Risk assessment using social networking data

• US 9,300,676 B2
• Filed: 03/17/2014
• Issued: 03/29/2016
• Est. Priority Date: 03/15/2013
• Status: Active Grant

• Current Assignee: Socure, Inc.
• Original Assignee: Socure, Inc.
• Inventors: Madhu, Sunil, Pallotti, Giacomo, Romano, Edward J., Chavez, Alexander K.
• Primary Examiner(s): LEWIS, LISA C
• Application Number:
  US14/215,477
• Publication Number:
  US 20140282977A1
• Time in Patent Office: 743 Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06F 21/577 Assessing vulnerabilities and evaluating computer system security
• CPC Class Codes: G06Q 20/4016 involving fraud or risk level assessment in transaction processing
• CPC Class Codes: G06Q 50/01 Social networking
• CPC Class Codes: G06Q 50/265 Personal security, identity or safety
• CPC Class Codes: H04L 51/52 for supporting social networking services
• CPC Class Codes: H04L 63/12 Applying verification of the received information cryptographic mechanisms or cryptographic arrangements for data integrity or data verification H04L9/32
• CPC Class Codes: H04L 63/1408 by monitoring network traffic monitoring network traffic per se H04L43/00
• CPC Class Codes: H04L 63/1433 Vulnerability analysis
• CPC Class Codes: H04L 63/1483 service impersonation, e.g. phishing, pharming or web spoofing detection of rogue wireless access points H04W12/12
• CPC Class Codes: H04L 67/306 User profiles
• CPC Class Codes: H04L 67/52 specially adapted for the location of the user terminal
• CPC Class Codes: H04L 67/535 Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34
• CPC Class Codes: H04W 12/12 Detection or prevention of fraud
• CPC Class Codes: H04W 12/67 Risk-dependent, e.g. selecting a security level depending on risk profiles
• CPC Class Codes: H04W 4/21 for social networking applications

Abstract

Tools, strategies, and techniques are provided for evaluating the identities of different entities to protect individual consumers, business enterprises, and other organizations from identity theft and fraud. Risks associated with various entities can be analyzed and assessed based on analysis of social network data, professional network data, or other networking connections, among other data sources. In various embodiments, the risk assessment may include calculating an authenticity score based on the collected network data.

Claims

1. A computer-implemented method for calculating an authenticity score for a user account, the method comprising:

calculating, by an electronic processor of a computer system, a user score in response to at least a portion of profile data associated with at least one social network or professional network of a first user account, wherein the calculation comprises comparing at least a portion of the profile data of the first user account to at least one fake profile model associated with at least one social network or professional network;

calculating, by the processor, a connections score in response to at least one connection formed between the first user account and at least a second user account through the social network or professional network;

calculating, by the processor, an affinity score in response to an overlap between at least a portion of the profile data of the first user account and at least a portion of profile data associated with at least a second user account;

combining, by the processor, the calculated user score, the calculated connections score, and the calculated affinity score to yield an authenticity score for at least the first user account; and

determining, by the processor, accuracy of the authenticity score for the first user by comparing the calculated authenticity score against an average score range for at least one cluster of other users with similar affinity scores, wherein the accuracy is used to improve the at least one fake profile model.

2. The method of claim 1, wherein the profile data comprise at least one of user contact information, friends data, birth data, or timeline activity data associated with at least one user account.

3. The method of claim 1, wherein calculating the user score further comprises processing at least one activity feed of at least the first user account by calculating a frequency of posting.

4. The method of claim 3, wherein processing the activity feed includes identifying a baseline posting frequency and a burst usage pattern for at least the first user account.

5. The method of claim 1, wherein calculating the user score further comprising checking an identity of one or more user accounts making a post to distinguish between a post by the first user account and a post by at least a second user account connected to the first user.

6. The method of claim 5, further comprising classifying at least one post into a category based on content of the post.

7. The method of claim 5, further comprising calculating a number of social interactions associated with at least one posted item.

8. The method of claim 5, further comprising collecting metadata associated with at least one post including a location accessed by at least the first user account.

9. The method of claim 8, further comprising using the collected location metadata to track one or more frequently traveled locations of at least the first user account.

10. The method of claim 1, wherein calculating the user score further comprising processing at least one attribute of at least one application installed on at least the first user account.

11. The method of claim 1, wherein calculating the user score further comprising comparing at least a portion of the data of the first user to multiple fake profile models associated with multiple networks.

12. The method of claim 1, wherein calculating the connections score includes processing data associated with connections formed between the first user account and at least one account associated with a friend, family member, follower, or peer of the first user account.

13. The method of claim 1, wherein calculating the affinity score includes computing the overlap between at least one profile attribute, activity, installed application, or connection of the first user account and at least one profile attribute, activity, installed application, or connection of the second user account.

14. The method of claim 1, further comprising communicating at least one alert to a user account in association with at least one of the calculated scores.

15. The method of claim 1, wherein calculating the authenticity score includes combining data associated with at least one transaction involving the first user account or the second user account.

16. The method of claim 15, wherein the transaction involves at least one of a user registration transaction, an authentication transaction, an authorization transaction, a payment transaction, a remittance transaction, or a purchase transaction.

17. The method of claim 1, further comprising communicating at least one of the calculated scores to an enterprise using an application program interface.

18. The method of claim 17, wherein the enterprise includes at least one of a business, an organization, a financial institution, a merchant, a retailer, or an educational institution.

19. The method of claim 1, further comprising calculating an authenticity score for at least the second user account.

20. The method of claim 1, further comprising generating an enterprise-based dashboard programmed to display:

a number of transactions performed by one or more user accounts;

at least one authenticity score associated with a plurality of user accounts;

at least one authenticity score in association with at least one of a social network, a professional network, a domain, or a demographic characteristic; and

,

a graphical display including a representation associated with at least one of the scores or a combination of multiple scores.

21. A computer-implemented system for calculating an authenticity score for a user account, the system comprising:

at least one processor, an operating system configured to perform executable instructions, and a memory;

a computer program including instructions executable by the at least one processor to create an application comprising;

a software module calculating a user score in response to at least a portion of profile data associated with at least one social network or professional network of a first user account, wherein the calculation comprises comparing at least a portion of the profile data of the first user account to at least one fake profile model associated with at least one social network or professional network;

a software module calculating a connections score in response to at least one connection formed between the first user account and at least a second user account through the social network or the professional network;

a software module calculating an affinity score in response to an overlap between at least a portion of the profile data of the first user account and at least a portion of profile data associated with at least a second user account;

a software module combining the calculated user score, the calculated connections score, and the calculated affinity score to yield an authenticity score for at least the first user account; and

a software module determining accuracy of the authenticity score for the first user by comparing the calculated authenticity score against an average score range for at least one cluster of other users with similar affinity scores, wherein the accuracy is used to improve the at least one fake profile model.