US 11,855,994 B2
System and method for aggregating client data and cyber data for authentication determinations
Jean-Francois Legault, Brooklyn, NY (US); D. J. Knoedler, Powell, OH (US); Neil Gorin, New York, NY (US); and Kevin Liston, Columbus, OH (US)
Assigned to JPMORGAN CHASE BANK, N.A., New York, NY (US)
Filed by JPMorgan Chase Bank, N.A., New York, NY (US)
Filed on May 21, 2021, as Appl. No. 17/326,898.
Application 17/326,898 is a continuation of application No. 15/684,180, filed on Aug. 23, 2017, granted, now 11,019,063.
Prior Publication US 2021/0288968 A1, Sep. 16, 2021
Int. Cl. H04L 29/06 (2006.01); H04L 9/40 (2022.01); G06Q 20/40 (2012.01); G06N 20/00 (2019.01)
CPC H04L 63/102 (2013.01) [G06N 20/00 (2019.01); G06Q 20/401 (2013.01); G06Q 20/4016 (2013.01); H04L 63/08 (2013.01); H04L 63/0861 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A system that aggregates client data and cyber indicators to authenticate a client, the system comprising:
a memory that stores and maintains a plurality of client profiles; and
a computer server comprising at least one computer processor and coupled to the memory, the computer server programmed to:
receive, via an electronic input, a request to access a financial account of the client, the request comprising a client identifier and a password;
retrieve a client profile, from the memory, based on the client identifier and the password, the client profile including an aggregation of data into a plurality of data indicators;
determine that the request to access the financial account is one of within a predetermined time period or from a geographic location;
determine, in response to the request to access the financial account being within the predetermined time period or from the geographic location, an optimal number of the plurality of data indicators;
generate a risk score based on a weighting for each of the plurality of data indicators included within the optimal number, the weighting representing a confidence in the accuracy of each of those data indicators;
grant access to the financial account of the client in response to the risk score exceeding an account access risk threshold score; and
apply machine learning to the risk score generation, wherein the machine learning (1) analyzes behavior of a requestor and a transaction history to determine if the behavior is outside of an expected behavior range and updates the risk score based on a determination result, and (2) applies feedback to reduce improper denials of authorization requests.