US20140081705A1 - Industry size of wallet - Google Patents

Industry size of wallet Download PDF

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US20140081705A1
US20140081705A1 US14085522 US201314085522A US2014081705A1 US 20140081705 A1 US20140081705 A1 US 20140081705A1 US 14085522 US14085522 US 14085522 US 201314085522 A US201314085522 A US 201314085522A US 2014081705 A1 US2014081705 A1 US 2014081705A1
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wallet
consumer
industry
size
spend
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US14085522
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Prashant Kalia
Karlyn Heiner Crotty
Iwao Fusillo
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American Express Travel Related Services Co Inc
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American Express Travel Related Services Co Inc
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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
    • G06Q30/00Commerce, e.g. shopping or e-commerce
    • G06Q30/02Marketing, e.g. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards; Price estimation or determination
    • G06Q30/0202Market predictions or demand forecasting
    • 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
    • G06Q10/063Operations research or analysis
    • G06Q10/0637Strategic management or analysis
    • G06Q10/06375Prediction of business process outcome or impact based on a proposed change
    • 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
    • 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
    • G06Q30/02Marketing, e.g. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards; Price estimation or determination
    • G06Q30/0201Market data gathering, market analysis or market modelling
    • 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
    • G06Q30/02Marketing, e.g. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards; Price estimation or determination
    • G06Q30/0202Market predictions or demand forecasting
    • G06Q30/0204Market segmentation
    • G06Q30/0205Location or geographical consideration
    • 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

Abstract

Consumer spend by industry is modeled based on the industry sizes of wallet of consumers having a high share of wallet with a financial institution. A size of wallet is calculated for each consumer in a plurality of consumers. A share of wallet for each consumer is also calculated. A subset of the plurality of consumers whose share of wallet is above a given percentage of their size of wallet is then determined. For each consumer in the subset, an industry size of wallet is determined. A correlation between the industry size of wallet of a given consumer and one or more characteristics of the given consumer is then derived using the industry size of wallet for the consumers in the subset.

Description

    CROSS-REFERENCE TO RELATED APPLICATIONS
  • [0001]
    This application is a continuation of, claims priority to and the benefit of, U.S. patent application Ser. No. 13/776,178 filed Feb. 25, 2013 and entitled INDUSTRY SIZE OF WALLET.” The '178 application is a continuation of, claims priority to and the benefit of, U.S. patent application Ser. No. 13/538,936, filed Jun. 29, 2012 and entitled “INDUSTRY SIZE OF WALLET,” and issued as U.S. Pat. No. 8,401,947 on Mar. 19, 2013. The '936 application is a continuation of, claims priority to and the benefit of, U.S. patent application Ser. No. 11/608,179 filed Dec. 7, 2006 and entitled “INDUSTRY SIZE OF WALLET,” and issued as U.S. Pat. No. 8,239,250 on Aug. 7, 2012. The '179 application claims the benefit of U.S. Provisional Patent Application No. 60/868,229, filed Dec. 1, 2006 and entitled “INDUSTRY SIZE OF WALLET.” All of which are incorporated by reference herein in their entirety.
  • BACKGROUND OF THE INVENTION
  • [0002]
    1. Field of the Invention
  • [0003]
    The present application relates to financial data processing, in particular customer modeling and behavioral analysis.
  • [0004]
    2. Background Art
  • [0005]
    It is axiomatic that consumers will tend to spend more when they have greater purchasing power. The capability to accurately estimate a consumer's spend capacity could therefore allow a financial institution (such as a credit company, lender or any consumer services company) to better target potential prospects and identify any opportunities to increase consumer transaction volumes, without an undue increase in the risk of defaults. Consumers will be most attracted to products that are customized specifically for their individual interests and spending patterns. Attracting additional consumer spending in this manner, in turn, would increase such financial institution's revenues, primarily in the form of an increase in transaction fees and interest payments received. Consequently, a consumer model that can accurately estimate purchasing power and identify industries in which the consumer is most interested in spending is of paramount interest to many financial institutions and other consumer services companies.
  • [0006]
    A limited ability to estimate consumer spend behavior from point-in-time credit data has previously been available. A financial institution can, for example, simply monitor the balances of its own customers' accounts. When a credit balance is lowered, the financial institution could then assume that the corresponding consumer now has greater purchasing power. Such an assumption has its flaws, however. For example, it is oftentimes difficult to confirm whether the lowered balance is the result of a balance transfer to another account. Such balance transfers represent no increase in the consumer's capacity to spend, and so this simple model of consumer behavior has its flaws.
  • [0007]
    In order to achieve a complete picture of any consumer's purchasing ability and interests, one must examine in detail the full range of a consumer's financial accounts, including credit accounts, checking and savings accounts, investment portfolios, and the like. However, the vast majority of consumers do not maintain all such accounts with the same financial institution and the access to detailed financial information from other financial institutions is restricted by consumer privacy laws, disclosure policies and security concerns.
  • [0008]
    There is limited and incomplete consumer information from credit bureaus and the like at the aggregate and individual consumer levels. Since balance transfers are nearly impossible to consistently identify from the face of such records, this information has not previously been enough to obtain accurate estimates of a consumer's actual spending ability.
  • [0009]
    Accordingly, there is a need for a method and apparatus for determining a customer's size of wallet along with specific industries in which the customer is most likely to spend which addresses certain problems of existing technologies.
  • SUMMARY OF THE INVENTION
  • [0010]
    In one embodiment of the present invention, consumer spend by industry is modeled based on the industry sizes of wallet of consumers having a high share of wallet with a financial institution. A size of wallet is calculated for each consumer in a plurality of consumers. A share of wallet for each consumer is also calculated. A subset of the plurality of consumers whose share of wallet is above a given percentage of their size of wallet is then determined. For each consumer in the subset, an industry size of wallet is determined. A correlation between the industry size of wallet of a given consumer and one or more characteristics of the given consumer is then derived using the industry size of wallet for the consumers in the subset.
  • [0011]
    In another embodiment of the present invention, a customer can be targeted with an offer to increase the customer's industry share of wallet associated with a given financial institution. To do this, an industry size of wallet is estimated for one or more consumers. The external size of the industry size of wallet of each consumer is calculated, and one or more consumers having a relatively high external size of the industry wallet (that is, potential) and a reasonably high total share of wallet with the financial institution (that is, engagement with the financial institution) are targeted with offers to increase their industry share of wallet associated with the financial institution.
  • BRIEF DESCRIPTION OF THE DRAWINGS/FIGURES
  • [0012]
    The accompanying drawings, which are incorporated herein and form a part of the specification, illustrate the present invention and, together with the description, further serve to explain the principles of the invention and to enable a person skilled in the pertinent art to make and use the invention.
  • [0013]
    FIG. 1 is a flowchart of an exemplary process for creating an industry size of wallet model.
  • [0014]
    FIG. 2 is a flowchart of an exemplary process for targeting a consumer with an offer to increase spending.
  • [0015]
    FIG. 3 is a graph of average travel size of wallet by residence location.
  • [0016]
    FIG. 4 is a graph of average restaurant size of wallet by residence location.
  • [0017]
    FIG. 5 is graph of average industry size of wallet relative to a consumer's total size of wallet.
  • [0018]
    FIG. 6 is a graph of average industry size of wallet relative to a consumer's credit bureau tenure.
  • [0019]
    FIG. 7 is a graph of average industry size of wallet relative to a consumer's gender.
  • [0020]
    FIG. 8 is a graph of average everyday spend size of wallet by number of individuals in a household.
  • [0021]
    FIG. 9 is a graph of average everyday spend size of wallet by number of active transaction cards in a household.
  • [0022]
    FIG. 10 is a graph illustrating the predictions of an exemplary size of travel wallet model against actual travel spend.
  • [0023]
    FIG. 11 is a graph illustrating the predictions of an exemplary size of restaurant wallet model against actual restaurant spend.
  • [0024]
    FIG. 12 is a graph illustrating the predictions of an exemplary size of everyday spend wallet model against actual everyday spend.
  • [0025]
    FIG. 13 is a block diagram of an exemplary computer system useful for implementing the present invention.
  • [0026]
    The present invention will be described with reference to the accompanying drawings. The drawing in which an element first appears is typically indicated by the leftmost digit(s) in the corresponding reference number.
  • DETAILED DESCRIPTION OF THE INVENTION I. Overview
  • [0027]
    While specific configurations and arrangements are discussed, it should be understood that this is done for illustrative purposes only. A person skilled in the pertinent art will recognize that other configurations and arrangements can be used without departing from the spirit and scope of the present invention. It will be apparent to a person skilled in the pertinent art that this invention can also be employed in a variety of other applications.
  • [0028]
    The terms “user,” “end user,” “consumer,” “customer,” “participant,” and/or the plural form of these terms are used interchangeably throughout herein to refer to those persons or entities capable of accessing, using, being affected by and/or benefiting from the tool that the present invention provides for determining a household size of wallet.
  • [0029]
    Furthermore, the terms “business” or “merchant” may be used interchangeably with each other and shall mean any person, entity, distributor system, software and/or hardware that is a provider, broker and/or any other entity in the distribution chain of goods or services. For example, a merchant may be a grocery store, a retail store, a travel agency, a service provider, an on-line merchant or the like.
  • 1. Transaction Accounts and Instrument
  • [0030]
    A “transaction account” as used herein refers to an account associated with an open account or a closed account system (as described below). The transaction account may exist in a physical or non-physical embodiment. For example, a transaction account may be distributed in non-physical embodiments such as an account number, frequent-flyer account, telephone calling account or the like. Furthermore, a physical embodiment of a transaction account may be distributed as a financial instrument.
  • [0031]
    A financial transaction instrument may be traditional plastic transaction cards, titanium-containing, or other metal-containing, transaction cards, clear and/or translucent transaction cards, foldable or otherwise unconventionally-sized transaction cards, radio-frequency enabled transaction cards, or other types of transaction cards, such as credit, charge, debit, pre-paid or stored-value cards, or any other like financial transaction instrument. A financial transaction instrument may also have electronic functionality provided by a network of electronic circuitry that is printed or otherwise incorporated onto or within the transaction instrument (and typically referred to as a “smart card”), or be a fob having a transponder and an RFID reader.
  • 2. Use of Transaction Accounts
  • [0032]
    With regard to use of a transaction account, users may communicate with merchants in person (e.g., at the box office), telephonically, or electronically (e.g., from a user computer via the Internet). During the interaction, the merchant may offer goods and/or services to the user. The merchant may also offer the user the option of paying for the goods and/or services using any number of available transaction accounts. Furthermore, the transaction accounts may be used by the merchant as a form of identification of the user. The merchant may have a computing unit implemented in the form of a computer-server, although other implementations are possible.
  • [0033]
    In general, transaction accounts may be used for transactions between the user and merchant through any suitable communication means, such as, for example, a telephone network, intranet, the global, public Internet, a point of interaction device (e.g., a point of sale (POS) device, personal digital assistant (PDA), mobile telephone, kiosk, etc.), online communications, off-line communications, wireless communications, and/or the like.
  • [0034]
    A transaction account has a basic user, who is the primary user associated with the account. A transaction account may also have a supplemental user who is given access to the account by the basic user. The supplemental user may possess a duplicate of the transaction instrument associated with the account.
  • 3. Account and Merchant Numbers
  • [0035]
    An “account,” “account number” or “account code”, as used herein, may include any device, code, number, letter, symbol, digital certificate, smart chip, digital signal, analog signal, biometric or other identifier/indicia suitably configured to allow a consumer to access, interact with or communicate with a financial transaction system. The account number may optionally be located on or associated with any financial transaction instrument (e.g., rewards, charge, credit, debit, prepaid, telephone, embossed, smart, magnetic stripe, bar code, transponder or radio frequency card).
  • [0036]
    Persons skilled in the relevant arts will understand the breadth of the terms used herein and that the exemplary descriptions provided are not intended to be limiting of the generally understood meanings attributed to the foregoing terms.
  • [0037]
    It is noted that references in the specification to “one embodiment”, “an embodiment”, “an example embodiment”, etc., indicate that the embodiment described may include a particular feature, structure, or characteristic, but every embodiment may not necessarily include the particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it would be within the knowledge of one skilled in the art to effect such feature, structure, or characteristic in connection with other embodiments whether or not explicitly described.
  • [0038]
    While specific configurations and arrangements are discussed, it should be understood that this is done for illustrative purposes only. A person skilled in the pertinent art will recognize that other configurations and arrangements can be used without departing from the spirit and scope of the present invention. It will be apparent to a person skilled in the pertinent art that this invention can also be employed in a variety of other applications.
  • [0039]
    As used herein, the following terms shall have the following meanings. A trade or tradeline refers to a credit or charge vehicle issued to an individual customer by a credit grantor. Types of tradelines include, for example and without limitation, bank loans, credit card accounts, retail cards, personal lines of credit and car loans/leases. For purposes here, use of the term credit card shall be construed to include charge cards except as specifically noted. Tradeline data describes the customer's account status and activity, including, for example, names of companies where the customer has accounts, dates such accounts were opened, credit limits, types of accounts, balances over a period of time and summary payment histories. Tradeline data is generally available for the vast majority of actual consumers. Tradeline data, however, does not include individual transaction data, which is largely unavailable because of consumer privacy protections. Tradeline data may be used to determine both individual and aggregated consumer spending patterns, as described herein.
  • [0040]
    Consumer panel data measures consumer spending patterns from information that is provided by, typically, millions of participating consumer panelists. Such consumer panel data is available through various consumer research companies, such as comScore Networks, Inc. of Reston, Va. Consumer panel data may typically include individual consumer information such as credit risk scores, credit card application data, credit card purchase transaction data, credit card statement views, tradeline types, balances, credit limits, purchases, balance transfers, cash advances, payments made, finance charges, annual percentage rates and fees charged. Such individual information from consumer panel data, however, is limited to those consumers who have participated in the consumer panel, and so such detailed data may not be available for all consumers.
  • [0041]
    Although the present invention is described as relating to individual consumers, one of skill in the pertinent art(s) will recognize that it can also apply to small businesses and organizations without departing from the spirit and scope of the present invention.
  • II. Industry Size of Wallet
  • [0042]
    Consumers tend to spend more when they have greater purchasing power. It is thus advantageous for a financial institution (such as a credit company, lender or any consumer services company) to target existing customers and potential customers with opportunities to increase their transaction volumes. The capability to accurately estimate a consumer's spend capacity allows the financial institution to target potential prospects and identify any opportunities to increase consumer transaction volumes, without the financial institution experiencing an undue increase in the risk of defaults.
  • [0043]
    Additionally, consumers are most attracted to products that are customized specifically for their individual interests and spending patterns. Attracting additional consumer spending in this manner, in turn, increases the financial institution's revenues, primarily in the form of an increase in transaction fees and interest payments received.
  • [0044]
    A model may be developed that correlates spending patterns of consumers based on lifestyle characteristics of those consumers. Lifestyle characteristics may include, for example and without limitation, credit bureau tenure, age, gender, disposable income, geographic location, household size, number of transaction cards in a household, size of total spending wallet, and other third party data, as will be discussed in further detail below. Once lifestyle characteristics are identified as indicators of certain spending patterns, consumers can be categorized based on their lifestyle characteristics and the correlated spending patterns.
  • A. Model Development
  • [0045]
    A model for determining consumer spending patterns using various lifestyle characteristics may be developed based on detailed analysis of a number of consumers. Such a detailed analysis may include determining the total size of wallet of the customer, as well as ascertaining one or more lifestyle characteristics of the customer. FIG. 1 is an illustration of an exemplary method 100 for modeling consumer spending patterns using various lifestyle characteristics.
  • [0046]
    In step 102, the total size of wallet is determined for a plurality of consumers. The total size of wallet is the entire amount of spend by a particular consumer from tradeline data sources over a given period of time. The total size of wallet of a consumer may be calculated based on, for example and without limitation, internal customer tradeline data and/or external tradeline data available from, for example, a credit bureau. An exemplary method of calculating the size of wallet of an individual is described in U.S. patent application Ser. No. 11/169,588, filed Jun. 30, 2005, entitled Method and Apparatus for Consumer Interaction Based on Spend Capacity, incorporated by reference herein in its entirety.
  • [0047]
    Once the size of wallet has been calculated for a plurality of consumers, method 100 proceeds to step 104. In step 104, a subset of consumers having a high share of wallet with a particular financial institution is identified. The share of wallet is the portion of the spending wallet that is captured by the particular financial institution. Consumers having a high share of wallet with the particular financial institution may be those consumers whose spend on accounts associated with the financial institution is more than, for example, 90% of their total spend. This subset of consumers is used by the financial institution in modeling consumer behavior, because the financial institution typically has access to most of the individual records of charge of the consumers and can determine industry-related spending habits of the consumers. Consumers having an extremely high share of wallet with the financial institution (e.g., the top 1% of high-share consumers) may be excluded from the modeling process, to eliminate consideration of small business spending in the modeling process.
  • [0048]
    After determining the high-share subset of consumers, method 100 proceeds to step 106. In step 106, an industry size of wallet is calculated for each consumer. Information about the consumer's spending in various industries can be obtained in a variety of ways. As mentioned previously, since most of the spending of high-share consumers is done with the financial institution, the financial institution typically has a record of the consumer's spend by industry. If such a record does not already exist, the financial institution can, for example, analyze the records of charge of each consumer in the subset of consumers to determine the industry-related spending habits of each consumer. An industry is the type of good or service purchased by the consumer. Types of industries may include industries at a macro level, for example and without limitation, the travel industry, the restaurant industry, and the entertainment industry. Types of industries may also include industries at a micro level, for example and without limitation, the airline industry, the lodging industry, and the car rental industry, each of which is a subset of a macro industry, such as the travel industry. The industry-related spending habits of a consumer include, for example and without limitation, the amount of spend in a given industry and the rate of spend in the given industry. Although the present invention will mostly be described with respect to spend in the travel industry, one of skill in the relevant art(s) will recognize that the methods and systems disclosed herein may involve spend in any other industry without departing from the spirit and scope of the present invention.
  • [0049]
    Because the subset of consumers has a high share of wallet with the financial institution, it is reasonable to assume that the spending habits identified for each consumer using the records of the financial institution are reflective of the consumer's spending habits across his or her entire spending wallet. For example, if a person has a high share of wallet with the financial institution, that person's travel spending on accounts associated with the financial institution is likely approximately equal to his or her total travel spending. The amount of industry spend by each consumer in the high-share subset of consumers is deemed to be that consumer's industry size of wallet.
  • [0050]
    Once the industry size of wallet of each consumer in the subset of consumers has been determined, method 100 proceeds to step 108. In step 108, relationships between the characteristics and an industry size of wallet are identified. To identify these relationships, the spend habits of multiple consumers are examined to ascertain characteristics of the consumers that influence or are indicative of spend in a given industry. These characteristics include, for example and without limitation, financial and demographic characteristics, and are referred to herein as lifestyle characteristics. For example, if the financial institution wants to determine what factors influence travel spending, profiles of consumers who spend a high percentage of their wallet on travel can be compared to identify common lifestyle characteristics. In another example, profiles of consumers who spend a high percentage of their wallet on travel can be compared to profiles of consumers who spend a low percentage of their wallet on travel to identify differentiating lifestyle characteristics.
  • [0051]
    Some lifestyle characteristics may have a given weight (e.g., the magnitude of their effect on industry-related spend) regardless of the actual value of the characteristic. Other lifestyle characteristics may have a graded aspect to them, such that the weight of the variable is dependent on the value of the variable. An example lifestyle characteristic whose weight on airline spend varies based on the value of the characteristic is the geographic location of the consumer's residence. FIG. 3 is chart of residential zip codes versus average travel-related spend by residents of those zip codes. FIG. 3 takes into consideration the high-share subset of consumers, and computes, for example, an average airline spend value for each available zip code. As illustrated in FIG. 3, residents of zip codes closer to airports have more travel-related spend than residents of zip codes farther away from airports. A correlation thus exists between specific zip codes and the airline industry size of wallet, and the zip codes can be ranked based on their average airline spend. In this manner, the ranking becomes a variable indicative of airline spending.
  • [0052]
    The geographic location of the consumer's residence can also influence restaurant spend, as illustrated in FIG. 4. FIG. 4 is a chart of residential zip codes versus average restaurant-related spend by residents of those zip codes. Correlations between specific zip codes and restaurant spending can thus be identified.
  • [0053]
    Other lifestyle characteristics that influence spend in various industries may include, for example and without limitation, credit bureau tenure, age, gender, disposable income, geographic location, household size, number of transaction cards in a household, size of total spending wallet, and other third party data. FIG. 5 is a chart illustrating how the total size of a consumer's wallet is indicative of travel-related spend, restaurant-related spend, and everyday spend. As illustrated, travel-related spend has the strongest correlation with the total size of the consumer's wallet. FIG. 6 is a chart illustrating how credit bureau tenure is indicative of travel-related spend and restaurant-related spend. As illustrated, travel- and restaurant-related spend are significantly lower for consumers having low tenure with the bureau, and relatively higher for high tenure consumers. FIG. 7 is a chart illustrating how gender is indicative of travel-related spend and restaurant-related spend. As illustrated, travel- and restaurant-related spend is higher for males as compared to females. FIG. 8 is a chart illustrating the relationship between household size and everyday spend. As illustrated, everyday spend varies significantly with household size. Similarly, FIG. 9 is a chart illustrating the relationship between the number of active transaction cards in a household and everyday spend. As illustrated, everyday spend varies significantly with the number of active household cards.
  • [0054]
    After the lifestyle characteristics have been identified, method 100 proceeds to step 110, in which a model to determine industry size of wallet based on lifestyle characteristics of a consumer is created. In a first embodiment, the model simply identifies a typical industry size of wallet for consumers having certain lifestyle characteristics, based on the sizes of spending wallets of analyzed consumers sharing those lifestyle characteristics. In a second embodiment, a size of wallet algorithm is identified based on the correlations between consumers having common lifestyle characteristics.
  • [0055]
    An example size of wallet algorithm for travel-related spend and restaurant related-spend is defined in Equation 1:
  • [0000]

    Total Industry SoW=A+(B*Total Size of Plastic Spend Wallet)+(C*Location Rank)+(D*Customer Tenure on Bureau)+(E*Customer Gender),
  • [0000]
    where A, B, C, D, and E are correlation factors or weights corresponding to the importance of the associated lifestyle characteristics. A, B, C, D, and E may vary depending on whether the algorithm is used to determine, for example, travel size of wallet or restaurant size of wallet. FIG. 10 is a graph illustrating the travel size of wallet values predicted for various spend levels compared to the actual travel size of wallet values for the various spend levels. FIG. 11 is a graph illustrating the restaurant size of wallet predicted for various spend levels compared to the actual restaurant size of wallet values for the various spend levels. As illustrated, this model has a high level of prediction accuracy.
  • [0056]
    Similarly, an example everyday spend size of wallet algorithm is defined in Equation 2:
  • [0000]

    Total EDS SoW=V+(W*Total Size of Plastic Spend Wallet)+(X*Number of Active Household Cards)+(Y*Location Rank)+(Z*Household Size),
  • [0000]
    where V, W, X, Y, and Z are correlation factors or weights corresponding to the importance of the associated lifestyle characteristics. FIG. 12 is a graph illustrating the everyday spend size of wallet values predicted for various spend levels compared to the actual everyday spend size of wallet values for the various spend levels.
  • [0057]
    Similar modeling approaches can also be used to incorporate interaction between industry spends into the industry size of wallet model. For example, spend in particular industries or at particular merchants may be indicative of spend in other industries or at other merchants.
  • B. Consumer Targeting
  • [0058]
    Once a lifestyle characteristic indicative of spend in a particular industry has been identified, the financial institution can target consumers having that lifestyle characteristic with incentives to increase spend related to the industry, even if those consumers have low or medium share of wallet with the financial institution. FIG. 2 is an exemplary method 200 for targeting consumers with incentives to increase industry-related spend, according to an embodiment of the present invention.
  • [0059]
    In step 202, one or more lifestyle characteristics indicative of spend in a given industry are determined. These lifestyle characteristics may be determined in accordance with a method such as method 100 described above.
  • [0060]
    After step 202, method 100 proceed to step 204. In step 204, a consumer having one or more of the determined lifestyle characteristics is identified. Since many lifestyle characteristics of a consumer are typically publicly available (such as, for example, from credit bureaus), the consumer does not need to have a high industry share of wallet with the financial institution in order to be identified by the financial institution. This method can thus be used to target an individual having a low or medium industry share of wallet with the financial institution. Since the identified consumer has a lifestyle characteristic in common with consumers who make purchases related to the given industry, the financial institution can assume, without specific knowledge of the identified consumer's industry-related spend, that the identified consumer also makes purchases related to the given industry and would be accepting of incentives to increase spend related to the given industry.
  • [0061]
    After identifying the consumer having one or more lifestyle characteristics indicative of spend in the given industry, method 200 proceeds to step 206. In step 206, the consumer is assigned an industry size of wallet based on the consumer's lifestyle characteristics. The industry size of wallet may be based on, for example, industry sizes of wallet calculated in step 110 (using, for example, Equation 1 or 2) of method 100 above.
  • [0062]
    The external size of wallet of the customer is calculated in step 210. The customer's external size of wallet may be calculated, for example, by subtracting the magnitude of the customer's industry spending associated with the financial institution from the magnitude of the customer's industry size of wallet. The remaining amount, which corresponds to spend in the industry that is not associated with the financial institution, is also referred to herein as the “external industry spend.”
  • [0063]
    Method 200 then proceeds to step 212. In step 212, the identified consumer is targeted with an offer (or promotion) that will incent the consumer to increase spend related to the given industry. The offer may vary based on, for example, the external size of wallet calculated in step 210. If multiple consumers were identified in step 204, the consumers may be prioritized based on the external industry spend assigned in step 210, with consumers having a greater external industry spend taking priority over consumers having a smaller external industry spend.
  • [0064]
    Further, priority may be given to consumers having some minimal share of total wallet with the institution. A minimal share of total wallet will ensure a certain engagement level with the financial institution which would lead to improved responses to the spend offer. The customers can thus be optimized based on their total share of wallet and the amount of external industry spend, with the financial institution targeting only the most optimal consumers.
  • [0065]
    In a first embodiment, the offer may be an offer for a new product, which will encourage new spend related to the given industry. In the example of the airline industry, a consumer who has a lifestyle characteristic indicative of spend in the airline industry may be targeted, for example, with an offer for a credit card that is co-branded between the financial institution and an airline company. In a second embodiment, the offer may be an incentive to increase spending on an existing product held by the consumer. In the example of the airline industry, a consumer who has a lifestyle characteristic indicative of spend in the airline industry and who also has a financial account associated with a rewards program managed by the financial institution may be offered double reward points for spend on airline travel.
  • [0066]
    If a consumer qualifies for multiple spend offers or incentives, the financial institution may choose to target the consumer for the industry with the highest value of spend incentive. To do this, the consumer size and/or share of wallet is calculated for each industry (using, for example, Equations 1 and 2), and the industry having the largest size and/or share of the consumer's wallet determines the targeted industry.
  • III. Example Implementations
  • [0067]
    The present invention (i.e., process 100, process 200 or any part(s) or function(s) thereof) may be implemented using hardware, software or a combination thereof and may be implemented in one or more computer systems or other processing systems. However, the manipulations performed by the present invention were often referred to in terms, such as adding or comparing, which are commonly associated with mental operations performed by a human operator. No such capability of a human operator is necessary, or desirable in most cases, in any of the operations described herein which form part of the present invention. Rather, the operations are machine operations. Useful machines for performing the operation of the present invention include general purpose digital computers or similar devices.
  • [0068]
    In fact, in one embodiment, the invention is directed toward one or more computer systems capable of carrying out the functionality described herein. An example of a computer system 1300 is shown in FIG. 13.
  • [0069]
    The computer system 1300 includes one or more processors, such as processor 1304. The processor 1304 is connected to a communication infrastructure 1306 (e.g., a communications bus, cross-over bar, or network). Various software embodiments are described in terms of this exemplary computer system. After reading this description, it will become apparent to a person skilled in the relevant art(s) how to implement the invention using other computer systems and/or architectures.
  • [0070]
    Computer system 1300 can include a display interface 1302 that forwards graphics, text, and other data from the communication infrastructure 1306 (or from a frame buffer not shown) for display on the display unit 1330.
  • [0071]
    Computer system 1300 also includes a main memory 1308, preferably random access memory (RAM), and may also include a secondary memory 1310. The secondary memory 1310 may include, for example, a hard disk drive 1312 and/or a removable storage drive 1314, representing a floppy disk drive, a magnetic tape drive, an optical disk drive, etc. The removable storage drive 1314 reads from and/or writes to a removable storage unit 1318 in a well known manner. Removable storage unit 1318 represents a floppy disk, magnetic tape, optical disk, etc. which is read by and written to by removable storage drive 1314. As will be appreciated, the removable storage unit 1318 includes a computer usable storage medium having stored therein computer software and/or data.
  • [0072]
    In alternative embodiments, secondary memory 1310 may include other similar devices for allowing computer programs or other instructions to be loaded into computer system 1300. Such devices may include, for example, a removable storage unit 1318 and an interface 1320. Examples of such may include a program cartridge and cartridge interface (such as that found in video game devices), a removable memory chip (such as an erasable programmable read only memory (EPROM), or programmable read only memory (PROM)) and associated socket, and other removable storage units 1318 and interfaces 1320, which allow software and data to be transferred from the removable storage unit 1318 to computer system 1300.
  • [0073]
    Computer system 1300 may also include a communications interface 1324. Communications interface 1324 allows software and data to be transferred between computer system 1300 and external devices. Examples of communications interface 1324 may include a modem, a network interface (such as an Ethernet card), a communications port, a Personal Computer Memory Card International Association (PCMCIA) slot and card, etc. Software and data transferred via communications interface 1324 are in the form of signals 1328 which may be electronic, electromagnetic, optical or other signals capable of being received by communications interface 1324. These signals 1328 are provided to communications interface 1324 via a communications path (e.g., channel) 1326. This channel 1326 carries signals 1328 and may be implemented using wire or cable, fiber optics, a telephone line, a cellular link, a radio frequency (RF) link and other communications channels.
  • [0074]
    In this document, the terms “computer program medium” and “computer usable medium” are used to generally refer to media such as removable storage drive 1314 and a hard disk installed in hard disk drive 1312. These computer program products provide software to computer system 1300. The invention is directed to such computer program products.
  • [0075]
    Computer programs (also referred to as computer control logic) are stored in main memory 1308 and/or secondary memory 1310. Computer programs may also be received via communications interface 1324. Such computer programs, when executed, enable the computer system 1300 to perform the features of the present invention, as discussed herein. In particular, the computer programs, when executed, enable the processor 1304 to perform the features of the present invention. Accordingly, such computer programs represent controllers of the computer system 1300.
  • [0076]
    In an embodiment where the invention is implemented using software, the software may be stored in a computer program product and loaded into computer system 1300 using removable storage drive 1314, hard drive 1312 or communications interface 1324. The control logic (software), when executed by the processor 1304, causes the processor 1304 to perform the functions of the invention as described herein.
  • [0077]
    In another embodiment, the invention is implemented primarily in hardware using, for example, hardware components such as application specific integrated circuits (ASICs). Implementation of the hardware state machine so as to perform the functions described herein will be apparent to persons skilled in the relevant art(s).
  • [0078]
    In yet another embodiment, the invention is implemented using a combination of both hardware and software.
  • IV. CONCLUSION
  • [0079]
    While various embodiments of the present invention have been described above, it should be understood that they have been presented by way of example, and not limitation. It will be apparent to persons skilled in the relevant art(s) that various changes in form and detail can be made therein without departing from the spirit and scope of the present invention. Thus, the present invention should not be limited by any of the above described exemplary embodiments, but should be defined only in accordance with the following claims and their equivalents.
  • [0080]
    In addition, it should be understood that the figures and screen shots illustrated in the attachments, which highlight the functionality and advantages of the present invention, are presented for example purposes only. The architecture of the present invention is sufficiently flexible and configurable, such that it may be utilized (and navigated) in ways other than that shown in the accompanying figures.

Claims (20)

    What is claimed is:
  1. 1. A method comprising:
    determining, by a computer-based system for modeling consumer spend, an industry size of wallet for each consumer using a fixed weighting factor and a graded weighting factor in conjunction with lifestyle variables,
    wherein the graded weighting factor varies in accordance with the value of at least one of the lifestyle variables.
  2. 2. The method of claim 1, further comprising calculating the size of wallet for each consumer in the plurality of consumers by modeling, by the computer-based system, spending patterns using internal customer data and consumer panel data.
  3. 3. The method of claim 1, further comprising calculating the size of wallet for each consumer in the plurality of consumers by estimating, by the computer-based system, credit-related information of each consumer based on tradeline data of each consumer, previous balance transfers of each consumer, and a model of consumer spending patterns to arrive at estimated credit-related information.
  4. 4. The method of claim 3, wherein the calculating the size of wallet for each consumer in the plurality of consumers further comprises offsetting, by the computer-based system, the previous balance transfers from the estimated credit-related information.
  5. 5. The method of claim 1, wherein,
    calculating a share of wallet comprises calculating the share of wallet associated with a given financial institution; and
    determining a subset of the plurality of consumers comprises identifying consumers whose share of wallet associated with the financial institution is greater than approximately 90%.
  6. 6. The method of claim 1, wherein the determining an industry size of wallet comprises:
    determining the amount of spend within the industry using one or more accounts associated with a financial institution; and
    equating the amount of spend within the industry with the industry size of wallet.
  7. 7. The method of claim 1, further comprising:
    determining, by the computer-based system, a subset of the plurality of consumers whose share of wallet is above a given percentage of their size of wallet;
    deriving, by the computer-based system, a correlation between an industry size of wallet for a given consumer and one or more characteristics of the given consumer using the industry size of wallet for the consumers in the subset,
    wherein the characteristics of the consumer include at least one of:
    total size of wallet of the consumer;
    residence location;
    credit bureau tenure;
    age;
    gender;
    household size; and
    number of active transaction cards in a household of the consumer.
  8. 8. The method of claim 7, wherein the deriving a correlation comprises:
    identifying consumers having substantially similar industry sizes of wallet; and
    examining spend habits of the identified consumers to ascertain common characteristics that influence spend in the industry.
  9. 9. The method of claim 1, wherein the industry is at least one of a travel industry, a restaurant industry, or an everyday spend industry.
  10. 10. The method of claim 1, wherein the industry is at least one of an airline industry, a lodging industry, or a vehicle rental industry.
  11. 11. The method of claim 7, further comprising developing a model based on correlations between the industry size of wallet and the characteristics of the consumer.
  12. 12. The method of claim 1, further comprising calculating, by the computer-based system, a size of wallet for each consumer in a plurality of consumers.
  13. 13. The method of claim 1, wherein the lifestyle variables comprise at least one of a location rank, a length of each consumer's tenure with a credit bureau, each consumer's gender.
  14. 14. The method of claim 1, wherein determining the industry size of wallet further comprises using each consumer's household size.
  15. 15. A method comprising:
    calculating, by a computer-based system for targeting consumers, an external size of an industry size of wallet of each consumer using a fixed weighting factor and a graded weighting factor in conjunction with lifestyle variables,
    wherein the graded weighting factor varies in accordance with the value of at least one of the lifestyle variables.
  16. 16. The method of claim 15, wherein the industry size of wallet of each consumer is calculated by modeling, by the computer-based system, spending patterns using internal customer data, and consumer panel data.
  17. 17. The method of claim 15, wherein the industry size of wallet of each consumer is calculated by:
    estimating, by the computer-based system, credit-related information of each consumer based on tradeline data of each consumer, previous balance transfers of each consumer, and a model of consumer spending patterns to arrive at estimated credit-related information; and
    offsetting, by the computer-based system, the previous balance transfers from the estimated credit-related information.
  18. 18. The method of claim 15, further comprising targeting, by the computer-based system, one or more consumers having a relatively high external size of the industry size of wallet and a given minimal total share of wallet with offers to increase an industry share of wallet associated with the financial institution.
  19. 19. The method of claim 15, wherein the calculating an external size of the industry size of wallet of the consumer comprises subtracting industry spend of the consumer associated with the financial institution from the industry size of wallet of the consumer.
  20. 20. A computer readable storage medium bearing instructions, the instructions, when executed by a processor for modeling consumer spend by industry, cause said processor to perform operations comprising:
    determining, by the processor, an industry size of wallet for each consumer using a fixed weighting factor and a graded weighting factor in conjunction with lifestyle variables,
    wherein the graded weighting factor varies in accordance with the value of at least one of the lifestyle variables.
US14085522 2006-12-01 2013-11-20 Industry size of wallet Abandoned US20140081705A1 (en)

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US11608179 US8239250B2 (en) 2006-12-01 2006-12-07 Industry size of wallet
US13538936 US8401947B2 (en) 2006-12-01 2012-06-29 Industry size of wallet
US13776178 US8615458B2 (en) 2006-12-01 2013-02-25 Industry size of wallet
US14085522 US20140081705A1 (en) 2006-12-01 2013-11-20 Industry size of wallet

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Families Citing this family (50)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7593891B2 (en) 2003-05-30 2009-09-22 Experian Scorex Llc Credit score simulation
US9710852B1 (en) 2002-05-30 2017-07-18 Consumerinfo.Com, Inc. Credit report timeline user interface
US7610229B1 (en) 2002-05-30 2009-10-27 Experian Information Solutions, Inc. System and method for interactively simulating a credit-worthiness score
US8930263B1 (en) 2003-05-30 2015-01-06 Consumerinfo.Com, Inc. Credit data analysis
US9400589B1 (en) 2002-05-30 2016-07-26 Consumerinfo.Com, Inc. Circular rotational interface for display of consumer credit information
US9569797B1 (en) 2002-05-30 2017-02-14 Consumerinfo.Com, Inc. Systems and methods of presenting simulated credit score information
US7814004B2 (en) 2004-10-29 2010-10-12 American Express Travel Related Services Company, Inc. Method and apparatus for development and use of a credit score based on spend capacity
US20070016501A1 (en) 2004-10-29 2007-01-18 American Express Travel Related Services Co., Inc., A New York Corporation Using commercial share of wallet to rate business prospects
US8086509B2 (en) 2004-10-29 2011-12-27 American Express Travel Related Services Company, Inc. Determining commercial share of wallet
US7822665B2 (en) 2004-10-29 2010-10-26 American Express Travel Related Services Company, Inc. Using commercial share of wallet in private equity investments
US8204774B2 (en) 2004-10-29 2012-06-19 American Express Travel Related Services Company, Inc. Estimating the spend capacity of consumer households
US8543499B2 (en) 2004-10-29 2013-09-24 American Express Travel Related Services Company, Inc. Reducing risks related to check verification
US7792732B2 (en) 2004-10-29 2010-09-07 American Express Travel Related Services Company, Inc. Using commercial share of wallet to rate investments
US8630929B2 (en) * 2004-10-29 2014-01-14 American Express Travel Related Services Company, Inc. Using commercial share of wallet to make lending decisions
US20080221947A1 (en) * 2005-10-24 2008-09-11 Megdal Myles G Using commercial share of wallet to make lending decisions
US20080221970A1 (en) * 2005-10-24 2008-09-11 Megdal Myles G Method and apparatus for targeting best customers based on spend capacity
US20080228539A1 (en) * 2005-10-24 2008-09-18 Megdal Myles G Using commercial share of wallet to manage vendors
US20080222027A1 (en) * 2005-10-24 2008-09-11 Megdal Myles G Credit score and scorecard development
US20080228606A1 (en) * 2005-10-24 2008-09-18 Megdal Myles G Determining commercial share of wallet
US20080255897A1 (en) * 2005-10-24 2008-10-16 Megdal Myles G Using commercial share of wallet in financial databases
US20080033852A1 (en) * 2005-10-24 2008-02-07 Megdal Myles G Computer-based modeling of spending behaviors of entities
US20080221990A1 (en) * 2005-10-24 2008-09-11 Megdal Myles G Estimating the spend capacity of consumer households
US20080222015A1 (en) * 2005-10-24 2008-09-11 Megdal Myles G Method and apparatus for development and use of a credit score based on spend capacity
US8799148B2 (en) 2006-08-31 2014-08-05 Rohan K. K. Chandran Systems and methods of ranking a plurality of credit card offers
US8036979B1 (en) 2006-10-05 2011-10-11 Experian Information Solutions, Inc. System and method for generating a finance attribute from tradeline data
US8239250B2 (en) * 2006-12-01 2012-08-07 American Express Travel Related Services Company, Inc. Industry size of wallet
US8606626B1 (en) 2007-01-31 2013-12-10 Experian Information Solutions, Inc. Systems and methods for providing a direct marketing campaign planning environment
WO2008147918A3 (en) 2007-05-25 2009-01-22 Experian Information Solutions System and method for automated detection of never-pay data sets
US9690820B1 (en) 2007-09-27 2017-06-27 Experian Information Solutions, Inc. Database system for triggering event notifications based on updates to database records
US20090157517A1 (en) * 2007-12-14 2009-06-18 The Western Union Company Adjustable balance limit prepaid presentation instrument
US8380559B2 (en) * 2007-12-31 2013-02-19 American Express Travel Related Services Company, Inc. Identifying luxury merchants and consumers
US20100023374A1 (en) * 2008-07-25 2010-01-28 American Express Travel Related Services Company, Inc. Providing Tailored Messaging to Customers
US9256904B1 (en) 2008-08-14 2016-02-09 Experian Information Solutions, Inc. Multi-bureau credit file freeze and unfreeze
US20110178855A1 (en) * 2010-01-20 2011-07-21 American Express Travel Related Services Company, System and method for increasing marketing performance using spend level data
US20110178844A1 (en) * 2010-01-20 2011-07-21 American Express Travel Related Services Company, Inc. System and method for using spend behavior to identify a population of merchants
US20120278177A1 (en) * 2011-04-27 2012-11-01 American Express Travel Related Services Company, Inc. Systems and methods for spend analysis
US9558519B1 (en) 2011-04-29 2017-01-31 Consumerinfo.Com, Inc. Exposing reporting cycle information
US8738516B1 (en) 2011-10-13 2014-05-27 Consumerinfo.Com, Inc. Debt services candidate locator
US8473410B1 (en) 2012-02-23 2013-06-25 American Express Travel Related Services Company, Inc. Systems and methods for identifying financial relationships
US9477988B2 (en) 2012-02-23 2016-10-25 American Express Travel Related Services Company, Inc. Systems and methods for identifying financial relationships
US8442886B1 (en) 2012-02-23 2013-05-14 American Express Travel Related Services Company, Inc. Systems and methods for identifying financial relationships
US8781954B2 (en) 2012-02-23 2014-07-15 American Express Travel Related Services Company, Inc. Systems and methods for identifying financial relationships
US8538869B1 (en) 2012-02-23 2013-09-17 American Express Travel Related Services Company, Inc. Systems and methods for identifying financial relationships
US9947004B2 (en) 2012-06-28 2018-04-17 Green Dot Corporation Wireless client transaction systems and related methods
US20140095251A1 (en) * 2012-10-03 2014-04-03 Citicorp Credit Services, Inc. Methods and Systems for Optimizing Marketing Strategy to Customers or Prospective Customers of a Financial Institution
US9916621B1 (en) 2012-11-30 2018-03-13 Consumerinfo.Com, Inc. Presentation of credit score factors
US9870589B1 (en) 2013-03-14 2018-01-16 Consumerinfo.Com, Inc. Credit utilization tracking and reporting
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
USD759690S1 (en) 2014-03-25 2016-06-21 Consumerinfo.Com, Inc. Display screen or portion thereof with graphical user interface

Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6202053B1 (en) * 1998-01-23 2001-03-13 First Usa Bank, Na Method and apparatus for generating segmentation scorecards for evaluating credit risk of bank card applicants
US6430539B1 (en) * 1999-05-06 2002-08-06 Hnc Software Predictive modeling of consumer financial behavior
US20030009368A1 (en) * 2001-07-06 2003-01-09 Kitts Brendan J. Method of predicting a customer's business potential and a data processing system readable medium including code for the method
US20040133474A1 (en) * 2002-12-31 2004-07-08 Big Y Foods, Inc. Method of processing customer information for a retail environment
US20060143075A1 (en) * 2003-09-22 2006-06-29 Ryan Carr Assumed demographics, predicted behaviour, and targeted incentives
US20060143071A1 (en) * 2004-12-14 2006-06-29 Hsbc North America Holdings Inc. Methods, systems and mediums for scoring customers for marketing
US20060253328A1 (en) * 2005-05-06 2006-11-09 Ujjal Kohli Targeted advertising using verifiable information
US20070282681A1 (en) * 2006-05-31 2007-12-06 Eric Shubert Method of obtaining and using anonymous consumer purchase and demographic data
US20080033852A1 (en) * 2005-10-24 2008-02-07 Megdal Myles G Computer-based modeling of spending behaviors of entities
US8239250B2 (en) * 2006-12-01 2012-08-07 American Express Travel Related Services Company, Inc. Industry size of wallet

Family Cites Families (201)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5621812A (en) 1989-05-01 1997-04-15 Credit Verification Corporation Method and system for building a database for use with selective incentive marketing in response to customer shopping histories
US5712985A (en) 1989-09-12 1998-01-27 Lee; Michael D. System and method for estimating business demand based on business influences
US5819226A (en) 1992-09-08 1998-10-06 Hnc Software Inc. Fraud detection using predictive modeling
US5675746A (en) * 1992-09-30 1997-10-07 Marshall; Paul S. Virtual reality generator for use with financial information
US5974396A (en) 1993-02-23 1999-10-26 Moore Business Forms, Inc. Method and system for gathering and analyzing consumer purchasing information based on product and consumer clustering relationships
US5500513A (en) 1994-05-11 1996-03-19 Visa International Automated purchasing control system
JP3865775B2 (en) 1995-04-11 2007-01-10 キネテック インコーポレイテッド Identification data in a data processing system
US5699527A (en) 1995-05-01 1997-12-16 Davidson; David Edward Method and system for processing loan
US5857175A (en) 1995-08-11 1999-01-05 Micro Enhancement International System and method for offering targeted discounts to customers
US5930774A (en) 1996-01-29 1999-07-27 Overlap, Inc. Method and computer program for evaluating mutual fund portfolios
US20080275820A1 (en) 2000-01-21 2008-11-06 Raymond Anthony Joao Apparatus and method for providing account security
US5933817A (en) 1996-09-27 1999-08-03 Hucal; Stephen J. Tiered interest rate revolving credit system and method
US5966699A (en) * 1996-10-11 1999-10-12 Zandi; Richard System and method for conducting loan auction over computer network
KR100230455B1 (en) 1996-10-21 1999-11-15 윤종용 Accounting apparatus and method of management automation system
US5864830A (en) 1997-02-13 1999-01-26 Armetta; David Data processing method of configuring and monitoring a satellite spending card linked to a host credit card
US5970478A (en) 1997-03-12 1999-10-19 Walker Asset Management Limited Partnership Method, apparatus, and program for customizing credit accounts
US6119103A (en) 1997-05-27 2000-09-12 Visa International Service Association Financial risk prediction systems and methods therefor
US6105001A (en) 1997-08-15 2000-08-15 Larry A. Masi Non-cash transaction incentive and commission distribution system
US7376603B1 (en) 1997-08-19 2008-05-20 Fair Isaac Corporation Method and system for evaluating customers of a financial institution using customer relationship value tags
US6128599A (en) 1997-10-09 2000-10-03 Walker Asset Management Limited Partnership Method and apparatus for processing customized group reward offers
US6026398A (en) 1997-10-16 2000-02-15 Imarket, Incorporated System and methods for searching and matching databases
US20020194099A1 (en) 1997-10-30 2002-12-19 Weiss Allan N. Proxy asset system and method
US6249770B1 (en) 1998-01-30 2001-06-19 Citibank, N.A. Method and system of financial spreading and forecasting
US6021362A (en) 1998-02-17 2000-02-01 Maggard; Karl J. Method and apparatus for dispensing samples and premiums
US7340423B1 (en) 1998-04-24 2008-03-04 First Data Corporation Method for defining a relationship between an account and a group
US6185543B1 (en) 1998-05-15 2001-02-06 Marketswitch Corp. Method and apparatus for determining loan prepayment scores
US6311169B2 (en) 1998-06-11 2001-10-30 Consumer Credit Associates, Inc. On-line consumer credit data reporting system
US7249114B2 (en) 1998-08-06 2007-07-24 Cybersettle Holdings, Inc. Computerized dispute resolution system and method
US6266649B1 (en) 1998-09-18 2001-07-24 Amazon.Com, Inc. Collaborative recommendations using item-to-item similarity mappings
US6405181B2 (en) 1998-11-03 2002-06-11 Nextcard, Inc. Method and apparatus for real time on line credit approval
US6324524B1 (en) 1998-11-03 2001-11-27 Nextcard, Inc. Method and apparatus for an account level offer of credit and real time balance transfer
US6134548A (en) 1998-11-19 2000-10-17 Ac Properties B.V. System, method and article of manufacture for advanced mobile bargain shopping
US6298348B1 (en) 1998-12-03 2001-10-02 Expanse Networks, Inc. Consumer profiling system
US7702550B2 (en) 1999-03-31 2010-04-20 New Market Solutions, Llc Multiple computer system supporting a private constant-dollar financial product
US6658412B1 (en) 1999-06-30 2003-12-02 Educational Testing Service Computer-based method and system for linking records in data files
US8600869B1 (en) 1999-08-31 2013-12-03 Capital One Financial Corporation System and method for assigning a line of credit to a credit card account
US7373324B1 (en) 1999-10-07 2008-05-13 Robert C. Osborne Method and system for exchange of financial investment advice
US9886722B1 (en) 1999-11-26 2018-02-06 Esurance Insurance Services, Inc. Insurance marketing methods
WO2001045012A8 (en) 1999-12-15 2002-06-06 Donald W Binns Systems and methods for providing consumers anonymous pre-approved offers from a consumer-selected group of merchants
US20020029188A1 (en) * 1999-12-20 2002-03-07 Schmid Stephen J. Method and apparatus to facilitate competitive financing activities among myriad lenders on behalf of one borrower
US7191150B1 (en) 2000-02-01 2007-03-13 Fair Isaac Corporation Enhancing delinquent debt collection using statistical models of debt historical information and account events
US20010027413A1 (en) 2000-02-23 2001-10-04 Bhutta Hafiz Khalid Rehman System, software and method of evaluating, buying and selling consumer's present and potential buying power through a clearing house
US20040024692A1 (en) 2001-02-27 2004-02-05 Turbeville Wallace C. Counterparty credit risk system
US6687713B2 (en) 2000-02-29 2004-02-03 Groupthink Unlimited, Inc. Budget information, analysis, and projection system and method
US7076462B1 (en) 2000-03-02 2006-07-11 Nelson Joseph E System and method for electronic loan application and for correcting credit report errors
US8280773B2 (en) 2000-03-13 2012-10-02 Intellions, Inc. Method and apparatus for internet customer retention
US7599879B2 (en) * 2000-03-24 2009-10-06 Jpmorgan Chase Bank, National Association Syndication loan administration and processing system
JP2001282957A (en) 2000-03-29 2001-10-12 Moody's Investers Service Inc System and method for analyzing credit risk
US7263506B2 (en) 2000-04-06 2007-08-28 Fair Isaac Corporation Identification and management of fraudulent credit/debit card purchases at merchant ecommerce sites
US20020049626A1 (en) * 2000-04-14 2002-04-25 Peter Mathias Method and system for interfacing clients with relationship management (RM) accounts and for permissioning marketing
US20020019804A1 (en) 2000-06-29 2002-02-14 Sutton Robert E. Method for providing financial and risk management
US7376618B1 (en) 2000-06-30 2008-05-20 Fair Isaac Corporation Detecting and measuring risk with predictive models using content mining
JP2002024540A (en) 2000-07-07 2002-01-25 Nec Corp Financing examination system and financing examination method
US20040199456A1 (en) 2000-08-01 2004-10-07 Andrew Flint Method and apparatus for explaining credit scores
US8078524B2 (en) 2001-02-22 2011-12-13 Fair Isaac Corporation Method and apparatus for explaining credit scores
US20020052836A1 (en) 2000-08-31 2002-05-02 Yuri Galperin Method and apparatus for determining a prepayment score for an individual applicant
US20030208428A1 (en) 2001-09-26 2003-11-06 Sylain Raynes Inverse solution for structured finance
US8190511B2 (en) 2000-10-05 2012-05-29 American Express Travel Related Services Company, Inc. Systems, methods and computer program products for offering consumer loans having customized terms for each customer
US7426488B1 (en) 2000-11-14 2008-09-16 Gompers Paul A Private equity investments
JP2002163449A (en) 2000-11-29 2002-06-07 World Business Management Kk Method and system for financing and evaluating method for technology-secured credit
US20030113727A1 (en) 2000-12-06 2003-06-19 Girn Kanwaljit Singh Family history based genetic screening method and apparatus
US20020073099A1 (en) 2000-12-08 2002-06-13 Gilbert Eric S. De-identification and linkage of data records
US20020107765A1 (en) 2000-12-13 2002-08-08 Timothy Walker Electronic financing system
US6859785B2 (en) 2001-01-11 2005-02-22 Case Strategy Llp Diagnostic method and apparatus for business growth strategy
US7529698B2 (en) 2001-01-16 2009-05-05 Raymond Anthony Joao Apparatus and method for providing transaction history information, account history information, and/or charge-back information
US20040088221A1 (en) * 2001-01-30 2004-05-06 Katz Gary M System and method for computing measures of retailer loyalty
US20020147623A1 (en) 2001-03-02 2002-10-10 Rifaat Ismail Ibrahim Method for interactive communication and information processing
US8433632B2 (en) 2001-03-13 2013-04-30 Lawson Software, Inc. Interactive method and apparatus for real-time financial
US7324962B1 (en) 2001-03-29 2008-01-29 Symbol Technologies, Inc. Network for alliance marketing
US7398225B2 (en) * 2001-03-29 2008-07-08 American Express Travel Related Services Company, Inc. System and method for networked loyalty program
US20020143661A1 (en) 2001-03-30 2002-10-03 Tumulty William J. System and method for prioritizing customer inquiries
US20020194117A1 (en) 2001-04-06 2002-12-19 Oumar Nabe Methods and systems for customer relationship management
US6640204B2 (en) * 2001-04-06 2003-10-28 Barry E. Feldman Method and system for using cooperative game theory to resolve statistical joint effects
US20020194140A1 (en) 2001-04-18 2002-12-19 Keith Makuck Metered access to content
US7346573B1 (en) 2001-05-10 2008-03-18 Goldman Sachs & Co. Methods and systems for managing investments in complex financial investments
US7249076B1 (en) 2001-05-14 2007-07-24 Compucredit Intellectual Property Holdings Corp. Iii Method for providing credit offering and credit management information services
US7555451B2 (en) 2001-05-17 2009-06-30 Microsoft Corporation Cash flow forecasting
JP4198473B2 (en) 2001-05-23 2008-12-17 惇郎 奴田原 Deposit balance automatic adjustment system and method
US20030004787A1 (en) 2001-05-30 2003-01-02 The Procter & Gamble Company Marketing system
WO2002099598A3 (en) 2001-06-07 2004-03-25 First Usa Bank Na System and method for rapid updating of credit information
US7552081B2 (en) 2001-06-29 2009-06-23 International Business Machines Corporation User rating system for online auctions
US20030002639A1 (en) 2001-07-02 2003-01-02 Huie David L. Real-time call validation system
US20030093289A1 (en) 2001-07-31 2003-05-15 Thornley Robert D. Reporting and collecting rent payment history
US20030061132A1 (en) 2001-09-26 2003-03-27 Yu, Mason K. System and method for categorizing, aggregating and analyzing payment transactions data
US7657471B1 (en) 2001-10-01 2010-02-02 Lawson Software, Inc. Method and apparatus providing automated financial plan controls
US7966235B1 (en) 2001-10-01 2011-06-21 Lawson Software, Inc. Method and apparatus providing automated control of spending plans
EP1444629A4 (en) 2001-10-23 2006-06-14 Electronic Data Syst Corp System and method for managing spending
CA2919269A1 (en) 2001-11-01 2003-05-08 Jpmorgan Chase Bank, N.A. System and method for establishing or modifying an account with user selectable terms
US6654727B2 (en) 2001-11-29 2003-11-25 Lynn Tilton Method of securitizing a portfolio of at least 30% distressed commercial loans
US7574396B2 (en) 2001-12-04 2009-08-11 Andrew Kalotay Associates, Inc. Method of and apparatus for administering an asset-backed security using coupled lattice efficiency analysis
US20030130884A1 (en) 2002-01-09 2003-07-10 Gerald Michaluk Strategic business planning method
US20030139986A1 (en) 2002-01-23 2003-07-24 Electronic Data Systems Spend analysis system and method
US7630932B2 (en) 2002-01-31 2009-12-08 Transunion Interactive, Inc. Loan rate and lending information analysis system
US20030149610A1 (en) 2002-02-06 2003-08-07 Rowan Christopher G. Method of strategic planning
US20030171942A1 (en) 2002-03-06 2003-09-11 I-Centrix Llc Contact relationship management system and method
US20050262014A1 (en) 2002-03-15 2005-11-24 Fickes Steven W Relative valuation system for measuring the relative values, relative risks, and financial performance of corporate enterprises
JP2006513462A (en) 2002-03-20 2006-04-20 カタリナ マーケティング インターナショナル,インク. Target incentives based on the predicted behavior
US7099878B2 (en) 2002-04-08 2006-08-29 First Data Corporation System and method for managing account addresses
US7587352B2 (en) 2002-04-10 2009-09-08 Research Affiliates, Llc Method and apparatus for managing a virtual portfolio of investment objects
JP4358475B2 (en) 2002-04-23 2009-11-04 株式会社 金融工学研究所 Credit rating system
US20030212618A1 (en) 2002-05-07 2003-11-13 General Electric Capital Corporation Systems and methods associated with targeted leading indicators
US20040078248A1 (en) 2002-05-29 2004-04-22 Altschuler Douglas H. Method and apparatus for protecting an entity against loss in its valuation
US7593891B2 (en) 2003-05-30 2009-09-22 Experian Scorex Llc Credit score simulation
US7747502B2 (en) 2002-06-03 2010-06-29 Research Affiliates, Llc Using accounting data based indexing to create a portfolio of assets
US20030229580A1 (en) 2002-06-10 2003-12-11 David Gass Method for establishing or improving a credit score or rating for a business
US20030236725A1 (en) 2002-06-25 2003-12-25 First Data Corporation Financial statement presentment systems and methods
US20040002916A1 (en) 2002-07-01 2004-01-01 Sarah Timmerman Systems and methods for managing balance transfer accounts
JP2004078435A (en) 2002-08-13 2004-03-11 Ibm Japan Ltd Risk management device, risk management system, risk management method, future expected profit computing method, and program
US7792715B1 (en) 2002-09-21 2010-09-07 Mighty Net, Incorporated Method of on-line credit information monitoring and control
US20040064401A1 (en) 2002-09-27 2004-04-01 Capital One Financial Corporation Systems and methods for detecting fraudulent information
US20040122736A1 (en) 2002-10-11 2004-06-24 Bank One, Delaware, N.A. System and method for granting promotional rewards to credit account holders
US7966255B2 (en) 2002-11-01 2011-06-21 American Express Travel Related Services Company, Inc. Method and apparatus for a no pre-set spending limit transaction card
US7720761B2 (en) 2002-11-18 2010-05-18 Jpmorgan Chase Bank, N. A. Method and system for enhancing credit line management, price management and other discretionary levels setting for financial accounts
US7603300B2 (en) 2002-11-18 2009-10-13 Sap Aktiengesellschaft Collection and analysis of trading data in an electronic marketplace
US7472090B1 (en) 2002-12-31 2008-12-30 Capital One Financial Corporation Method and system for providing a higher credit limit to a customer
US7912842B1 (en) 2003-02-04 2011-03-22 Lexisnexis Risk Data Management Inc. Method and system for processing and linking data records
US7403942B1 (en) 2003-02-04 2008-07-22 Seisint, Inc. Method and system for processing data records
US7657540B1 (en) 2003-02-04 2010-02-02 Seisint, Inc. Method and system for linking and delinking data records
US7302405B2 (en) 2003-02-19 2007-11-27 Accenture Global Services Gmbh Methods for managing and developing sourcing and procurement operations
US20040177030A1 (en) 2003-03-03 2004-09-09 Dan Shoham Psychometric Creditworthiness Scoring for Business Loans
WO2004079487A8 (en) 2003-03-03 2005-02-17 John Woods & Associates Ltd System and method for managing investment funds
US8326712B2 (en) 2003-04-16 2012-12-04 American Express Travel Related Services Company, Inc. Method and system for technology consumption management
US7873527B2 (en) 2003-05-14 2011-01-18 International Business Machines Corporation Insurance for service level agreements in e-utilities and other e-service environments
US7647344B2 (en) 2003-05-29 2010-01-12 Experian Marketing Solutions, Inc. System, method and software for providing persistent entity identification and linking entity information in an integrated data repository
US7937286B2 (en) * 2003-06-10 2011-05-03 Citicorp Credit Services, Inc. System and method for analyzing marketing efforts
US20050015330A1 (en) 2003-07-11 2005-01-20 Beery Peter Douglas Method for enabling risk management for sellers of items on internet auction sites
US7249128B2 (en) 2003-08-05 2007-07-24 International Business Machines Corporation Performance prediction system with query mining
US20090132347A1 (en) 2003-08-12 2009-05-21 Russell Wayne Anderson Systems And Methods For Aggregating And Utilizing Retail Transaction Records At The Customer Level
WO2005020031A3 (en) 2003-08-22 2006-06-15 Jill Maitland Methods and systems for predicting business behavior from profiling consumer card transactions
US20050125334A1 (en) * 2003-12-03 2005-06-09 Eric Masella Automated method and system for processing mortgage leads
KR100439437B1 (en) 2003-12-18 2004-07-09 주식회사 교원나라 Bank transaction system for linked accounts via common account
US7792719B2 (en) 2004-02-04 2010-09-07 Research Affiliates, Llc Valuation indifferent non-capitalization weighted index and portfolio
US8103530B2 (en) 2004-03-26 2012-01-24 Accenture Global Services Limited Enhancing insight-driven customer interactions with an optimizing engine
US20060282356A1 (en) * 2004-04-15 2006-12-14 Brad Andres System and method for structured put auction rate combination structure
JP2006004307A (en) 2004-06-21 2006-01-05 Hitachi Ltd Business assessment support method
US8571951B2 (en) * 2004-08-19 2013-10-29 Leadpoint, Inc. Automated attachment of segmentation data to hot contact leads for facilitating matching of leads to interested lead buyers
US7516149B2 (en) 2004-08-30 2009-04-07 Microsoft Corporation Robust detector of fuzzy duplicates
US20070016501A1 (en) 2004-10-29 2007-01-18 American Express Travel Related Services Co., Inc., A New York Corporation Using commercial share of wallet to rate business prospects
US7610243B2 (en) 2004-10-29 2009-10-27 American Express Travel Related Services Company, Inc. Method and apparatus for rating asset-backed securities
US7822665B2 (en) 2004-10-29 2010-10-26 American Express Travel Related Services Company, Inc. Using commercial share of wallet in private equity investments
US8086509B2 (en) 2004-10-29 2011-12-27 American Express Travel Related Services Company, Inc. Determining commercial share of wallet
US20070244732A1 (en) 2004-10-29 2007-10-18 American Express Travel Related Services Co., Inc., A New York Corporation Using commercial share of wallet to manage vendors
US7792732B2 (en) 2004-10-29 2010-09-07 American Express Travel Related Services Company, Inc. Using commercial share of wallet to rate investments
US8326672B2 (en) 2004-10-29 2012-12-04 American Express Travel Related Services Company, Inc. Using commercial share of wallet in financial databases
US7814004B2 (en) 2004-10-29 2010-10-12 American Express Travel Related Services Company, Inc. Method and apparatus for development and use of a credit score based on spend capacity
US8326671B2 (en) 2004-10-29 2012-12-04 American Express Travel Related Services Company, Inc. Using commercial share of wallet to analyze vendors in online marketplaces
US8630929B2 (en) 2004-10-29 2014-01-14 American Express Travel Related Services Company, Inc. Using commercial share of wallet to make lending decisions
US7788147B2 (en) 2004-10-29 2010-08-31 American Express Travel Related Services Company, Inc. Method and apparatus for estimating the spend capacity of consumers
US8131614B2 (en) 2004-10-29 2012-03-06 American Express Travel Related Services Company, Inc. Using commercial share of wallet to compile marketing company lists
US7840484B2 (en) 2004-10-29 2010-11-23 American Express Travel Related Services Company, Inc. Credit score and scorecard development
US20070226114A1 (en) 2004-10-29 2007-09-27 American Express Travel Related Services Co., Inc., A New York Corporation Using commercial share of wallet to manage investments
US8204774B2 (en) 2004-10-29 2012-06-19 American Express Travel Related Services Company, Inc. Estimating the spend capacity of consumer households
US20060242050A1 (en) 2004-10-29 2006-10-26 American Express Travel Related Services Company, Inc. Method and apparatus for targeting best customers based on spend capacity
US20060242048A1 (en) 2004-10-29 2006-10-26 American Express Travel Related Services Company, Inc. Method and apparatus for determining credit characteristics of a consumer
US8543499B2 (en) 2004-10-29 2013-09-24 American Express Travel Related Services Company, Inc. Reducing risks related to check verification
US20070016500A1 (en) 2004-10-29 2007-01-18 American Express Travel Related Services Co., Inc. A New York Corporation Using commercial share of wallet to determine insurance risk
US7661110B2 (en) 2004-10-29 2010-02-09 At&T Intellectual Property I, L.P. Transaction tool management integration with change management
US7912770B2 (en) 2004-10-29 2011-03-22 American Express Travel Related Services Company, Inc. Method and apparatus for consumer interaction based on spend capacity
US7409362B2 (en) 2004-12-23 2008-08-05 Diamond Review, Inc. Vendor-driven, social-network enabled review system and method with flexible syndication
US20060155624A1 (en) 2005-01-08 2006-07-13 Schwartz Jason P Insurance product, risk transfer product, or fidelity bond product for lost income and/or expenses due to jury duty service
US20060178957A1 (en) 2005-01-18 2006-08-10 Visa U.S.A. Commercial market determination and forecasting system and method
US20060195390A1 (en) 2005-02-28 2006-08-31 Educap, Inc. Administration of dual component financial instruments
US8177121B2 (en) * 2006-01-13 2012-05-15 Intuit Inc. Automated aggregation and comparison of business spending relative to similar businesses
US7908242B1 (en) 2005-04-11 2011-03-15 Experian Information Solutions, Inc. Systems and methods for optimizing database queries
WO2006122324A3 (en) 2005-05-11 2007-11-15 Rose Higgins Interactive user interface for accessing health and financial data
US20060271552A1 (en) 2005-05-26 2006-11-30 Venture Capital & Consulting Group, Llc. Targeted delivery of content
WO2007017874A3 (en) * 2005-08-10 2007-07-05 Axcessnet Innovations Llc Networked loan market and lending management system
US20080222015A1 (en) 2005-10-24 2008-09-11 Megdal Myles G Method and apparatus for development and use of a credit score based on spend capacity
US20080222027A1 (en) 2005-10-24 2008-09-11 Megdal Myles G Credit score and scorecard development
US20080222016A1 (en) 2005-10-24 2008-09-11 Megdal Myles G Using commercial share of wallet to manage investments
US20080221973A1 (en) 2005-10-24 2008-09-11 Megdal Myles G Using commercial share of wallet to rate investments
US20080221990A1 (en) 2005-10-24 2008-09-11 Megdal Myles G Estimating the spend capacity of consumer households
US20080228606A1 (en) 2005-10-24 2008-09-18 Megdal Myles G Determining commercial share of wallet
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
US20080228635A1 (en) 2005-10-24 2008-09-18 Megdal Myles G Reducing risks related to check verification
US20080228539A1 (en) 2005-10-24 2008-09-18 Megdal Myles G Using commercial share of wallet to manage vendors
US20080255897A1 (en) 2005-10-24 2008-10-16 Megdal Myles G Using commercial share of wallet in financial databases
US20080221934A1 (en) 2005-10-24 2008-09-11 Megdal Myles G Using commercial share of wallet to determine insurance risk
US20080243680A1 (en) 2005-10-24 2008-10-02 Megdal Myles G Method and apparatus for rating asset-backed securities
US20080221947A1 (en) 2005-10-24 2008-09-11 Megdal Myles G Using commercial share of wallet to make lending decisions
US20080221972A1 (en) 2005-10-24 2008-09-11 Megdal Myles G Method and apparatus for determining credit characteristics of a consumer
US20080221970A1 (en) 2005-10-24 2008-09-11 Megdal Myles G Method and apparatus for targeting best customers based on spend capacity
US20080228538A1 (en) 2005-10-24 2008-09-18 Megdal Myles G Using commercial share of wallet to analyze vendors in online marketplaces
US20080228556A1 (en) 2005-10-24 2008-09-18 Megdal Myles G Method and apparatus for consumer interaction based on spend capacity
US7753259B1 (en) 2006-04-13 2010-07-13 Jpmorgan Chase Bank, N.A. System and method for granting promotional rewards to both customers and non-customers
US7624070B2 (en) 2006-03-08 2009-11-24 Martin Frederick Lebouitz Open payments target marketing system
US20070244779A1 (en) 2006-03-28 2007-10-18 Ran Wolff Business to business financial transactions
US20070265957A1 (en) 2006-05-10 2007-11-15 Asheesh Advani System and method for automated flexible person-to-person lending
US7912865B2 (en) 2006-09-26 2011-03-22 Experian Marketing Solutions, Inc. System and method for linking multiple entities in a business database
US7945512B2 (en) 2007-03-14 2011-05-17 Ebay Inc. Spending and savings secondary linked accounts
US20090006245A1 (en) 2007-06-26 2009-01-01 Jeremy Rabson Method and system for administering linked loans
US8548903B2 (en) 2007-10-23 2013-10-01 Trans Union Llc. Systems and methods for minimizing effects of authorized user credit tradelines
US7882027B2 (en) 2008-03-28 2011-02-01 American Express Travel Related Services Company, Inc. Consumer behaviors at lender level
US8266168B2 (en) 2008-04-24 2012-09-11 Lexisnexis Risk & Information Analytics Group Inc. Database systems and methods for linking records and entity representations with sufficiently high confidence
US20100088220A1 (en) 2008-10-07 2010-04-08 Syphr Llc Systems and Methods for Providing Loan Analysis
US20100287093A1 (en) 2009-05-07 2010-11-11 Haijian He System and Method for Collections on Delinquent Financial Accounts
US8799150B2 (en) 2009-09-30 2014-08-05 Scorelogix Llc System and method for predicting consumer credit risk using income risk based credit score
US8595058B2 (en) 2009-10-15 2013-11-26 Visa U.S.A. Systems and methods to match identifiers
US8498930B2 (en) 2010-11-09 2013-07-30 Creditxpert, Inc. System and method for credit forecasting

Patent Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6202053B1 (en) * 1998-01-23 2001-03-13 First Usa Bank, Na Method and apparatus for generating segmentation scorecards for evaluating credit risk of bank card applicants
US6430539B1 (en) * 1999-05-06 2002-08-06 Hnc Software Predictive modeling of consumer financial behavior
US20030009368A1 (en) * 2001-07-06 2003-01-09 Kitts Brendan J. Method of predicting a customer's business potential and a data processing system readable medium including code for the method
US20040133474A1 (en) * 2002-12-31 2004-07-08 Big Y Foods, Inc. Method of processing customer information for a retail environment
US20060143075A1 (en) * 2003-09-22 2006-06-29 Ryan Carr Assumed demographics, predicted behaviour, and targeted incentives
US20060143071A1 (en) * 2004-12-14 2006-06-29 Hsbc North America Holdings Inc. Methods, systems and mediums for scoring customers for marketing
US20060253328A1 (en) * 2005-05-06 2006-11-09 Ujjal Kohli Targeted advertising using verifiable information
US20080033852A1 (en) * 2005-10-24 2008-02-07 Megdal Myles G Computer-based modeling of spending behaviors of entities
US20070282681A1 (en) * 2006-05-31 2007-12-06 Eric Shubert Method of obtaining and using anonymous consumer purchase and demographic data
US8239250B2 (en) * 2006-12-01 2012-08-07 American Express Travel Related Services Company, Inc. Industry size of wallet

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