# US 10,313,388 B2 · Risk assessment using social networking data > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,313,388 B2 - Document ID: US10313388B2 - Status: Granted - Kind: B2 - Application number: 15/907,721 - Filed: 2018-02-28 - Published: 2019-01-10 - Granted: 2019-06-04 - Earliest claimed: 2013-03-15 - Assignees: Socure, Inc. - Applicants: Socure Inc. - Inventors: Sunil Madhu; Giacomo Pallotti; Edward J. Romano; Alexander K. Chavez - Web page: https://patentgrep.com/patent/US10313388B2 ## CPC - H04L 63/12: {Applying verification of the received information (cryptographic mechanisms or cryptographic arrangements for data integrity or data verification H04L9/32)} - G06F 16/24578: {using ranking} - G06F 16/951: Indexing; Web crawling techniques - G06F 21/577: {Assessing vulnerabilities and evaluating computer system security} - G06Q 10/42: Determination of affinities or common interests between users - G06Q 10/48: using social graphs - G06Q 20/4016: {involving fraud or risk level assessment in transaction processing} - G06Q 50/265: {Personal security, identity or safety} - H04L 51/52: for supporting social networking services - H04L 63/1408: {by monitoring network traffic (monitoring network traffic per se H04L43/00)} - H04L 63/1433: {Vulnerability analysis} - H04L 63/1483: {service impersonation, e.g. phishing, pharming or web spoofing (detection of rogue wireless access points H04W12/12)} - H04L 67/306: User profiles - H04L 67/52: specially adapted for the location of the user terminal - H04L 67/535: {Tracking the activity of the user (network monitoring arrangements H04L43/00; recording of computer activity G06F11/34)} - H04W 4/21: for social networking applications - H04W 12/12: Detection or prevention of fraud - H04W 12/67: Risk-dependent, e.g. selecting a security level depending on risk profiles - H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M) - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR - H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72) ## 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 ### Claim 1 A computer-implemented method for calculating a risk score for an entity account, the method comprising: (a) calculating, by an electronic processor of a computer system, an entity score in response to at least a portion of profile data associated with at least one online or mobile network of a first entity account, wherein the calculation comprises comparing at least a portion of the profile data of the first entity account to at least one reference profile model associated with at least one online or mobile network, the at least one reference profile model including a fake profile; (b) 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 online or mobile network; (c) calculating, via the processor, a risk score for the first entity account based on the calculated entity score and the calculated connections score; and (d) determining, by the processor, an accuracy of the risk score for the first entity by comparing the calculated risk score to an average score range for at least one cluster of other entities, to improve the at least one reference profile model; and (e) generating, by the processor, a dashboard, the dashboard including an alert based on the risk score. ### Claim 2 (depends on 1) The method of claim 1, wherein the profile data comprise at least one of: entity contact information, friend's data, birth data, network data, geolocation data, image data, video data, or timeline activity associated with at least one entity account. ### Claim 3 (depends on 1) The method of claim 1, wherein calculating the entity score further comprises processing at least one activity feed of at least the first entity account by calculating a frequency of posting. ### Claim 4 (depends on 3) 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 entity account. ### Claim 5 (depends on 1) The method of claim 1, wherein calculating the entity score further comprises checking an identity of one or more entity accounts making a post to distinguish between a post by the first entity account and a post by at least a second entity account connected to the first entity. ### Claim 6 (depends on 5) The method of claim 5, further comprising classifying at least one post into a category based on content of the post. ### Claim 7 (depends on 5) The method of claim 5, further comprising calculating a number of interactions associated with at least one posted item. ### Claim 8 (depends on 5) The method of claim 5, further comprising collecting metadata associated with at least one post including a location accessed by at least the first entity account. ### Claim 9 (depends on 1) The method of claim 1, wherein calculating the entity score further comprises processing at least one attribute of at least one application installed on at least the first entity account. ### Claim 10 (depends on 1) The method of claim 1, wherein calculating the entity score further comprises comparing at least a portion of the profile data of the first entity to multiple reference profile models associated with multiple networks. ### Claim 11 (depends on 1) The method of claim 1, wherein calculating the connections score comprises processing data associated with connections formed between the first entity account and at least one account associated with a friend, family member, follower, owner, or peer of the first entity account. ### Claim 12 (depends on 1) The method of claim 1, further comprising communicating at least one alert to an entity account in association with at least one of the calculated scores. ### Claim 13 (depends on 1) The method of claim 1, wherein calculating the risk score includes combining data associated with at least one transaction involving the first entity account or the second entity account. ### Claim 14 (depends on 13) The method of claim 13, wherein the transaction comprises a registration transaction, an authentication transaction, an authorization transaction, a payment transaction, a remittance transaction, a cryptographic blockchain transaction, a purchase transaction, or a combination thereof. ### Claim 15 (depends on 1) The method of claim 1, further comprising communicating at least one of the calculated scores to an enterprise using an application program interface. ### Claim 16 (depends on 15) The method of claim 15, wherein the enterprise comprises a business, an organization, or a combination thereof. ### Claim 17 (depends on 1) The method of claim 1, further comprising calculating a risk score for at least the second entity account. ### Claim 18 (depends on 1) The method of claim 1, wherein the dashboard is configured to display at least one of: a) a number of transactions performed by one or more entity accounts; b) at least one risk score associated with a plurality of entity accounts; c) at least one risk score in association with a social network, a professional network, an online network, a domain, a demographic characteristic, a psychographic characteristic, or a combination thereof; and d) a graphical representation associated with at least one of the scores or a combination of multiple scores. ### Claim 19 A computer-implemented system for calculating a risk score for an entity account, the system comprising: a) at least one processor, an operating system configured to perform executable instructions, and a memory; b) a computer program including instructions executable by the at least one processor to create an application comprising: i) a software module configured to calculate an entity score in response to at least a portion of profile data associated with at least one online or mobile network of a first entity account, wherein the calculation comprises comparing at least a portion of the profile data of the first entity account to at least one reference profile model associated with at least one online or mobile network, the at least one reference profile model including a fake profile; ii) a software module configured to calculate a connections score in response to at least one connection formed between the first entity account and at least a second entity account through the online or mobile network; iii) a software module configured to calculate a risk score for the first entity account based on the calculated entity score and the calculated connections score; iv) a software module configured to determine accuracy of the risk score for the first entity by comparing the calculated risk score to an average score range for at least one cluster of other entities, to improve the at least one reference profile model; and vi) a software module configured to provide a dashboard, the dashboard configured to display an alert based on the risk score. ### Claim 20 A non-transitory computer-readable storage medium storing computer-executable instructions to: calculate an entity score in response to at least a portion of profile data associated with at least one online or mobile network of a first entity account, the calculation including comparing at least a portion of the profile data of the first entity account to at least one reference profile model associated with at least one online or mobile network, the at least one reference profile model including a fake profile; calculate a connections score in response to at least one connection formed between the first entity account and at least a second entity account through the online or mobile network; calculate a risk score for at least the first entity account based on the calculated entity score and the calculated connections score; determine accuracy of the risk score for the first entity by comparing the calculated risk score to an average score range for at least one cluster of other entities, to improve the at least one reference profile model; and provide a dashboard, the dashboard configured to display at least one alert based on the risk score.