TELECOM SOCIAL NETWORK ANALYSIS DRIVEN FRAUD PREDICTION AND CREDIT SCORING

• US 20140129420A1
• Filed: 11/08/2012
• Published: 05/08/2014
• Est. Priority Date: 11/08/2012
• Status: Abandoned Application

• Current Assignee: Mastercard International Incorporated (MasterCard Incorporated)
• Original Assignee: Mastercard International Incorporated (MasterCard Incorporated)
• Inventors: Howe, Justin Xavier
• Application Number:
  US13/671,982
• Publication Number:
  US 20140129420A1
• Time in Patent Office: Days
• US Class Current: 705/38
• CPC Class Codes: G06Q 40/02 Banking, e.g. interest calculation or account maintenance credit or loans G06Q40/03
• CPC Class Codes: G06Q 50/01 Social networking

Abstract

A method for scoring a user'"'"'s propensity for credit fraud includes forming a social graph from Call Detail Records (“CDR”), the users being nodes and weighted edges connecting node pairs representing a relationship between those users. Initial scores are assigned to users. A first user/credit applicant final score is calculated as a sum of all weighted initial scores of users having a degree of separation of n with the first user, along a path of connecting edges on the social graph, each weighted initial score being a product of the weight of the edges connecting the corresponding node pair, the user initial score, and the inverse square of the degree of separation with the first user. The summation of the degree weighted initial scores of users with degree of separation of n or less is the first user'"'"'s credit-fraud score.

Claims

1. A computer-implemented method for calculating a score indicating a propensity of a person to engage in negative credit practices from telephone call records, the method comprising:

retrieving telephone call data comprising records of telephone calls between users;

forming a social graph from the telephone call data, wherein the users are represented as nodes and an existence of a record of at least one telephone call between a pair of users is represented as an edge connecting a corresponding node pair on the social graph;

determining a strength of a relationship of each of a plurality of second users having a degree of separation of one with a first user using the social graph of records of telephone calls between users;

assigning a weight corresponding to the strength of the relationship to the edge connecting the corresponding node pair;

assigning an initial score to the first user and to each of the plurality of second users, the initial score indicating a propensity for engaging in a negative credit practice, a score of zero indicating a lack of a record of engaging in the negative credit practice; and

determining a score for the first user to engage in the negative credit practice comprising calculating a first degree cumulative score based on the initial scores assigned to the second users having a degree of separation of one and the weight of the edges connecting the corresponding node pairs.

2. The computer-implemented method of claim 1, wherein calculating the first degree cumulative score for the first user comprises:

weighting the initial score assigned to each of the plurality of second users by the corresponding weight of the edge connecting the corresponding node pair to form a plurality of weighted scores for the second users, and

adding the plurality of weighted scores for the second users to calculate the first degree cumulative score.

3. The computer-implemented method of claim 2, wherein determining the score for the first user comprises adding the first degree cumulative score to the initial score for the first user, a higher credit score representing a higher propensity that the first user will engage in the negative credit practice.

4. The computer-implemented method of claim 1, further comprising:

identifying a plurality of third users having a degree of separation of two with the first user using the social graph of records of telephone calls between users, wherein an existence of a record of at least one telephone call between one of the plurality of third users and one of the plurality of second users is represented as an edge connecting a corresponding second node pair on the social graph;

determining a strength of a relationship of each of the plurality of third users with each of the plurality of second users using the records of telephone calls;

assigning a weight corresponding to the strength of the relationship to the edge connecting each of the corresponding second node pairs formed by one of the plurality of third users and one of the plurality of second users;

assigning an initial score to each of the plurality of third users, the initial score indicating a propensity for engaging in a negative credit practice, a score of zero indicating a lack of a record of engaging in the negative credit practice; and

wherein determining the score for the first user to engage in the negative credit practice further comprises calculating a second degree cumulative score based on the initial scores assigned to the third users, the degree of separation between each of the plurality of third users and the first user, and the weight of the edges connecting the corresponding second node pairs.

5. The computer-implemented method of claim 4, wherein calculating the first degree cumulative score for the first user comprises:

weighting the initial score assigned to each of the plurality of second users by the corresponding weight of the edge connecting the corresponding node pair to form a plurality of weighted scores for the second users, and

adding the plurality of weighted scores for the second users to calculate the first degree cumulative score; and

calculating the second degree cumulative score for the first user comprises;

weighting the initial score assigned to each of the plurality of third users by the corresponding weight of the edge connecting the corresponding second node pair and by the inverse of the square of the degree of separation with the first user to form a plurality of weighted scores for the third users; and

adding the plurality of weighted scores for the third users to calculate the second degree cumulative score;

wherein determining the score for the first user comprises adding the first degree cumulative score and the second degree cumulative score to the initial score for the first user, a higher credit score representing a higher propensity that the first user will engage in the negative credit practice.

6. The computer-implemented method of claim 1, further comprising:

identifying a degree of separation n with the first user for a user, where n is greater than 1, using the social graph of records of telephone calls between users, and a path connecting the user having the degree of separation of n with the first user comprising a set of edges connecting the corresponding node pairs on the social graph;

assigning a weight corresponding to a strength of a relationship between a pair of users represented by the corresponding node pair for each of the edges along the path using the records of telephone calls;

assigning an initial score to each user along the path from the user of degree of separation of n and the first user, the initial score indicating a propensity for engaging in a negative credit practice, a score of zero indicating a lack of a record of engaging in the negative credit practice; and

wherein determining the score for the first user to engage in the negative credit practice further comprises calculating a degree-weighted score for each of the users along the path based on the initial score assigned to each user, the degree of separation of each user along the path and the first user, and the weight of the edges connecting the corresponding node pairs along the path.

7. The computer-implemented method of claim 6, wherein calculating the degree-weighted score for each of the users along the path comprises:

weighting the initial score assigned to each of the users along the path by the corresponding weight of the edge connecting the corresponding node pair and by the inverse of the square of the degree of separation of the user along the path with the first user to form a plurality of degree-weighted scores for the users along the path connecting the user with degree of separation n and the first user; and

adding the plurality of degree-weighted scores to the first cumulative score and to the initial score for the first user to calculate the score for the first user, a higher credit score representing a higher propensity that the first user will engage in the negative credit practice.

8. The computer-implemented method of claim 1, wherein the negative credit practice is bust-out fraud.

9. The computer-implemented method of claim 1, wherein the score is an indicator of non-compliant merchant behavior.

10. The computer-implemented method of claim 1, wherein the negative credit practice is bankruptcy and the score is an indicator for predicting bankruptcy.

11. The computer-implemented method of claim 1, wherein the edge is a directed edge directed toward the first user on the social graph, and the weight of the directed edge further reflects a degree of influence of one user over an other user in the corresponding node pair, the one user having a higher degree of separation from the first user than the other user.

12. The computer-implemented method of claim 1, wherein the initial scores indicating a propensity for engaging in the negative credit practice are derived from a credit bureau or credit reporting agency.

13. The computer-implemented method of claim 1, wherein for each pair of users represented by the corresponding node pair on the social graph, the weight corresponding to the strength of the relationship between the pair of users is determined based on at least one of a frequency of calls between the users, a total number of calls, an average call duration, a direction of calls, and an immediacy of a reciprocating call.

14. The computer-implemented method of claim 12, wherein the telephone call data comprising records of telephone calls between users includes an identifying number for each of the users for matching with the initial scores derived from the credit bureau or credit reporting agency for an individual.

15. The computer-implemented method of claim 1, further comprising filtering the telephone call data for forming the social graph by removing at least one of calls that are shorter than a predetermined duration, calls to or from business phone numbers, calls to or from customer service numbers, calls to a user'"'"'s voicemail service, toll-free calls, calls to or from public phones.

16. The computer-implemented method of claim 15, wherein each of the retrieved telephone call data records comprise at least a calling number, a receiving number, a time of call, a call duration, and a geolocation from which the telephone call originated, the method further comprising generating usage statistics for each calling number.

17. The computer-implemented method of claim 16, further comprising generating an initial call history fingerprint for each of the calling numbers comprising a list of phone numbers called, the call duration, frequency, and time of day associated with each phone number called, periodically generating an updated call history fingerprint for each of the calling numbers, and identifying a change of ownership of one of the calling numbers based on a comparison between the initial call history fingerprint and the updated call history fingerprint.

18. The computer-implemented method of claim 16, the method further comprising applying the usage statistics to identify multiple calling numbers used by a single user, assigning the telephone call records associated with the multiple calling numbers to an identification number associated with the single user, wherein one of the nodes of the social graph corresponds to the multiple calling numbers associated with the single user.

19. The computer-implemented method of claim 16, the method further comprising applying the usage statistics to identify pooled numbers, and removing the identified pooled numbers from the records of the telephone call data for forming the social graph.