SYSTEM AND METHOD FOR PREDICTING ITEMS PURCHASED BASED ON TRANSACTION DATA

• US 20150332414A1
• Filed: 05/13/2014
• Published: 11/19/2015
• Est. Priority Date: 05/13/2014
• Status: Abandoned Application

• Current Assignee: Mastercard International Incorporated (MasterCard Incorporated)
• Original Assignee: Mastercard International Incorporated (MasterCard Incorporated)
• Inventors: Unser, Kenny, Bernard, Serge, Malgatti, Nikhil A.
• Application Number:
  US14/276,561
• Publication Number:
  US 20150332414A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/22 Indexing; Data structures therefor; Storage structures
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06Q 40/12 Accounting

Abstract

A system for analyzing data comprising: an input module for receiving a transaction record corresponding to a product purchase; a database for storing the transaction received by the input module; a computerized predictive model for determining an indicator for the transaction record based on at least one of a customer identifier, a class of merchant, an amount of the transaction, and a terminal identifier, wherein the indicator is indicative of a likelihood of a correct product determination; and one or more processors for: executing the predictive models; and processing the transaction record based upon the indicator determined by the computerized predictive model.

Claims

1. A method for determining a type or category of product purchased as part of a payment card transaction between a customer and a merchant, the method comprising:

receiving at a computer processor, payment card transaction record data including at least one of a customer identifier, a merchant identifier, and a transaction purchase amount corresponding to a product purchase transaction, the transaction record omitting direct product purchase itemization data;

determining, using a predictive model, a likelihood indicator that a given type or category of product sold by the merchant matches that of the actual product purchased in the payment card transaction, by analyzing the transaction record data and comparing with historical data of previous payment card transactions, to generate one or more score indicators that represent different possible product types or categories of product purchased in the payment card transaction;

comparing the one or more score indicators with a threshold value to generate a score index; and

selecting the indicator having the highest score from the score index as representative of the type or category of product actually purchased in the transaction.

2. The method of claim 1, wherein the determining step further comprises comparing the price of the product purchase transaction with one or more predetermined price thresholds associated with a merchant spend profile, wherein a given category or type of product for purchase by the merchant is associated with a corresponding price threshold.

3. The method of claim 1, wherein said determining step further comprises receiving terminal identifier information in said transaction record representing the terminal at which said transaction occurred, and comparing the purchase price amount of said transaction record with an average purchase price transaction amount associated with said terminal.

4. The method of claim 3, further comprising determining average purchase transaction amounts for each terminal identifier of a given merchant, and comparing said terminal average purchase amounts to allocate categories or types of products purchased with each of said terminals of said merchant.

5. The method of claim 1, wherein said determining a likelihood indicator further comprises performing temporal purchase sequencing of transactions of said customer over a given time interval and correlating the transaction record data to determine a trend of customer behavior indicative of the likelihood that a given category or type of product sold by the merchant is representative of the type or category of product purchased in the transaction.

6. The method of claim 1, wherein said determining a likelihood indicator further comprises determining natural price breaks associated with a merchant based on computerized analysis of aggregate purchase price transaction records associated with a particular merchant.

7. The method of claim 1, wherein analyzing the transaction record data further includes analyzing the customer identifier, the transaction purchase amount, and a class of the merchant corresponding to said transaction record, and comparing with historical data of previous payment card transactions of at least one of the customer and merchant, to generate said one or more score indicators that represent different possible product types or categories of product purchased in the payment card transaction.

8. A system for determining a type or category of product purchased as part of a payment card transaction between a customer and a merchant using payment card transaction data that omits direct product itemization data for said transaction, the system comprising:

one or more data storage devices containing payment card transaction data of a plurality of customers and merchants, the payment card transaction data including customer information, merchant information, and transaction amounts and omitting direct product itemization data for said transactions;

one or more processors;

a memory in communication with the one or more processors and storing program instructions, the one or more processors operative with the program instructions to;

receive at a computer processor, payment card transaction record data including at least one of a customer identifier, a merchant identifier, and a transaction purchase amount corresponding to a product purchase transaction, the transaction record omitting direct product purchase itemization data;

determine, using a predictive model, a likelihood indicator that a given type or category of product sold by the merchant matches that of the actual product purchased in the payment card transaction, by analyzing the transaction record data and comparing with historical data of previous payment card transactions, to generate one or more score indicators that represent different possible product types or categories of product purchased in the payment card transaction;

compare the one or more score indicators with a threshold value to generate a score index; and

select the indicator having the highest score from the score index as representative of the type or category of product actually purchased in the transaction.

9. The system of claim 8, wherein the one or more processors is operative to compare the price of the product purchase transaction with one or more predetermined price thresholds associated with a merchant spend profile, wherein a given category or type of product for purchase by the merchant is associated with a corresponding price threshold.

10. The system of claim 8, wherein the one or more processors is operative to receive terminal identifier information in said transaction record representing the terminal at which said transaction occurred, and compare the purchase price amount of said transaction record with an average purchase price transaction amount associated with said terminal.

11. The system of claim 8, wherein the one or more processors is operative to determine average purchase transaction amounts for each terminal identifier of a given merchant, and compare said terminal average purchase amounts to allocate categories or types of products purchased with each of said terminals of said merchant.

12. The system of claim 8, wherein the one or more processors is operative to determine a likelihood indicator by performing temporal purchase sequencing of transactions of said customer over a given time interval and correlating the transaction record data to determine a trend of customer behavior indicative of the likelihood that a given category or type of product sold by the merchant is representative of the type or category of product purchased in the transaction.

13. The system of claim 8, wherein the one or more processors is operative to determine a likelihood indicator by determining natural price breaks associated with a merchant based on computerized analysis of aggregate purchase price transaction records associated with a particular merchant.

14. The system of claim 8, wherein the one or more processors is configured to analyze the transaction record data including the customer identifier, the transaction purchase amount, and a class of the merchant corresponding to said transaction record, and compare with historical data of previous payment card transactions of at least one of the customer and merchant, to generate said one or more score indicators that represent different possible product types or categories of product purchased in the payment card transaction.

15. A system for analyzing payment card transactions data comprising:

an input module for receiving a transaction record including at least one of a customer identifier, a merchant identifier, and a transaction amount, corresponding to a product purchase transaction, the transaction record omitting direct product purchase itemization data;

a database for storing the transaction record received by the input module;

a computerized predictive model for determining a likelihood indicator that a given type or category of the product purchased as part of the transaction, based on at least one of the customer identifier, transaction amount, a class of merchant, and a terminal identifier, wherein the indicator is indicative of a likelihood of a correct product determination; and

one or more computer processors for;

executing the predictive models; and

processing the transaction record based upon the indicator determined by the computerized predictive model.

16. The system of claim 15, wherein the computerized predictive model is constructed through an analytic process that identifies variables in a plurality of transaction records corresponding to a sale, and calculates at least one score based on analysis of said variables, wherein each score is indicative of a likelihood that the transaction record corresponds to a purchase of a particular good or service.

17. The system of claim 16, wherein the one or more processors aggregate transaction records to generate one or more merchant and customer profiles that associate the product categories of a given merchant with an average product price and average transaction amount.

18. The system of claim 16, wherein the one or more processors determines a variance threshold for a differential in scores that exceed the score threshold, and selects a highest score from scores whose differential exceeds the variance threshold.

19. The system of claim 18, wherein the one or more processors executing said predictive model identifies the transaction record and its corresponding product purchase as being a purchase of a particular type or category of item based on the selected highest score.