entity or account into a data record for that entity or account. In one embodiment, the data record is a single record in order to minimize database access and fetch time. Such TRAITS data records better encapsulate fine detail (e.g., the last 50 merchant category codes (MCCs) used by the cardholder) and allow actual time series data to be analyzed by the models 106. For example, a particular model 106 may use link analysis as part of its score generation.
  • [0034]
    In one embodiment, models 106 may include cardholder clusters (generated, for example, using an unsupervised or clustering based algorithm) based on the corresponding TRAITS in order to capture spending habits by category and spending behavior within a cluster. Particular models 106 may identify transition of an account from cluster to cluster and take into account expected divergences from normal behavior due to life events (see table below). As illustrated in Table 1 below, TRAITS can help the models 106 identify how customers move from one “activity state” to another. The models 106 may generate scores based on whether the particular transitions are indicative of fraudulent account behavior.
  • [0000]
    TABLE 1
    Example of TRAITS changing from one cluster to another
    Move From To
    Domestic Spender Foreign Spender
    Dormant Active
    Non-Holiday Holiday
    Non-Traveler Traveler
    Threw Card Away Reissue (cardholder begins
    using card again)
    Stable Residence Just Moved
  • [0035]
    TRAITS can also be based on transaction history for entities such as Automated Teller Machines (ATMs), Point of Sale devices (POS), Merchants, Issuer Portfolio, Issuer BIN, and MCC code levels.
  • [0036]
    Supervised or unsupervised models 106 can be generated (e.g., trained) based on entity traits. The unsupervised models 106 can be trained to spot new and emerging fraud trends that are not in the training data. Unsupervised models can also identify divergences from legitimate behavior and classify them into more or less risky clusters. This allows the models 106 to maintain a high level of adaptability to changing conditions without the need for frequent retraining.
  • [0037]
    In one embodiment, at least a portion of the models 106 may be configured to define a “committee” or “panels of experts.” A committee comprises a group of the models 106 that are optimized in terms of input variables and TRAITS for a particular type of fraud detection, particular type of transaction, particular type of entity or entity state, or suitable for processing any other particular data configurations. In one embodiment, each model 106 is configured to identify its area of expertise in terms of transaction or entity data to allow the model expert selector 204 to select the particular model 106 when suitable transaction data is being processed. By providing such configuration data, the system 100 can be configured more easily, with less configuration management, etc. to include new or different models 106 over time or in a particular instance of the system 100.
  • [0038]
    For example, the models 106 may comprise models 106 configured to process foreign transactions, jewelry purchases, and or young accounts (“thin files”). Thus, a young account (thin file) transacting at a jewelry store outside the United States may employ all three of these example models 106 to score the transaction. Each model 106 may provide a score and confidence level to the score summarizer 206 to be combined to generate a combined score and appropriate risk indicators.
  • [0039]
    Models 106 may be based on any suitable modeling technique including, for example, neural networks, cascades (which may significantly improve performance for real-time referrals if trained on fraud transaction tags (identifying training transactions as fraudulent) rather than fraud account tags (identifying accounts as being used fraudulently)), support vector machines (SVM), genetic algorithms (GA), fuzzy logic, fraud case-based reasoning, decision trees, naïve Bayesian, logistic regression, and scorecards.
  • [0040]
    The models 106 may be supervised (i.e., with fraud tagging of the training data) or unsupervised (i.e., without fraud tags). In one embodiment, unsupervised models are used for models 106 that use as input data such as TRAITS built on portfolios of accounts, Bank Identification Number (BIN)—a unique series of numbers assigned by Visa/MasterCard to member institutions that identifies each institution in transaction processing, and merchant category codes (MCC). The unsupervised models 106 can help identify new fraud trends by identifying accounts and their TRAITS that are diverging from legitimate behavior, but that do not diverge in a way previously identified. The unsupervised models 106 may be based on one or more of clustering (e.g., K-means or Mahalanobis distance based models), anomaly (or outlier) detection (e.g., principal components or compression neural networks), competitive learning (e.g., Kohonen self-organizing maps) and one-class support vector machine (SVM).
  • [0041]
    When such expert models 106 are small and have fewer inputs than typical monolithic models, they can be refreshed or retrained much more efficiently. If it is desirable to retrain a particular expert model 106, such retraining can be performed without retraining the entire set of models.
  • [0042]
    Desirably, the use of smaller expert models 106 via the model bus 216 allows various models 106 to be easily retrained or updated on different cycles. For example, it has been found that a typical fraud detection model is desirably retrained on a 12-18 month interval. Performance tends to decay somewhat over the time the model package is deployed. By retraining only a subset of the expert models 106 at different time points (e.g., a staggered retrain), the model 106 can be kept more up to date. For example, after the initial deployment of the models 106, half of the expert's models may be retrained at the 6 month point, the remaining half at the 12 month point, and the original half at the 18 month point again, and so forth. Thus, the models 106 can be retrained more frequently than typical single-retrain model packages where the entire model is monolithically retrained.
  • [0043]
    In one embodiment, the model updater 120 may segment the data into subsets to better model input data. For example, if subsets of a data set are identified with significantly distinct behavior, special models designed especially for these subsets normally outperform a general fit-all model. In one embodiment, a prior knowledge of data can be used to segment the data for generation or retraining of the models 106. For example, in one embodiment, data is segregated geographically so that, for example, regional differences do not confound fraud detection. In other embodiments, data driven techniques, e.g., unsupervised techniques such as clustering, are used to identify data segments that may benefit from a separate supervised model.
  • [0044]
    In one embodiment, the models 106 may output various types of data indicative of fraud to the score summarize 206. In addition to fraud scores in a particular range, e.g., 1-999, secondary scores may be generated to provide additional ways of detecting or quantifying risks such as first-party fraud, identity theft, application fraud, and merchant risk. Such scores for the models 106 may depend on the characteristics of the transaction, any triggers in place for the account, and TRAITS of entities associated with particular transaction data. One or more of the models may also generate a “bust-out” score (e.g., in the range of 1-999) indicative of a cardholder who maintains good credit for a time period then suddenly begins spending larger amounts (without paying). One or more of the models may also generate a “mass compromise” score (e.g., in the range of 1-999) indicative of a multi-account compromise. It has been found that the use of TRAITS for groups of accountholders or a portfolio of accounts desirably improves the detection of mass compromise events by the models 106. Model scores may also include a point-of-sale (POS) Action Score (Refer Score) (1-999), for example, for real time identification and referral to the vendor or issuer of accounts or transactions that appear to be fraudulent prior to approval. One or more of the models 106 may generate fraud application risk scores for new accounts and balance transfer and convenience check related scores. In addition, one or more of the models 106 may predict fraud types including identity fraud. One or more of the models 106 may also generate loss estimates to allow an issuer or other user of the system 100 to identify and focus on accounts with potential large losses. Treatment or authorization scores may be generated to provide data indicative of how to treat or whether to authorize a transaction. A chargeback status may be generated based on Issuer rules or on a success probability generated by the particular model 106. Scores may also be generated that are associated with various entities to the transaction such as the merchant, the transacting POS, or the transacting ATM.
  • [0045]
    One or more of the models 106 may also be configured to read in data files of account numbers (e.g., for mass compromises) and output various severity indicators to incorporate with the fraud score for special handling (e.g., identify cases for follow-up at lower scores, refer at lower scores, etc.)
  • [0046]
    The model expert selector 204 may select one or more of the models 106, e.g., a subset of the models 106, for evaluating a particular set of transaction data based on one or more of the triggers 114, the historical transaction and TRAITS database 118, data associated with the type or amount of the transaction, and the entities involved.
  • [0047]
    The score summarizer 206 assesses the confidence of each of the output of the selected models 106 and provides combined weight and treatment guidance, including a final score and risk indicators. The selected risk indicators may include explanations of potential types of frauds and recommendations for action. The score summarizer 106 may use a weighting to combine the scores of the selected models 106. In one embodiment, one or more of the models 106 provides weighting data. The score summarizer 106 combines the scores of each of the selected models 106 and provides a combined score along with data such as the risk indicators.
  • [0048]
    The models 106 and/or the score summarizer 206 may be configured to provide the Issuer with customized, automatic, and dynamic calibration to control referral levels and case creation volumes. Such dynamic calibration may be based on the actual in-production score distributions so as to eliminate ad hoc or arbitrary Christmas or other seasonal calibration. Such dynamic calibration conveniently provides the issuer with the ability to modify score distributions during times of emergencies or unusual events, such as natural disasters.
  • [0049]
    FIG. 4 is a flowchart illustrating an example of a method 154 of processing transaction data according to one embodiment of the fraud detection system 100. At a block 302, the data input manager 202 receives transaction data. Next at a block 304, the data input manager 202 receives, e.g., from I/O modules 220, data associated with transacting entity and TRAITS data, e.g., historical transactions, from one or more databases. Next at a block 306, the model expert selector 204 may identify clusters associated with the transacting entity based on the historical transactions. Proceeding to a block 308, the model expert selector 204 selects one or more of the models 106, e.g., based on transaction data, transacting entity, clusters, and historical transactions. Next at a block 310, the data input manager 202 applies transaction data, transacting entity, and historical transactions to the selected models 106 to generate scores and risk indicators. Moving to a block 312, the score summarizer 206 combines the model scores and reasons to generate a combined score and risk indicators. The system 100 may repeat the acts associated with the method 154 for multiple transactions, transactions authorizations, etc. in real time or in batch mode.
  • [0050]
    FIG. 5 is a flowchart illustrating in more detail an example of applying transaction data, transacting entity, and historical transactions to models to generate scores and risk indicators of the block 310 method 154. FIG. 5 illustrates an example of a committee scoring model for combining outputs of several models 106. At a block 332, the data input manager 202 (FIG. 3) applies a feature vector including one or more of transaction, transacting entity, cluster, and historical transactions to one or more models 106 to generate scores and risk indicators. Next at a block 334, the models 106 each generate score and reason data based on the feature vector. Moving to a block 336, the score summarizer 206 applies mixture parameters to the score and reason data to generate a committee score for the group of the models 106.
  • [0051]
    FIG. 6 is a flowchart illustrating in more detail another example of applying transaction data, transacting entity, and historical transactions to models to generate scores and risk indicators of the block 310 of the method 154. FIG. 6 illustrates an example of a scoring model in which two or more of the models 106 are used sequentially to generate score and risk indicator data. At a block 342, the data input manager 202 (FIG. 3) applies a feature vector including one or more of transaction, transacting entity, cluster, and historical transactions to one or more models 106 to generate scores and risk indicators. Next at a block 344, one of the models 106 generates score and reason data based on the feature vector. Moving to a block 346, the score summarizer 206 compares the score and reason data to one or more selected or predetermined criteria. Next at a decision block 348, if the comparison of the criteria with the score and reason data is indicative of applying an additional model, the method 310 proceeds back to the block 342 in which the feature vector (and/or additional or different data) are applied to another one of the models 106. Returning to the decision block 348, if the criteria are not indicative of applying another model, it proceeds to the block 350 in which the scoring summarizer 206 generates score and reason data based on the data obtained at the block 346.
  • [0052]
    In one embodiment, the committee score may be determined using one or more of the following models:
      • an equal weight average:
  • [0000]
    s c = 1 N i = 1 N s i ,
  • [0000]
    where N is the number of scores;
      • a weighted average:
  • [0000]
    s c = i = 1 N s i α i ,
  • [0000]
    where N is the number of scores and αi is estimated based on how predictive scores i is;
      • a competitive committee:
  • [0000]
    s c = 1 M i = 1 M s i ,
  • [0000]
    where siε (set of largest M scores); and
      • a neural network-based committee score.
  • [0057]
    FIG. 7 is a functional block diagram illustrating portions of one embodiment of the fraud detection system 100. In particular, FIG. 7 illustrates one embodiment of a process model for the system 100. The driver 102 comprises one or more processes or threads providing transaction data to the scorer 104. The scorer 104 includes one or more processes 402 for executing the data input manager 202 (FIG. 3). The data input manager processes 402 communicate via a shared memory 404 with one or more model execution processes 406 that execute the models 106, one or more scoring cache processes 408 configured to updating and maintaining transaction scores, and one or more TRAITS I/O processes configured to communicate with the TRAITS database 118. The processes 402, 406, 408, and 410 may be executed on one or more processors (not shown). In one embodiment, the processes 402, 406, 408, and 410 may be executed on one or more mainframe computer systems.
  • [0058]
    It is to be recognized that depending on the embodiment, certain acts or events of any of the methods described herein can be performed in a different sequence, may be added, merged, or left out all together (e.g., not all described acts or events are necessary for the practice of the method). Moreover, in certain embodiments, acts or events may be performed concurrently, e.g., through multi-threaded processing, interrupt processing, or multiple processors, rather than sequentially.
  • [0059]
    Those of skill will recognize that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
  • [0060]
    The various illustrative logical blocks, modules, and circuits described in connection with the embodiments disclosed herein may be implemented or performed with a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.
  • [0061]
    The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such that the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In the alternative, the processor and the storage medium may reside as discrete components in a user terminal.
  • [0062]
    While the above detailed description has shown, described, and pointed out novel features of the invention as applied to various embodiments, it will be understood that various omissions, substitutions, and changes in the form and details of the device or process illustrated may be made by those skilled in the art without departing from the spirit of the invention. As will be recognized, the present invention may be embodied within a form that does not provide all of the features and benefits set forth herein, as some features may be used or practiced separately from others. The scope of the invention is indicated by the appended claims rather than by the foregoing description. All changes which come within the meaning and range of equivalency of the claims are to be embraced within their scope.

Claims (28)

1. A computerized method of detecting fraud, the method comprising:
receiving, on at least one processor, data associated with a financial transaction and at least one transacting entity;
storing the data to a data storage;
selecting at least one model from a plurality of models;
applying the data to the selected model;
generating a score based on the selected model; and
generating data indicative of fraud based at least partly on the score.
2. The method of claim 1, wherein the data associated with the transacting entity comprises at least a portion of each of a plurality of historical transactions of the transacting entity.
3. The method of claim 1, further comprising:
authorizing the financial transaction based on the data indicative of fraud.
4. The method of claim 1, wherein the method is performed in real time during authorization of a transaction.
5. The method of claim 1, wherein the method is performed in batch mode after at least one transaction has been completed.
6. The method of claim 1, further comprising:
associating an indicator of fraud with at least one of the transacting entity and an account associated with the transacting entity.
7. The method of claim 1, further comprising:
identifying the transaction data for review based at least in part on the indicator of fraud.
8. The method of claim 1, wherein the selected model comprises a plurality of models.
9. The method of claim 1, further comprising updating the selected model via a model bus which provides for new models to be added and existing models to be removed without the need to retrain or update existing models.
10. The method of claim 9, further comprising selecting another model based on the data associated with the financial transaction.
11. The method of claim 1, wherein the transacting entity is an account, and wherein the method further comprises:
applying the received data to another model;
identifying the transacting entity as being associated with a plurality of clusters, wherein each of the clusters associates with a plurality of accounts based on the application of the received data to the other model and the received data;
identifying a transition associated with the transacting entity between at least two of the clusters; and
generating data indicative of fraud based at least partly on the score and at least partly on the identified transition.
12. The method of claim 1, wherein generating the data indicative of the fraud comprises combining outputs of the plurality of models using at least one of a committee and a panel of experts.
13. The method of claim 1, wherein the selected model comprises at least one of: a neural network, a cascaded neural network, a support vector machine, a genetic algorithm, a fuzzy logic model, a case-based reasoning model, a decision tree, a naïve Bayesian model, a logistic regression model, and a scorecard model.
14. A system for detecting fraud, the system comprising:
a storage configured to receive data associated with at least one transacting entity; and
a processor configured to:
select at least one model;
apply transaction data and the data associated with the at least one transacting entity to at least one model;
generate a score based on the model; and
generate data indicative of fraud based at least partly on the score.
15. The system of claim 14, wherein the processor is further configured to apply transaction data to at least one model, and wherein the system is configured to provide for incorporation of new models or removal of existing models independently of the selected model.
16. The system of claim 15, wherein the selected model is a specialized model.
17. The system of claim 14, further comprising a model expert selector configured to select one or more models for evaluating a particular set of transaction data.
18. The system of claim 17 wherein the model expert selector selects one or more models based on at least two of a trigger, the historical transaction data, data associated with the type or amounts of the transaction, and data indicative of the entities involved.
19. The system of claim 14, wherein the processor is further configured to associate an indicator of fraud with at least one of the transacting entity and an account associated with the transacting entity.
20. The system of claim 14, wherein the processor is further configured to select the at least one model based on the portion of each of the plurality of historical transactions.
21. The system of claim 20, wherein the processor is further configured to select at least another model based on the data associated with the financial transaction.
22. The system of claim 21, wherein the other model comprises a plurality of models.
23. The system of claim 22, wherein the processor is configured to generate the data indicative of the fraud at least in part by combining outputs of the plurality of models using at least one of a committee and a panel of experts.
24. The system of claim 14, wherein the at least one model comprises at least one of: a neural network, a cascaded neural network, a support vector machine, a genetic algorithm, a fuzzy logic model, a case-based reasoning model, a decision tree, a naïve Bayesian model, a logistic regression model, and a scorecard model.
25. The system of claim 14, wherein the processor is further configured to:
apply another model to the portion of each of the plurality of historical transactions; and
identify a first cluster with the transacting entity based on the other model.
26. The system of claim 22, wherein the transacting entity is an account, wherein the processor is further configured to:
apply the received data to another model;
identify the transacting entity as being associated with a plurality of clusters, wherein each of the clusters associates with a plurality of accounts based on the application of the received data to the other model and the received data;
identify a transition associated with the transacting entity between at least two of the clusters; and
generate data indicative of fraud based at least partly on the score and at least partly on the identified transition.
27. A computer readable medium having computer readable program code embodied thereon for detecting fraudulent transactions, the method comprising:
receiving data associated with a financial transaction and at least one transacting entity into a data storage;
applying the data to at least one model;
generating a score based on the model; and
generating data indicative of fraud based at least partly on the score,
wherein the data associated with the transacting entity comprises respective values of at least one data field of each of a plurality of historical transactions of the transacting entity.
28. A computerized fraud detection system, comprising:
means for receiving, on at least one processor, data associated with a financial transaction and at least one transacting entity, wherein the data associated with the transacting entity comprises respective values of at least one data field of each of a plurality of historical transactions of the transacting entity;
means for processing transaction data, said processing means configured to:
apply the data to at least one model;
generate a score based on the model; and
generate data indicative of fraud based at least partly on the score.
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Cited By (56)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090129377A1 (en) * 2007-11-19 2009-05-21 Simon Chamberlain Service for mapping ip addresses to user segments
US20100106629A1 (en) * 2006-06-13 2010-04-29 First American Real Estate Tax Service, Llc. Automatic delinquency item processing with customization for lenders
US20100250469A1 (en) * 2005-10-24 2010-09-30 Megdal Myles G Computer-Based Modeling of Spending Behaviors of Entities
US7975299B1 (en) 2007-04-05 2011-07-05 Consumerinfo.Com, Inc. Child identity monitor
US7991689B1 (en) * 2008-07-23 2011-08-02 Experian Information Solutions, Inc. Systems and methods for detecting bust out fraud using credit data
US8024264B2 (en) 2007-04-12 2011-09-20 Experian Marketing Solutions, Inc. Systems and methods for determining thin-file records and determining thin-file risk levels
US8036979B1 (en) 2006-10-05 2011-10-11 Experian Information Solutions, Inc. System and method for generating a finance attribute from tradeline data
US20120101930A1 (en) * 2010-10-21 2012-04-26 Caiwei Li Software and Methods for Risk and Fraud Mitigation
WO2012058066A1 (en) * 2010-10-29 2012-05-03 Q2 Software, Inc. System, method and computer program product for real-time online transaction risk and fraud analytics and management
US8214262B1 (en) 2006-12-04 2012-07-03 Lower My Bills, Inc. System and method of enhancing leads
US8301574B2 (en) 2007-09-17 2012-10-30 Experian Marketing Solutions, Inc. Multimedia engagement study
WO2012121983A3 (en) * 2011-03-04 2012-12-06 Brighterion, Inc. Systems and methods for adaptive identification of sources of fraud
US8355967B2 (en) 2008-06-18 2013-01-15 Consumerinfo.Com, Inc. Personal finance integration system and method
US8364588B2 (en) 2007-05-25 2013-01-29 Experian Information Solutions, Inc. System and method for automated detection of never-pay data sets
US8458074B2 (en) 2010-04-30 2013-06-04 Corelogic Solutions, Llc. Data analytics models for loan treatment
US8600872B1 (en) * 2007-07-27 2013-12-03 Wells Fargo Bank, N.A. System and method for detecting account compromises
US8606626B1 (en) 2007-01-31 2013-12-10 Experian Information Solutions, Inc. Systems and methods for providing a direct marketing campaign planning environment
US8706587B1 (en) * 2008-02-28 2014-04-22 Bank Of America Corporation Statistical prioritization and detection of potential financial crime events
US8781953B2 (en) 2003-03-21 2014-07-15 Consumerinfo.Com, Inc. Card management system and method
US20140214669A1 (en) * 2013-01-29 2014-07-31 Gravic, Inc. Methods for Reducing the Merchant Chargeback Notification Time
WO2014152419A1 (en) * 2013-03-15 2014-09-25 Mastercard International Incorporated Transaction-history driven counterfeit fraud risk management solution
US8914317B2 (en) 2012-06-28 2014-12-16 International Business Machines Corporation Detecting anomalies in real-time in multiple time series data with automated thresholding
US8931058B2 (en) 2010-07-01 2015-01-06 Experian Information Solutions, Inc. Systems and methods for permission arbitrated transaction services
US9058627B1 (en) 2002-05-30 2015-06-16 Consumerinfo.Com, Inc. Circular rotational interface for display of consumer credit information
US20150262184A1 (en) * 2014-03-12 2015-09-17 Microsoft Corporation Two stage risk model building and evaluation
US9147042B1 (en) 2010-11-22 2015-09-29 Experian Information Solutions, Inc. Systems and methods for data verification
US9230283B1 (en) 2007-12-14 2016-01-05 Consumerinfo.Com, Inc. Card registry systems and methods
US9256904B1 (en) 2008-08-14 2016-02-09 Experian Information Solutions, Inc. Multi-bureau credit file freeze and unfreeze
US9336494B1 (en) * 2012-08-20 2016-05-10 Context Relevant, Inc. Re-training a machine learning model
US9342783B1 (en) 2007-03-30 2016-05-17 Consumerinfo.Com, Inc. Systems and methods for data verification
USD759690S1 (en) 2014-03-25 2016-06-21 Consumerinfo.Com, Inc. Display screen or portion thereof with graphical user interface
USD759689S1 (en) 2014-03-25 2016-06-21 Consumerinfo.Com, Inc. Display screen or portion thereof with graphical user interface
USD760256S1 (en) 2014-03-25 2016-06-28 Consumerinfo.Com, Inc. Display screen or portion thereof with graphical user interface
US9406085B1 (en) 2013-03-14 2016-08-02 Consumerinfo.Com, Inc. System and methods for credit dispute processing, resolution, and reporting
WO2016137443A1 (en) * 2015-02-24 2016-09-01 Hewlett Packard Enterprise Development Lp Using fuzzy inference to determine likelihood that financial account scenario is associated with illegal activity
US9443268B1 (en) 2013-08-16 2016-09-13 Consumerinfo.Com, Inc. Bill payment and reporting
US9477737B1 (en) 2013-11-20 2016-10-25 Consumerinfo.Com, Inc. Systems and user interfaces for dynamic access of multiple remote databases and synchronization of data based on user rules
US9529851B1 (en) 2013-12-02 2016-12-27 Experian Information Solutions, Inc. Server architecture for electronic data quality processing
US9536263B1 (en) 2011-10-13 2017-01-03 Consumerinfo.Com, Inc. Debt services candidate locator
US9542553B1 (en) 2011-09-16 2017-01-10 Consumerinfo.Com, Inc. Systems and methods of identity protection and management
US9558519B1 (en) 2011-04-29 2017-01-31 Consumerinfo.Com, Inc. Exposing reporting cycle information
US9576030B1 (en) 2014-05-07 2017-02-21 Consumerinfo.Com, Inc. Keeping up with the joneses
US9595051B2 (en) 2009-05-11 2017-03-14 Experian Marketing Solutions, Inc. Systems and methods for providing anonymized user profile data
US9607336B1 (en) 2011-06-16 2017-03-28 Consumerinfo.Com, Inc. Providing credit inquiry alerts
US9633322B1 (en) 2013-03-15 2017-04-25 Consumerinfo.Com, Inc. Adjustment of knowledge-based authentication
US9654541B1 (en) 2012-11-12 2017-05-16 Consumerinfo.Com, Inc. Aggregating user web browsing data
US9652802B1 (en) 2010-03-24 2017-05-16 Consumerinfo.Com, Inc. Indirect monitoring and reporting of a user's credit data
US9679426B1 (en) 2016-01-04 2017-06-13 Bank Of America Corporation Malfeasance detection based on identification of device signature
US9690820B1 (en) 2007-09-27 2017-06-27 Experian Information Solutions, Inc. Database system for triggering event notifications based on updates to database records
US9697263B1 (en) 2013-03-04 2017-07-04 Experian Information Solutions, Inc. Consumer data request fulfillment system
US20170195436A1 (en) * 2015-12-30 2017-07-06 Paypal, Inc. Trust score determination using peer-to-peer interactions
US9710852B1 (en) 2002-05-30 2017-07-18 Consumerinfo.Com, Inc. Credit report timeline user interface
US9721147B1 (en) 2013-05-23 2017-08-01 Consumerinfo.Com, Inc. Digital identity
US9830646B1 (en) 2012-11-30 2017-11-28 Consumerinfo.Com, Inc. Credit score goals and alerts systems and methods
US9853959B1 (en) 2012-05-07 2017-12-26 Consumerinfo.Com, Inc. Storage and maintenance of personal data
US9870589B1 (en) 2013-03-14 2018-01-16 Consumerinfo.Com, Inc. Credit utilization tracking and reporting

Families Citing this family (89)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20030222138A1 (en) * 2002-05-31 2003-12-04 Carole Oppenlander System and method for authorizing transactions
US8930261B2 (en) * 2005-04-21 2015-01-06 Verint Americas Inc. Method and system for generating a fraud risk score using telephony channel based audio and non-audio data
US8510215B2 (en) * 2005-04-21 2013-08-13 Victrio, Inc. Method and system for enrolling a voiceprint in a fraudster database
US20060248019A1 (en) * 2005-04-21 2006-11-02 Anthony Rajakumar Method and system to detect fraud using voice data
US9113001B2 (en) 2005-04-21 2015-08-18 Verint Americas Inc. Systems, methods, and media for disambiguating call data to determine fraud
US8793131B2 (en) 2005-04-21 2014-07-29 Verint Americas Inc. Systems, methods, and media for determining fraud patterns and creating fraud behavioral models
US8073691B2 (en) * 2005-04-21 2011-12-06 Victrio, Inc. Method and system for screening using voice data and metadata
US20120053939A9 (en) * 2005-04-21 2012-03-01 Victrio Speaker verification-based fraud system for combined automated risk score with agent review and associated user interface
US9571652B1 (en) 2005-04-21 2017-02-14 Verint Americas Inc. Enhanced diarization systems, media and methods of use
US8924285B2 (en) * 2005-04-21 2014-12-30 Verint Americas Inc. Building whitelists comprising voiceprints not associated with fraud and screening calls using a combination of a whitelist and blacklist
US8903859B2 (en) 2005-04-21 2014-12-02 Verint Americas Inc. Systems, methods, and media for generating hierarchical fused risk scores
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
US20080221971A1 (en) * 2005-10-24 2008-09-11 Megdal Myles G Using commercial share of wallet to rate business prospects
US20080228540A1 (en) * 2005-10-24 2008-09-18 Megdal Myles G Using commercial share of wallet to compile marketing company lists
US20080228541A1 (en) * 2005-10-24 2008-09-18 Megdal Myles G Using commercial share of wallet in private equity investments
US20080243680A1 (en) * 2005-10-24 2008-10-02 Megdal Myles G Method and apparatus for rating asset-backed securities
US20080221973A1 (en) * 2005-10-24 2008-09-11 Megdal Myles G Using commercial share of wallet to rate investments
US8346638B2 (en) * 2005-10-26 2013-01-01 Capital One Financial Corporation Systems and methods for processing transaction data to perform a merchant chargeback
US8280805B1 (en) 2006-01-10 2012-10-02 Sas Institute Inc. Computer-implemented risk evaluation systems and methods
US20070203826A1 (en) * 2006-02-15 2007-08-30 Russell Thomas A Fraud early warning system and method
US8567669B2 (en) * 2006-02-24 2013-10-29 Fair Isaac Corporation Method and apparatus for a merchant profile builder
US7912773B1 (en) * 2006-03-24 2011-03-22 Sas Institute Inc. Computer-implemented data storage systems and methods for use with predictive model systems
US20070282617A1 (en) * 2006-05-30 2007-12-06 The Mitre Corporation System, method, and computer program product for forensic auditing
US7657569B1 (en) 2006-11-28 2010-02-02 Lower My Bills, Inc. System and method of removing duplicate leads
US8787633B2 (en) * 2007-01-16 2014-07-22 Purdue Research Foundation System and method of organism identification
US20090018940A1 (en) * 2007-03-30 2009-01-15 Liang Wang Enhanced Fraud Detection With Terminal Transaction-Sequence Processing
US8725597B2 (en) * 2007-04-25 2014-05-13 Google Inc. Merchant scoring system and transactional database
US20090089190A1 (en) * 2007-09-27 2009-04-02 Girulat Jr Rollin M Systems and methods for monitoring financial activities of consumers
US8666841B1 (en) 2007-10-09 2014-03-04 Convergys Information Management Group, Inc. Fraud detection engine and method of using the same
US20090204470A1 (en) * 2008-02-11 2009-08-13 Clearshift Corporation Multilevel Assignment of Jobs and Tasks in Online Work Management System
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
US8041597B2 (en) * 2008-08-08 2011-10-18 Fair Isaac Corporation Self-calibrating outlier model and adaptive cascade model for fraud detection
US8805836B2 (en) * 2008-08-29 2014-08-12 Fair Isaac Corporation Fuzzy tagging method and apparatus
US20100174638A1 (en) * 2009-01-06 2010-07-08 ConsumerInfo.com Report existence monitoring
US8762239B2 (en) * 2009-01-12 2014-06-24 Visa U.S.A. Inc. Non-financial transactions in a financial transaction network
US20100287093A1 (en) * 2009-05-07 2010-11-11 Haijian He System and Method for Collections on Delinquent Financial Accounts
US8924279B2 (en) * 2009-05-07 2014-12-30 Visa U.S.A. Inc. Risk assessment rule set application for fraud prevention
US8204833B2 (en) * 2009-05-27 2012-06-19 Softroute Corporation Method for fingerprinting and identifying internet users
US8600873B2 (en) * 2009-05-28 2013-12-03 Visa International Service Association Managed real-time transaction fraud analysis and decisioning
US20100306029A1 (en) * 2009-06-01 2010-12-02 Ryan Jolley Cardholder Clusters
US20110004498A1 (en) * 2009-07-01 2011-01-06 International Business Machines Corporation Method and System for Identification By A Cardholder of Credit Card Fraud
US20110016041A1 (en) * 2009-07-14 2011-01-20 Scragg Ernest M Triggering Fraud Rules for Financial Transactions
US20110016052A1 (en) * 2009-07-16 2011-01-20 Scragg Ernest M Event Tracking and Velocity Fraud Rules for Financial Transactions
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
US20110022517A1 (en) * 2009-07-22 2011-01-27 Ayman Hammad Apparatus including data bearing medium for authorizing a payment transaction using seasoned data
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
US8924438B2 (en) * 2009-11-12 2014-12-30 Verizon Patent And Licensing Inc. Usage record enhancement and analysis
US20110225076A1 (en) * 2010-03-09 2011-09-15 Google Inc. Method and system for detecting fraudulent internet merchants
US20130117278A1 (en) * 2010-03-12 2013-05-09 David Martens Methods, computer-accessible medium and systems for construction of and interference with networked data, for example, in a financial setting
US8626663B2 (en) * 2010-03-23 2014-01-07 Visa International Service Association Merchant fraud risk score
US8725613B1 (en) 2010-04-27 2014-05-13 Experian Information Solutions, Inc. Systems and methods for early account score and notification
US8660954B2 (en) * 2010-05-03 2014-02-25 Fundacao CPQD—Centro de Pesquisa E Desenvolvimento em Telecommuncacoes Fraud and events integrated management method and system
US8296225B2 (en) * 2010-05-20 2012-10-23 Fair Isaac Corporation Time-efficient and deterministic adaptive score calibration techniques for maintaining a predefined score distribution
US20120158586A1 (en) * 2010-12-16 2012-06-21 Verizon Patent And Licensing, Inc. Aggregating transaction information to detect fraud
CA2830797A1 (en) * 2011-03-23 2012-09-27 Detica Patent Limited An automated fraud detection method and system
WO2013082190A1 (en) * 2011-11-28 2013-06-06 Visa International Service Association Transaction security graduated seasoning and risk shifting apparatuses, methods and systems
WO2012142131A3 (en) 2011-04-11 2013-01-17 Visa International Service Association Interoperable financial transactions via mobile devices
US20120278249A1 (en) * 2011-04-29 2012-11-01 American Express Travel Related Services Company, Inc. Generating an Identity Theft Score
US9514167B2 (en) * 2011-08-01 2016-12-06 Qatar Foundation Behavior based record linkage
US8639757B1 (en) 2011-08-12 2014-01-28 Sprint Communications Company L.P. User localization using friend location information
US8478688B1 (en) * 2011-12-19 2013-07-02 Emc Corporation Rapid transaction processing
US8396935B1 (en) 2012-04-10 2013-03-12 Google Inc. Discovering spam merchants using product feed similarity
US8856923B1 (en) * 2012-06-29 2014-10-07 Emc Corporation Similarity-based fraud detection in adaptive authentication systems
US9424612B1 (en) 2012-08-02 2016-08-23 Facebook, Inc. Systems and methods for managing user reputations in social networking systems
US9368116B2 (en) 2012-09-07 2016-06-14 Verint Systems Ltd. Speaker separation in diarization
EP2946355A1 (en) 2013-01-21 2015-11-25 Features Analytics SA System and method for characterizing financial messages
US20140214504A1 (en) * 2013-01-31 2014-07-31 Sony Corporation Virtual meeting lobby for waiting for online event
CN103209174B (en) * 2013-03-12 2016-03-30 华为技术有限公司 A data protection method, apparatus and system for
US8868486B2 (en) 2013-03-15 2014-10-21 Palantir Technologies Inc. Time-sensitive cube
US9811830B2 (en) 2013-07-03 2017-11-07 Google Inc. Method, medium, and system for online fraud prevention based on user physical location data
US9460722B2 (en) 2013-07-17 2016-10-04 Verint Systems Ltd. Blind diarization of recorded calls with arbitrary number of speakers
US9747419B2 (en) 2013-12-18 2017-08-29 Mastercard International Incorporated Privacy-compliant analysis of health by transaction data
US20150178825A1 (en) * 2013-12-23 2015-06-25 Citibank, N.A. Methods and Apparatus for Quantitative Assessment of Behavior in Financial Entities and Transactions
US20150269346A1 (en) * 2014-03-24 2015-09-24 Mastercard International Incorporated Mining transaction data for healthiness index
US9535974B1 (en) 2014-06-30 2017-01-03 Palantir Technologies Inc. Systems and methods for identifying key phrase clusters within documents
US9256664B2 (en) 2014-07-03 2016-02-09 Palantir Technologies Inc. System and method for news events detection and visualization
US20150039512A1 (en) * 2014-08-08 2015-02-05 Brighterion, Inc. Real-time cross-channel fraud protection
US9280661B2 (en) 2014-08-08 2016-03-08 Brighterion, Inc. System administrator behavior analysis
US9779407B2 (en) 2014-08-08 2017-10-03 Brighterion, Inc. Healthcare fraud preemption
US9043894B1 (en) 2014-11-06 2015-05-26 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
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
US20160196615A1 (en) * 2015-01-06 2016-07-07 Wells Fargo Bank, N.A. Cross-channel fraud detection
US9875742B2 (en) 2015-01-26 2018-01-23 Verint Systems Ltd. Word-level blind diarization of recorded calls with arbitrary number of speakers
US9665460B2 (en) 2015-05-26 2017-05-30 Microsoft Technology Licensing, Llc Detection of abnormal resource usage in a data center
US9392008B1 (en) 2015-07-23 2016-07-12 Palantir Technologies Inc. Systems and methods for identifying information related to payment card breaches
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
US9456000B1 (en) 2015-08-06 2016-09-27 Palantir Technologies Inc. Systems, methods, user interfaces, and computer-readable media for investigating potential malicious communications

Citations (15)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US3603143A (en) * 1968-05-25 1971-09-07 Licentia Gmbh Apparatus for testing the vane anchorage of turbine vanes
US3690160A (en) * 1969-04-09 1972-09-12 Licentia Gmbh Apparatus for testing the blade anchorage of turbine blades
US5819226A (en) * 1992-09-08 1998-10-06 Hnc Software Inc. Fraud detection using predictive modeling
US6134532A (en) * 1997-11-14 2000-10-17 Aptex Software, Inc. System and method for optimal adaptive matching of users to most relevant entity and information in real-time
US6250166B1 (en) * 1999-06-04 2001-06-26 General Electric Company Simulated dovetail testing
US6430539B1 (en) * 1999-05-06 2002-08-06 Hnc Software Predictive modeling of consumer financial behavior
US20020133371A1 (en) * 2001-01-24 2002-09-19 Cole James A. Automated mortgage fraud prevention method and system
US20020133721A1 (en) * 2001-03-15 2002-09-19 Akli Adjaoute Systems and methods for dynamic detection and prevention of electronic fraud and network intrusion
US20020194119A1 (en) * 2001-05-30 2002-12-19 William Wright Method and apparatus for evaluating fraud risk in an electronic commerce transaction
US20030093366A1 (en) * 2001-11-13 2003-05-15 Halper Steven C. Automated loan risk assessment system and method
US6728695B1 (en) * 2000-05-26 2004-04-27 Burning Glass Technologies, Llc Method and apparatus for making predictions about entities represented in documents
US20050108025A1 (en) * 2003-11-14 2005-05-19 First American Real Estate Solutions, L.P. Method for mortgage fraud detection
US20050252304A1 (en) * 2004-05-17 2005-11-17 Woodward Colin J Apparatus and method for fatigue testing
US20050276401A1 (en) * 2003-11-05 2005-12-15 Madill Robert P Jr Systems and methods for assessing the potential for fraud in business transactions
US7933762B2 (en) * 2004-04-16 2011-04-26 Fortelligent, Inc. Predictive model generation

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4734564A (en) 1985-05-02 1988-03-29 Visa International Service Association Transaction system with off-line risk assessment
US5058179A (en) 1990-01-31 1991-10-15 At&T Bell Laboratories Hierarchical constrained automatic learning network for character recognition
US5177342A (en) 1990-11-09 1993-01-05 Visa International Service Association Transaction approval system
US7263506B2 (en) * 2000-04-06 2007-08-28 Fair Isaac Corporation Identification and management of fraudulent credit/debit card purchases at merchant ecommerce sites
EP1450321A1 (en) 2003-02-21 2004-08-25 Swisscom Mobile AG Method and system for detecting possible fraud in paying transactions

Patent Citations (18)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US3603143A (en) * 1968-05-25 1971-09-07 Licentia Gmbh Apparatus for testing the vane anchorage of turbine vanes
US3690160A (en) * 1969-04-09 1972-09-12 Licentia Gmbh Apparatus for testing the blade anchorage of turbine blades
US5819226A (en) * 1992-09-08 1998-10-06 Hnc Software Inc. Fraud detection using predictive modeling
US6330546B1 (en) * 1992-09-08 2001-12-11 Hnc Software, Inc. Risk determination and management using predictive modeling and transaction profiles for individual transacting entities
US6134532A (en) * 1997-11-14 2000-10-17 Aptex Software, Inc. System and method for optimal adaptive matching of users to most relevant entity and information in real-time
US6839682B1 (en) * 1999-05-06 2005-01-04 Fair Isaac Corporation Predictive modeling of consumer financial behavior using supervised segmentation and nearest-neighbor matching
US7165037B2 (en) * 1999-05-06 2007-01-16 Fair Isaac Corporation Predictive modeling of consumer financial behavior using supervised segmentation and nearest-neighbor matching
US6430539B1 (en) * 1999-05-06 2002-08-06 Hnc Software Predictive modeling of consumer financial behavior
US6250166B1 (en) * 1999-06-04 2001-06-26 General Electric Company Simulated dovetail testing
US6728695B1 (en) * 2000-05-26 2004-04-27 Burning Glass Technologies, Llc Method and apparatus for making predictions about entities represented in documents
US20020133371A1 (en) * 2001-01-24 2002-09-19 Cole James A. Automated mortgage fraud prevention method and system
US20020133721A1 (en) * 2001-03-15 2002-09-19 Akli Adjaoute Systems and methods for dynamic detection and prevention of electronic fraud and network intrusion
US20020194119A1 (en) * 2001-05-30 2002-12-19 William Wright Method and apparatus for evaluating fraud risk in an electronic commerce transaction
US20030093366A1 (en) * 2001-11-13 2003-05-15 Halper Steven C. Automated loan risk assessment system and method
US20050276401A1 (en) * 2003-11-05 2005-12-15 Madill Robert P Jr Systems and methods for assessing the potential for fraud in business transactions
US20050108025A1 (en) * 2003-11-14 2005-05-19 First American Real Estate Solutions, L.P. Method for mortgage fraud detection
US7933762B2 (en) * 2004-04-16 2011-04-26 Fortelligent, Inc. Predictive model generation
US20050252304A1 (en) * 2004-05-17 2005-11-17 Woodward Colin J Apparatus and method for fatigue testing

Cited By (84)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9710852B1 (en) 2002-05-30 2017-07-18 Consumerinfo.Com, Inc. Credit report timeline user interface
US9400589B1 (en) 2002-05-30 2016-07-26 Consumerinfo.Com, Inc. Circular rotational interface for display of consumer credit information
US9058627B1 (en) 2002-05-30 2015-06-16 Consumerinfo.Com, Inc. Circular rotational interface for display of consumer credit information
US8781953B2 (en) 2003-03-21 2014-07-15 Consumerinfo.Com, Inc. Card management system and method
US20100250469A1 (en) * 2005-10-24 2010-09-30 Megdal Myles G Computer-Based Modeling of Spending Behaviors of Entities
US20100106629A1 (en) * 2006-06-13 2010-04-29 First American Real Estate Tax Service, Llc. Automatic delinquency item processing with customization for lenders
US8224745B2 (en) 2006-06-13 2012-07-17 Corelogic Tax Services, Llc Automatic delinquency item processing with customization for lenders
US8626646B2 (en) 2006-10-05 2014-01-07 Experian Information Solutions, Inc. System and method for generating a finance attribute from tradeline data
US9563916B1 (en) 2006-10-05 2017-02-07 Experian Information Solutions, Inc. System and method for generating a finance attribute from tradeline data
US8036979B1 (en) 2006-10-05 2011-10-11 Experian Information Solutions, Inc. System and method for generating a finance attribute from tradeline data
US8315943B2 (en) 2006-10-05 2012-11-20 Experian Information Solutions, Inc. System and method for generating a finance attribute from tradeline data
US8214262B1 (en) 2006-12-04 2012-07-03 Lower My Bills, Inc. System and method of enhancing leads
US8606626B1 (en) 2007-01-31 2013-12-10 Experian Information Solutions, Inc. Systems and methods for providing a direct marketing campaign planning environment
US9508092B1 (en) 2007-01-31 2016-11-29 Experian Information Solutions, Inc. Systems and methods for providing a direct marketing campaign planning environment
US9342783B1 (en) 2007-03-30 2016-05-17 Consumerinfo.Com, Inc. Systems and methods for data verification
US7975299B1 (en) 2007-04-05 2011-07-05 Consumerinfo.Com, Inc. Child identity monitor
US8024264B2 (en) 2007-04-12 2011-09-20 Experian Marketing Solutions, Inc. Systems and methods for determining thin-file records and determining thin-file risk levels
US8271378B2 (en) 2007-04-12 2012-09-18 Experian Marketing Solutions, Inc. Systems and methods for determining thin-file records and determining thin-file risk levels
US8738515B2 (en) 2007-04-12 2014-05-27 Experian Marketing Solutions, Inc. Systems and methods for determining thin-file records and determining thin-file risk levels
US8364588B2 (en) 2007-05-25 2013-01-29 Experian Information Solutions, Inc. System and method for automated detection of never-pay data sets
US9251541B2 (en) 2007-05-25 2016-02-02 Experian Information Solutions, Inc. System and method for automated detection of never-pay data sets
US8612340B1 (en) * 2007-07-27 2013-12-17 Wells Fargo Bank, N.A. System and method for detecting account compromises
US8600872B1 (en) * 2007-07-27 2013-12-03 Wells Fargo Bank, N.A. System and method for detecting account compromises
US8301574B2 (en) 2007-09-17 2012-10-30 Experian Marketing Solutions, Inc. Multimedia engagement study
US9690820B1 (en) 2007-09-27 2017-06-27 Experian Information Solutions, Inc. Database system for triggering event notifications based on updates to database records
US8533322B2 (en) 2007-11-19 2013-09-10 Experian Marketing Solutions, Inc. Service for associating network users with profiles
US7996521B2 (en) 2007-11-19 2011-08-09 Experian Marketing Solutions, Inc. Service for mapping IP addresses to user segments
US9058340B1 (en) 2007-11-19 2015-06-16 Experian Marketing Solutions, Inc. Service for associating network users with profiles
US20090129377A1 (en) * 2007-11-19 2009-05-21 Simon Chamberlain Service for mapping ip addresses to user segments
US9542682B1 (en) 2007-12-14 2017-01-10 Consumerinfo.Com, Inc. Card registry systems and methods
US9767513B1 (en) 2007-12-14 2017-09-19 Consumerinfo.Com, Inc. Card registry systems and methods
US9230283B1 (en) 2007-12-14 2016-01-05 Consumerinfo.Com, Inc. Card registry systems and methods
US8706587B1 (en) * 2008-02-28 2014-04-22 Bank Of America Corporation Statistical prioritization and detection of potential financial crime events
US8355967B2 (en) 2008-06-18 2013-01-15 Consumerinfo.Com, Inc. Personal finance integration system and method
US8001042B1 (en) * 2008-07-23 2011-08-16 Experian Information Solutions, Inc. Systems and methods for detecting bust out fraud using credit data
US20120158574A1 (en) * 2008-07-23 2012-06-21 Experian Information Solutions, Inc. Systems and methods for detecting bust out fraud using credit data
US7991689B1 (en) * 2008-07-23 2011-08-02 Experian Information Solutions, Inc. Systems and methods for detecting bust out fraud using credit data
US9792648B1 (en) 2008-08-14 2017-10-17 Experian Information Solutions, Inc. Multi-bureau credit file freeze and unfreeze
US9489694B2 (en) 2008-08-14 2016-11-08 Experian Information Solutions, Inc. Multi-bureau credit file freeze and unfreeze
US9256904B1 (en) 2008-08-14 2016-02-09 Experian Information Solutions, Inc. Multi-bureau credit file freeze and unfreeze
US9595051B2 (en) 2009-05-11 2017-03-14 Experian Marketing Solutions, Inc. Systems and methods for providing anonymized user profile data
US9652802B1 (en) 2010-03-24 2017-05-16 Consumerinfo.Com, Inc. Indirect monitoring and reporting of a user's credit data
US8458074B2 (en) 2010-04-30 2013-06-04 Corelogic Solutions, Llc. Data analytics models for loan treatment
US8775300B2 (en) 2010-04-30 2014-07-08 Corelogic Solutions, Llc Data analytics models for loan treatment
US8931058B2 (en) 2010-07-01 2015-01-06 Experian Information Solutions, Inc. Systems and methods for permission arbitrated transaction services
US8666861B2 (en) * 2010-10-21 2014-03-04 Visa International Service Association Software and methods for risk and fraud mitigation
US20120101930A1 (en) * 2010-10-21 2012-04-26 Caiwei Li Software and Methods for Risk and Fraud Mitigation
WO2012058066A1 (en) * 2010-10-29 2012-05-03 Q2 Software, Inc. System, method and computer program product for real-time online transaction risk and fraud analytics and management
US20120109821A1 (en) * 2010-10-29 2012-05-03 Jesse Barbour System, method and computer program product for real-time online transaction risk and fraud analytics and management
US9684905B1 (en) 2010-11-22 2017-06-20 Experian Information Solutions, Inc. Systems and methods for data verification
US9147042B1 (en) 2010-11-22 2015-09-29 Experian Information Solutions, Inc. Systems and methods for data verification
US8458069B2 (en) 2011-03-04 2013-06-04 Brighterion, Inc. Systems and methods for adaptive identification of sources of fraud
WO2012121983A3 (en) * 2011-03-04 2012-12-06 Brighterion, Inc. Systems and methods for adaptive identification of sources of fraud
US9558519B1 (en) 2011-04-29 2017-01-31 Consumerinfo.Com, Inc. Exposing reporting cycle information
US9607336B1 (en) 2011-06-16 2017-03-28 Consumerinfo.Com, Inc. Providing credit inquiry alerts
US9665854B1 (en) 2011-06-16 2017-05-30 Consumerinfo.Com, Inc. Authentication alerts
US9542553B1 (en) 2011-09-16 2017-01-10 Consumerinfo.Com, Inc. Systems and methods of identity protection and management
US9536263B1 (en) 2011-10-13 2017-01-03 Consumerinfo.Com, Inc. Debt services candidate locator
US9853959B1 (en) 2012-05-07 2017-12-26 Consumerinfo.Com, Inc. Storage and maintenance of personal data
US8914317B2 (en) 2012-06-28 2014-12-16 International Business Machines Corporation Detecting anomalies in real-time in multiple time series data with automated thresholding
US8924333B2 (en) 2012-06-28 2014-12-30 International Business Machines Corporation Detecting anomalies in real-time in multiple time series data with automated thresholding
US9336494B1 (en) * 2012-08-20 2016-05-10 Context Relevant, Inc. Re-training a machine learning model
US9654541B1 (en) 2012-11-12 2017-05-16 Consumerinfo.Com, Inc. Aggregating user web browsing data
US9830646B1 (en) 2012-11-30 2017-11-28 Consumerinfo.Com, Inc. Credit score goals and alerts systems and methods
US20140214669A1 (en) * 2013-01-29 2014-07-31 Gravic, Inc. Methods for Reducing the Merchant Chargeback Notification Time
US9697263B1 (en) 2013-03-04 2017-07-04 Experian Information Solutions, Inc. Consumer data request fulfillment system
US9406085B1 (en) 2013-03-14 2016-08-02 Consumerinfo.Com, Inc. System and methods for credit dispute processing, resolution, and reporting
US9697568B1 (en) 2013-03-14 2017-07-04 Consumerinfo.Com, Inc. System and methods for credit dispute processing, resolution, and reporting
US9870589B1 (en) 2013-03-14 2018-01-16 Consumerinfo.Com, Inc. Credit utilization tracking and reporting
US9633322B1 (en) 2013-03-15 2017-04-25 Consumerinfo.Com, Inc. Adjustment of knowledge-based authentication
US9747644B2 (en) 2013-03-15 2017-08-29 Mastercard International Incorporated Transaction-history driven counterfeit fraud risk management solution
WO2014152419A1 (en) * 2013-03-15 2014-09-25 Mastercard International Incorporated Transaction-history driven counterfeit fraud risk management solution
US9721147B1 (en) 2013-05-23 2017-08-01 Consumerinfo.Com, Inc. Digital identity
US9443268B1 (en) 2013-08-16 2016-09-13 Consumerinfo.Com, Inc. Bill payment and reporting
US9477737B1 (en) 2013-11-20 2016-10-25 Consumerinfo.Com, Inc. Systems and user interfaces for dynamic access of multiple remote databases and synchronization of data based on user rules
US9529851B1 (en) 2013-12-02 2016-12-27 Experian Information Solutions, Inc. Server architecture for electronic data quality processing
US20150262184A1 (en) * 2014-03-12 2015-09-17 Microsoft Corporation Two stage risk model building and evaluation
USD760256S1 (en) 2014-03-25 2016-06-28 Consumerinfo.Com, Inc. Display screen or portion thereof with graphical user interface
USD759690S1 (en) 2014-03-25 2016-06-21 Consumerinfo.Com, Inc. Display screen or portion thereof with graphical user interface
USD759689S1 (en) 2014-03-25 2016-06-21 Consumerinfo.Com, Inc. Display screen or portion thereof with graphical user interface
US9576030B1 (en) 2014-05-07 2017-02-21 Consumerinfo.Com, Inc. Keeping up with the joneses
WO2016137443A1 (en) * 2015-02-24 2016-09-01 Hewlett Packard Enterprise Development Lp Using fuzzy inference to determine likelihood that financial account scenario is associated with illegal activity
US20170195436A1 (en) * 2015-12-30 2017-07-06 Paypal, Inc. Trust score determination using peer-to-peer interactions
US9679426B1 (en) 2016-01-04 2017-06-13 Bank Of America Corporation Malfeasance detection based on identification of device signature

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