Methods and systems for predicting business behavior from profiling consumer card transactions

• US 7,853,469 B2
• Filed: 08/23/2004
• Issued: 12/14/2010
• Est. Priority Date: 08/22/2003
• Status: Active Grant

• Current Assignee: Mastercard International Incorporated (MasterCard Incorporated)
• Original Assignee: MasterCard International InCorporation (MasterCard Incorporated)
• Inventors: Murly, Tatiana, Rademaker, Jennifer Riddell, Walker, Denise A., Maitland, Jill
• Primary Examiner(s): Jarrett; Scott L
• Application Number:
  US10/923,947
• Publication Number:
  US 20050197954A1
• Time in Patent Office: 2,304 Days
• Field of Search: 705/8
• US Class Current: 705/7.12
• CPC Class Codes: G06Q 10/0631 Resource planning, allocation, distributing or scheduling for enterprises or organisations
• CPC Class Codes: G06Q 20/10 specially adapted for electronic funds transfer [EFT] systems; specially adapted for home banking systems
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 40/02 Banking, e.g. interest calculation or account maintenance credit or loans G06Q40/03

Abstract

A system and method are provided for predicting small business behavior by analysis of consumer payment card transaction data. Transaction and amount velocity analysis of industry categories and/or real-time transaction-based profiling is employed to identify those consumer payment card accounts that are being inappropriately used to make small business purchases. A small business behavior predictor model is used to score transaction data and update cardholder profiles according to the likelihood that the transaction data represents small business activity.

Claims

1. A method for identifying a consumer conducting business transactions using a non-business payment card account by analysis of payment card transaction data, the method comprising:

receiving a transaction data file from a transaction data warehouse, said data file including consumer payment card transaction data records reflecting payment transactions using said non-business payment card account;

retrieving a business behavior predictor model from a model data warehouse, said business behavior predictor model containing logic for identifying business spending using a non-business payment card account, said logic based on at least one transaction characteristic for purchases associated with one or more merchant category indicators;

and processing said transaction data file with a computer programmed in accordance with said logic to generate at least one consumer profile score associated with said consumer;

wherein said consumer profile score is indicative of a likelihood that said consumer is conducting business transactions with said non-business payment card account.

2. The method of claim 1, further comprising storing an account profile associated with said consumer in a profile data warehouse, said account profile including said at least one consumer profile score.

3. The method of claim 2, further comprising:

retrieving, prior to said processing said transaction data file, said account profile from said profile data warehouse,

periodically updating said consumer profile score based on said logic contained in said business predictor model, and

storing said updated profile score in said profile data warehouse after said processing.

4. The method of claim 3, wherein said periodically updating occurs at least once per month.

5. The method of claim 1, wherein said predetermined merchant category indicator is at least one of a Merchant Category Code and a Standard Industrial Classification code.

6. The method of claim 1, wherein said predetermined merchant category indicator is associated with merchants selected from the group consisting of Consumer Electronics, Courier Services, Airlines, Restaurants, Hotels, Office Supplies, Reproduction Services, and Fuel merchants

wherein said logic is arranged such that an increased transaction characteristic associated with purchases at said merchant category indicator results in said consumer profile score corresponding to a higher likelihood that said consumer is conducting business transactions with said non-business payment card.

7. The method of claim 1, wherein said predetermined merchant category indicator is associated with merchants selected from the group consisting of Miscellaneous Apparel and Not Valid-No Data, wherein said logic is arranged such that an increased transaction characteristic associated with purchases at said merchant category indicator results in said consumer profile score corresponding to a lower likelihood that said consumer is conducting business transactions with said non-business payment card.

8. The method of claim 1, further comprising generating a report identifying at least one consumer having a consumer profile score.

9. The method of claim 1, further comprising transmitting a small business account card offer to said consumer.

10. The method of claim 1, wherein said merchant category indicators are associated with purchases from at least one category of merchants.

11. The method of claim 1, wherein said at least one transaction characteristic is selected from the group consisting of transaction velocity, transaction volume, spending velocity, and spending volume.