Computerized-methods and systems for identifying duplicate entries in a database of merchant data

• US 10,319,042 B2
• Filed: 10/20/2015
• Issued: 06/11/2019
• Est. Priority Date: 10/20/2015
• Status: Active Grant

• Current Assignee: Mastercard International Incorporated (MasterCard Incorporated)
• Original Assignee: Mastercard International Incorporated (MasterCard Incorporated)
• Inventors: Arvapally, Ravi Santosh, Yang, Peng
• Primary Examiner(s): Goyea, Olusegun
• Application Number:
  US14/887,512
• Publication Number:
  US 20170109831A1
• Time in Patent Office: 1,330 Days
• Field of Search: 705 729, 705 39, 705 44, 705 30, 705 1423, 705 1453, 705 1447
• CPC Class Codes: G06Q 40/12 Accounting

Abstract

Systems and methods are provided for verifying and/or confirming merchant data for multiple different transactions. One method generally includes accessing merchant data for a merchant where the merchant data includes a master merchant identifier for the merchant, and querying, by a computing device, multiple users as to which of a list of merchant data entries, for multiple different transactions and including different merchant identifiers, corresponds to the master merchant identifier for the merchant. The users are associated with a location within a predefined distance of the confirmed merchant and/or have performed one or more purchase transactions at the merchant. The method also generally includes receiving, at the computing device, a response from at least one of the users, in which the at least one of the users selects a data entry from the list, and determining whether to proceed in querying additional users.

Claims

1. A computer-implemented method for use in verifying merchant data for a merchant, in connection with transaction data stored in a payment network, the method comprising:

accessing a master merchant identifier for the merchant;

querying, by a computing device, multiple users as to which of a list of merchant data entries included in transaction data for multiple different transactions corresponds to the master merchant identifier for the merchant, wherein the users are associated with a location within a predefined distance of the merchant and/or have performed one or more purchase transactions at the merchant;

receiving, at the computing device, a response from at least one of the users, in which the at least one of the users selects a merchant data entry from the list;

determining a score indicative of a probability that at least one of the merchant data entries on the list corresponds accurately to the merchant, based at least in part on the response from the at least one of the users; and

querying one or more additional users as to which of the list of merchant data entries corresponds to the master merchant identifier for the merchant, when the determined score fails to satisfy a predefine threshold.

2. The computer-implemented method of claim 1, wherein querying the one or more additional users includes causing the master merchant identifier for the merchant to be displayed to the one or more additional users, along with the list of merchant data entries.

3. The computer-implemented method of claim 2, wherein the master merchant identifier for the merchant includes an address for the merchant and a name of the merchant.

4. The computer-implemented method of claim 3, wherein querying the one or more additional users includes querying the one or more additional users when a pre-specified maximum number of queries have not already been submitted to the multiple users.

5. The computer-implemented method of claim 2, further comprising:

receiving, at the computing device, a response from at least one of the one or more additional users, in which the at least one of the one or more additional users selects a merchant data entry from the list; and

calculating an updated score indicative of a probability that at least one of the data entries on the list corresponds accurately to the merchant, based at least in part on the response from the at least one of the users and a response received from the at least one of the one or more additional users.

6. The computer-implemented method of claim 5, wherein the updated score is calculated based on at least an internal matching score and the response received from the at least one of the one or more additional users.

7. The computer-implemented method of claim 5, further comprising updating a location designation value for the merchant in a database of merchant data when the score satisfies a score threshold.

8. The computer-implemented method of claim 5, wherein the method further comprises:

providing, by the computing device, verification questions to the multiple users and the one or more additional users; and

either;

discarding the responses from the multiple users and the one or more additional users when the users'"'"' responses to the verification questions are incorrect;

or

providing, by the computing device, additional verification questions to the multiple users and the one or more additional users, when the users'"'"' responses to the verification questions are incorrect.

13. The method of claim 1, wherein determining the score indicative of the probability that at least one of the merchant data entries on the list corresponds accurately to the merchant includes calculating a probability match score consistent with the following equation:

9. A system for use in identifying duplicate merchant locations in payment network transaction data, the system comprising:

a payment network including at least one processor coupled to a memory, the memory having a transaction data structure including transaction data for a plurality of transactions involving a plurality of merchants and at least one location identifier for each of the plurality of merchants, the at least one processor of the payment network configured to;

provide one or more services to users registered with the payment network, wherein the one or more services are network-accessible to each of the users; and

for each of the plurality of transactions in the transaction data structure;

receive an authorization request for the transaction from a merchant via an acquirer, the authorization request including a merchant location identifier for the merchant; and

store the merchant location identifier for the merchant in the transaction data structure as part of the transaction data for the transaction; and

a verification engine including at least one processor coupled to a memory, the memory of the verification engine including a merchant data structure comprising a master location for each of the plurality of merchants involved in the plurality of transactions in the transaction data structure, the at least one processor of the verification engine configured to;

identify a merchant of the plurality of merchants for audit;

determine, from the merchant data structure, the master location for the identified merchant for audit;

identify, from the plurality of merchants involved in the plurality of transactions in the transaction data structure, a plurality of close-match merchants for the identified merchant for audit, based on a similarity of the merchant location identifiers in the transaction data in the transaction data structure for each of the plurality of close-match merchants to the determined master location for the identified merchant for audit;

identify a plurality of the users registered with the payment network for crowd sourcing, based on at least one of;

a location associated with the user being within a predefined distance from at least one of the merchant location identifiers for the identified plurality of close-match merchants, a location associated with the user being within a predefined distance from the determined master location for the identified merchant for audit, and/or the user having performed one or more purchase transactions at the identified merchant for audit;

query at least some of the identified plurality of users, in connection with a Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA), as to which of the merchant location identifiers for the identified plurality of close-match merchants is associated with the master location of the identified merchant for audit, thereby enabling the crowd sourcing in connection with verifying, by the CAPTCHA, that responses to the queries are not provided by automated computers;

receive responses from the queried users selecting ones of the merchant location identifiers for the identified plurality of close-match merchants as associated with the master location of the identified merchant for audit;

calculate a score indicative of a probability that at least one of the merchant location identifiers, for the identified plurality of close-match merchants, corresponds accurately to the determined master location of the identified merchant for audit, based on the responses from the queried users;

compare the calculated score to a threshold value,

when the calculated score satisfies the threshold value, update a location designation value for the identified merchant for audit in the merchant data structure;

when the calculated score does not satisfy the threshold value, query additional ones of the identified plurality of users, in connection with the CAPTCHA, as to which of the merchant location identifiers for the identified plurality of close-match merchants is associated with the master location of the identified merchant for audit;

receive responses from the queried additional users selecting ones of the merchant location identifiers for the identified plurality of close-match merchants as associated with the master location of the identified merchant for audit; and

update the score based on the responses from the queried additional users and, when the updated score satisfies the threshold value, update the location designation value for the identified merchant for audit in the merchant data structure.

10. The system of claim 9, wherein, in connection with calculating the score and/or updating the score, the at least one processor of the verification engine is configured to:

determine which merchant location identifier has been selected most often in the responses received from the queried users;

calculate a probability that the most selected merchant location identifier has achieved a most selected status by random chance; and

calculate the score based on the probability and an internal matching score for the responses received from the queried users.

11. The system of claim 10, wherein the at least one processor of the verification engine is further configured to mark the location designation value for the identified merchant for audit as unconfirmed in the merchant data structure, when the calculated score and the updated score do not satisfy the threshold value after a maximum allowed number of responses is received.

12. The system of claim 10, wherein the at least one processor of the verification engine is further configured to:

provide at least one verification question to each of the queried users and/or the queried additional users; and

either;

for each of the queried users and/or the queried additional users, discard the response from the user when the response from the user to the at least one verification question is incorrect;

or

for each of the queried users and/or the queried additional users, provide at least one additional verification question when the response from the user to the at least one verification question is incorrect.