US 11,741,480 B2
Identifying fraudulent online applications
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 May 27, 2022, as Appl. No. 17/827,015.
Application 17/827,015 is a continuation of application No. 16/913,814, filed on Jun. 26, 2020, granted, now 11,348,122.
Application 16/913,814 is a continuation of application No. 15/465,874, filed on Mar. 22, 2017, granted, now 10,825,028, issued on Nov. 3, 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 2022/0366433 A1, Nov. 17, 2022
This patent is subject to a terminal disclaimer.
Int. Cl. G06Q 30/018 (2023.01); G06N 20/00 (2019.01); G06Q 20/32 (2012.01); G06Q 20/40 (2012.01); G06Q 20/24 (2012.01); G06Q 20/20 (2012.01); G06N 5/046 (2023.01); G06Q 20/10 (2012.01); G06Q 20/34 (2012.01); 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 20/407 (2013.01); G06Q 20/409 (2013.01); G06Q 20/4016 (2013.01); G06Q 30/0225 (2013.01); G06Q 30/0248 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method of identifying fraudulent online applications, the method comprising:
receiving, by a computer system, applicant data associated with an online application;
determining, by the computer system, an IP address of a source computer associated with the online application;
training a machine learning model to determine an online application fraud detection rule, based on historical online application data and corresponding online search activity;
determining, by the computer system, using the online application fraud detection rule, whether online activity associated with the IP address indicates that the online application is potentially fraudulent; and
in response to determining that the online activity indicates that the online application is potentially fraudulent, performing, by the computer system, a fraud mitigation action including at least one of:
flagging the online application as potentially fraudulent; or
generating an electronic alert indicating that the online application is potentially fraudulent.