US 11,687,937 B1
Reducing false positives using customer data 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. 23, 2020, as Appl. No. 17/78,744.
Application 17/078,744 is a continuation of application No. 15/465,827, filed on Mar. 22, 2017, granted, now 10,832,248.
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.
This patent is subject to a terminal disclaimer.
Int. Cl. G06Q 20/40 (2012.01); G06Q 20/32 (2012.01)
CPC G06Q 20/4016 (2013.01) [G06Q 20/3224 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method for detecting whether electronic fraud alerts are false positives prior to transmission to customer mobile devices based upon customer data, the method comprising:
training a machine learning program using fraud classifications made in connection with at least one of a type of transaction data or a value of transaction data associated with a plurality of financial accounts, such that the machine learning program learns a characteristic of the transaction data that is indicative of different fraud classifications, wherein the different fraud classifications include a lost or stolen card, an account takeover, a counterfeit card, or an application fraud;
receiving, by one or more processors, transaction data detailing a financial transaction associated with a customer;
determining, by the one or more processors and based at least in part on the transaction data, that an electronic fraud alert is to be generated for the financial transaction;
determining, by the machine learning program, and based at least in part on a type of the transaction data or a value of the transaction data, a reason why the electronic fraud alert was generated, wherein the reason determined by the machine learning program includes at least one of:
an inconsistency between a location associated with the customer and a transaction location, at a time associated with the financial transaction; or
an unusual merchant or unusual item purchased associated with the financial transaction;
determining, by the one or more processors, that the reason why the electronic fraud alert was generated can be verified by customer data;
receiving, by the one or more processors, customer data;
verifying, by the one or more processors and based at least in part on the customer data, that the electronic fraud alert is not a false positive; and
transmitting the electronic fraud alert to a mobile device of the customer.