US 11,687,938 B1
Reducing false positives using customer feedback and machine learning
Timothy Kramme, Parker, TX (US); Elizabeth A. Flowers, Bloomington, IL (US); Reena Batra, Alpharetta, GA (US); Miriam Valero, Bloomington, IL (US); Puneit Dua, Bloomington, IL (US); Shanna L. Phillips, Bloomington, IL (US); Russell Ruestman, Minonk, IL (US); and Bradley A. Craig, Normal, IL (US)
Assigned to State Farm Mutual Automobile Insurance Company, Bloomington, IL (US)
Filed by State Farm Mutual Automobile Insurance Company, Bloomington, IL (US)
Filed on Oct. 26, 2020, as Appl. No. 17/80,476.
Application 17/080,476 is a continuation of application No. 15/465,832, filed on Mar. 22, 2017, granted, now 10,872,339.
Claims priority of provisional application 62/365,699, filed on Jul. 22, 2016.
Claims priority of provisional application 62/331,530, filed on May 4, 2016.
Claims priority of provisional application 62/318,423, filed on Apr. 5, 2016.
Claims priority of provisional application 62/313,196, filed on Mar. 25, 2016.
Int. Cl. G06Q 20/40 (2012.01); G06Q 20/24 (2012.01); G06N 20/00 (2019.01); G06Q 20/32 (2012.01)
CPC G06Q 20/4016 (2013.01) [G06N 20/00 (2019.01); G06Q 20/24 (2013.01); G06Q 20/3224 (2013.01); G06Q 20/409 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method for reducing false positive electronic fraud alerts, the method comprising:
receiving data detailing a first financial transaction associated with a customer;
generating, by a rules-based engine, based at least in part on the data detailing the first financial transaction, an electronic fraud alert;
transmitting the electronic fraud alert to a device of the customer;
receiving customer feedback from the device of the customer, the customer feedback indicating that the electronic fraud alert was a false positive;
generating, by one or more processors and based at least in part on the data detailing the first financial transaction, an updated rules-based engine to facilitate reducing an amount of false positive fraud alerts generated in the future, wherein generating the updated rules-based engine comprises:
providing, to a trained machine learning program, input data associated with the first financial transaction;
determining, based on an output of the machine learning program, a reason why the false positive was generated; and
modifying a first rule of the rules-based engine, based on the determined reason why the false positive was generated; and
evaluating, by the one or more processors using the updated rules-based engine, a second financial transaction.