US 11,989,740 B2
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 Nov. 23, 2022, as Appl. No. 17/993,758.
Application 17/993,758 is a continuation of application No. 17/080,476, filed on Oct. 26, 2020, granted, now 11,687,938.
Application 17/080,476 is a continuation of application No. 15/465,832, filed on Mar. 22, 2017, granted, now 10,872,339, issued on Dec. 22, 2020.
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.
Prior Publication US 2023/0088436 A1, Mar. 23, 2023
Int. Cl. G06Q 30/01 (2023.01); G06N 5/046 (2023.01); G06N 20/00 (2019.01); G06Q 20/10 (2012.01); G06Q 20/20 (2012.01); G06Q 20/24 (2012.01); G06Q 20/32 (2012.01); G06Q 20/34 (2012.01); G06Q 20/40 (2012.01); G06Q 30/018 (2023.01); G06Q 30/0207 (2023.01); G06Q 30/0241 (2023.01)
CPC G06Q 30/0185 (2013.01) [G06N 5/046 (2013.01); G06N 20/00 (2019.01); G06Q 20/102 (2013.01); G06Q 20/20 (2013.01); G06Q 20/24 (2013.01); G06Q 20/3224 (2013.01); G06Q 20/34 (2013.01); G06Q 20/401 (2013.01); G06Q 20/4016 (2013.01); G06Q 20/407 (2013.01); G06Q 20/409 (2013.01); G06Q 30/0225 (2013.01); G06Q 30/0248 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method, comprising:
retrieving, by a processor, historical account data associated with a plurality of financial accounts, wherein the historical account data includes fraud classification labels that identify different types of fraud that have been determined to be associated with at least one of corresponding transactions or corresponding financial accounts;
generating, by the processor, fraud classification rules by training a machine learning program, based on the historical account data, to identify factors that are predictive of the different types of fraud indicated by the fraud classification labels;
predicting, with the processor, and by applying the fraud classification rules to account data associated with a particular financial account, a preliminary fraud classification corresponding to a transaction associated with the particular financial account, wherein the preliminary fraud classification identifies a particular type of fraud, of the different types of fraud, that is predicted to be associated with the transaction;
receiving, by the processor, a final fraud classification associated with the transaction, wherein the final fraud classification confirms or contradicts the preliminary fraud classification; and
updating, with the processor, the fraud classification rules by re-training the machine learning program based on an additional fraud classification label indicating the final fraud classification.