Systems and Methods for Identifying Card-on-File Payment Account Transactions

• US 20180165759A1
• Filed: 12/12/2016
• Published: 06/14/2018
• Est. Priority Date: 12/12/2016
• Status: Active Application

• Current Assignee: Mastercard International Incorporated (MasterCard Incorporated)
• Original Assignee: Mastercard International Incorporated (MasterCard Incorporated)
• Inventors: Carrington, James Ernest, Dantu, Stephen, Sordi, Matthew, Mayawala, Divyam, Chen, Harry
• Application Number:
  US15/375,838
• Publication Number:
  US 20180165759A1
• Time in Patent Office: Days
• CPC Class Codes: G06Q 20/102 Bill distribution or payments
• CPC Class Codes: G06Q 20/36 using electronic wallets or electronic money safes
• CPC Class Codes: G06Q 20/4016 involving fraud or risk level assessment in transaction processing
• CPC Class Codes: G06Q 40/02 Banking, e.g. interest calculation or account maintenance credit or loans G06Q40/03

Abstract

Systems and methods are provided for use in identifying card-on-file payment account transactions. An example method includes accessing, by a computing device, transaction data included in a data structure, where the transaction data includes transaction data for a transaction associated with a payment account and involving a merchant. The method also includes generating, by the computing device, a card-on-file probability score for the transaction, based on whether the transaction data includes a recurring payment indicator for the transaction, whether the transaction involves a known card-on-file application, whether the merchant is a known card-on-file merchant, whether a card verification code is included in the transaction data for the transaction, and/or whether other transactions to the payment account and involving the merchant have been made in a defined interval. In connection therewith, a card-on-file status of the transaction is true when the card-on-file probability score satisfies a defined threshold.

Claims

1. A computer-implemented method for use in identifying card-on-file payment account transactions, the method comprising:

accessing, by a computing device, transaction data included in a data structure, the transaction data including transaction data for a transaction associated with a payment account and involving a merchant; and

generating, by the computing device, a card-on-file probability score for the transaction, based on at least two of the following factors;

whether the transaction data includes a recurring payment indicator for the transaction, whether the transaction involves a known card-on-file application, whether the merchant is a known card-on-file merchant, whether a card verification code is included in the transaction data for the transaction, and whether other transactions to the payment account and involving the merchant have been made in a defined interval;

whereby a card-on-file status of the transaction is true when the card-on-file probability score satisfies a defined threshold.

2. The computer-implemented method of claim 1, further comprising comparing, by the computing device, the card-on-file probability score to the defined threshold, and appending a card-on-file tag to the transaction when the card-on-file probability score satisfies the defined threshold.

3. The computer-implemented method of claim 1, wherein generating the card-on-file probability score for the transaction includes calculating the card-on-file probability score from sub-scores associated with the at least two of the factors;

and

wherein one of the sub-scores is based on the factor of whether the transaction data includes a recurring payment indicator for the transaction.

4. The computer-implemented method of claim 3, further comprising:

accessing, by the computing device, a card-on-file merchant data structure including multiple known card-on-file merchants;

assigning a first value to a second one of the sub-scores when the merchant is one of the multiple known card-on-file merchants; and

assigning a second different value to the second one of the sub-scores when the merchant is not one of the multiple known card-on-file merchants.

5. The computer-implemented method of claim 3, further comprising:

assigning a first value to a second one of the sub-scores, based on the known card-on-file application, when the transaction includes an in-application purchase; and

assigning a second different value to the second one of the sub-scores, based on the known card-on-file application, when the transaction does not includes the in-application purchase.

6. The computer-implemented method of claim 3, further comprising:

assigning a first value to a second one of the sub-scores, based on the known card-on-file application, when the transaction involves a known virtual wallet; and

assigning a second different value to the second one of the sub-scores, based on the known card-on-file application, when the transaction does not involve a known virtual wallet.

7. The computer-implemented method of claim 3, further comprising determining that the card-on-file probability score satisfies the defined threshold when the transaction data for the transaction includes a recurring payment indicator, when the transaction involves a known card-on-file application, and/or when the merchant is a known card-on-file merchant.

8. The computer-implemented method of claim 3, wherein calculating the card-on-file probability score from the sub-scores includes averaging the sub-scores.

9. The computer-implemented method of claim 1, wherein the card-on file probability score is based on sub-scores associated with each of:

whether the transaction data includes a recurring payment indicator for the transaction, whether the transaction involves a known virtual wallet application, whether the merchant is a known card-on-file merchant, whether a card verification code is included in the transaction data for the transaction, and whether other transactions to the payment account and involving the merchant have been made in a defined interval.

10. A system for use in identifying card-on-file payment account transactions, the system comprising:

at least one memory comprising a card-in-file merchant data structure including a listing of known card-on-file merchants, a known application data structure including a listing of multiple applications associated with card-on-file transactions, a card verification code (CVC) data structure including CVC penetration data, and a transaction data structure including at least one transaction involving a merchant; and

a prediction engine coupled to the at least one memory and configured, for a transaction, to;

determine a first sub-score for the transaction, based on a first one of multiple card-on-file factors for the transaction, the card-on-file factors including;

whether the transaction data includes a recurring payment indicator for the transaction, whether the transaction involves a known card-on-file application, whether the merchant is a known card-on-file merchant, whether a card verification code is included in the transaction data for the transaction, and whether other transactions to the payment account and involving the merchant have been made in a defined interval;

determine a second sub-score for the transaction based on a second one of the multiple card-on-file factors;

determine a third sub-score for the transaction based on a third one of the multiple card-on-file factors;

combine the first sub-score, the second sub-score, and the third sub-score into a card-on-file probability score; and

append at least one of the card-on-file probability score and a label associated with the card-on-file probability score to the at least one transaction, the label being associated with the card-on-file probability for the transaction relative to a defined threshold.

11. The system of claim 10, wherein the prediction engine is configured to access the transaction prior to determining the first sub-score.

12. The system of claim 10, wherein the prediction engine is configured to append the label associated with the card-on-file probability score to the transaction.

13. The system of claim 12, wherein the label associated with the card-on-file probability score is a card-on-file label;

and

wherein the prediction engine is configured to determine that the card-on-file probability score satisfies the defined threshold and append the card-on-file label to the transaction when the transaction data for the transaction includes a recurring payment indicator, when the transaction involves a known card-on-file application, and/or when the merchant is a known card-on-file merchant.

14. The system of claim 10, wherein the prediction engine is further configured to determine a fourth sub-score based on a fourth one of the multiple card-on-file factors;

and

wherein the prediction engine is configured to combine the first sub-score, the second sub-score, the third sub-score, and the fourth sub-score into the card-on-file probability score.

15. The system of claim 13, wherein the prediction engine is further configured to determine a fifth sub-score based on a fifth one of the multiple card-on-file factors;

and

wherein the prediction engine is configured to combine the first sub-score, the second sub-score, the third sub-score, the fourth sub-score, and the fifth sub-score into the card-on-file probability score.

16. A non-transitory computer-readable storage media including computer-executable instructions for identifying a card-on-file status of a transaction that, when executed by a processor, cause the processor to:

access a transaction including a merchant identifier and a consumer identifier;

determine a plurality of card-on-file factors for the transaction, wherein the plurality of card-on-file factors includes at least two of a recurring payment factor, an in-app purchase factor, a card-on-file merchant factor based on the merchant identifier, an absent card verification code factor, and a habitual spending factor based on the consumer identifier; and

determine a card-on-file probability score based on the plurality of card-on-file factors.

17. The non-transitory computer-readable storage media of claim 16, wherein the executable instructions, when executed by the processor, further cause the processor to append a card-on-file label to the transaction when the card-in-file factors for the transaction includes a recurring payment factor, an in-app purchase factor, and/or a card-on-file merchant factor.

18. The non-transitory computer-readable storage media of claim 17, wherein the executable instructions, when executed by the processor, further cause the processor, when the card-in-file factors for the transaction do not include a recurring payment factor, an in-app purchase factor, and a card-on-file merchant factor, to:

compare the card-on-file probability score to a defined threshold; and

append a card-on-file label to the transaction when the card-on-file probability score satisfies the defined threshold.

19. The non-transitory computer-readable storage media of claim 18, wherein determining the card-on-file probability score includes averaging sub-scores for each of the plurality of card-on-file factors.

20. The non-transitory computer-readable storage media of claim 16, wherein the card-on file probability score is based on sub-scores associated with each of the recurring payment factor, the in-app purchase factor, the card-on-file merchant factor, the absent card verification code factor, and the habitual spending factor based on the consumer identifier.