System and method for handling gamification fraud

• US 9,659,303 B2
• Filed: 03/26/2014
• Issued: 05/23/2017
• Est. Priority Date: 04/03/2013
• Status: Active Grant

• Current Assignee: Salesforce.com, Inc.
• Original Assignee: Salesforce.com, Inc.
• Inventors: Orun, Mehmet, Fuchs, Matthew
• Primary Examiner(s): Abrahamson, Amanda
• Assistant Examiner(s): Anderson, Scott C
• Application Number:
  US14/226,475
• Publication Number:
  US 20140304054A1
• Time in Patent Office: 1,154 Days
• CPC Class Codes: G06Q 30/0225 Avoiding frauds

Abstract

Some embodiments of the present invention include determining if updates performed by a second user include a systematic change such as a reversal of an update previously performed by a first user within a time window. The reversal is associated with a record of data used by a gamification application executing in a computer system. A time delay is introduced between the update performed by the second user and rewarding the second user if the update performed by the second user includes the reversal within the time window. An update history of the first user and the second user is evaluated to identify pattern of reversals associated with similar records within the time window. The second user is prevented from being rewarded based on identifying that there are patterns of reversals from the update history occurring within the time window.

Claims

1. A computer-implemented method for handling gamification fraud, the method including:

receiving, by a database system, a first update to a record in a database by a first user;

receiving, by the database system, a second update to the record by a second user;

evaluating, by the database system, the second update to the record made by the second user to determine that the second update is a systematic change of the first update to the record previously made by the first user;

introducing, by the database system, a time delay between the update made by the second user and an issuance of a reward to the second user based upon the determination that the second update performed made by the second user appears to be a systematic change;

evaluating, by the database system, update history of the first user and update history of the second user to identify that the second user has made corresponding systematic changes to updates of the first user to similar records; and

preventing, by the database system, the issuance of the reward to the second user at the expiration of the time delay based upon identifying that the second user has made corresponding systematic changes to updates of the first user to similar records.

2. The method of claim 1, wherein the first update performed by the first user comprises multiple changes to multiple records, and wherein the second update performed by the second user comprises multiple systematic changes to the same multiple records.

3. The method of claim 1, wherein evaluating the update history of the first user and the update history of the second user comprises using a clustering technique to identify data clusters from the update histories by pairing the first user with the second user.

4. The method of claim 3, wherein evaluating the update history of the first user and the update history of the second user further comprises applying a time window to the data clusters to identify fraudulent activities by the first user and the second user.

5. The method of claim 4, wherein the clustering technique is a statistical analysis technique to identify the data clusters.

6. The method of claim 5, wherein the systematic change performed by the second user includes a reversal update, and wherein the clustering technique is Hadoop technique.

7. The method of claim 1, wherein evaluating the update history of the first user and the second user comprises using a flattening technique to combine related records from the update history to determine collaborative fraud committed by the first user and the second user.

8. An apparatus for handling gamification fraud, the apparatus comprising:

one or more processors; and

a non-transitory computer readable medium storing a plurality of instructions, which when executed, cause the one or more processors to;

receive a first update to a record in a database by a first user;

receive a second update to the record by a second user;

evaluate the second update to the record made by the second user to determine that the second update is a systematic change of the first update to the record previously made by the first user;

introduce a time delay between the update made by the second user and an issuance of a reward to the second user based upon the determination that the second update made by the second user appears to be a systemic change;

evaluate update history of the first user and update history of the second user to identify that the second user has made corresponding systematic changes to updates of the first user to similar records; and

prevent the issuance of the reward to the second user at the expiration of the time delay based upon identifying that the second user has made corresponding systematic changes to updates of the first user to similar records.

9. The system of claim 8, wherein the first update performed by the first user comprises multiple changes to multiple records, and wherein the second update performed by the second user comprises multiple systematic changes to the same multiple records.

10. The system of claim 8, wherein the update history of the first user and the update history of the second user comprises are evaluated using a clustering technique to identify data clusters from the update histories by pairing the first user with the second user.

11. The system of claim 10, wherein the update history of the first user and the update history of the second user further are evaluated by applying a time window to the data clusters to identify fraudulent activities by the first user and the second user.

12. The system of claim 11, wherein the clustering technique is a statistical analysis technique to identify the data clusters.

13. The system of claim 12, wherein the systematic change performed by the second user includes a reversal update, and wherein the clustering technique is Hadoop technique.

14. The system of claim 8, wherein the update history of the first user and the update history of the second user comprises are evaluated using a flattening technique to combine related records from the update history to determine collaborative fraud committed by the first user and the second user.

15. A computer program product, comprising a non-transitory computer-readable medium having a computer-readable program code embodied therein to be executed by one or more processors, the program code including instructions to:

receive a first update to a record in a database by a first user;

receive a second update to the record by a second user;

evaluate the second update to the record made by the second user to determine that the second update is a systematic change of the first update to the record previously made by the first user;

introduce a time delay between the update made by the second user and an issuance of a reward to the second user based upon the determination that the second update made by the second user appears to be a systemic change;

evaluate update history of the first user and update history of the second user to identify that the second user has made corresponding systematic changes to updates of the first user to similar records; and

prevent the issuance of the reward to the second user at the expiration of the time delay based upon identifying that the second user has made corresponding systematic changes to updates of the first user to similar records.

16. The computer program product of claim 15, wherein the update performed by the first user comprises multiple changes to multiple records, and wherein the update performed by the second user comprises multiple systematic changes to the same multiple records.

17. The computer program product of claim 15, wherein evaluating the update history of the first user and the update history of the second user comprises using a clustering technique to identify data clusters from the update history by pairing the first user with the second user.

18. The computer program product of claim 17, wherein evaluating the update history of the first user and the update history of the second user further comprises applying a time window to the data clusters to identify fraudulent activities by the first user and the second user.

19. The computer program product of claim 18, wherein the clustering technique is a statistical analysis technique to identify the data clusters.

20. The computer program product of claim 15, wherein evaluating the update history of the first user and the update history of the second user comprises using a flattening technique to combine related records from the update history to determine collaborative fraud committed by the first user and the second user.