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The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Patent No.:

US 12236470 B1

PDFFull Text
Date of Patent:
Feb. 25, 2025

Filed:

Jul. 06, 2023

Heuristic money laundering detection engine

Applicant:

State Farm Mutual Automobile Insurance Company, Bloomington, IL (US);

Inventors:

Elizabeth A. Flowers, Bloomington, IL (US);

Puneit Dua, Bloomington, IL (US);

Eric Balota, Bloomington, IL (US);

Shanna L. Phillips, Bloomington, IL (US);

Assignee:

State Farm Mutual Automobile Insurance Company, Bloomington, IL (US);

Attorney:

Lee & Hayes, P.C.

Primary Examiner:

Paul Danneman

Int. Cl.
CPC ...
G06Q 40/12 (2023.01); G06F 40/30 (2020.01); G06N 3/006 (2023.01); G06N 5/01 (2023.01); G06N 5/04 (2023.01); G06N 20/00 (2019.01); G06Q 10/0631 (2023.01); G06Q 20/04 (2012.01); G06Q 20/24 (2012.01); G06Q 20/40 (2012.01); G06Q 30/01 (2023.01); G06Q 30/016 (2023.01); G06Q 30/018 (2023.01); G06Q 30/0601 (2023.01); G06Q 40/02 (2023.01); G06Q 40/03 (2023.01); H04M 3/22 (2006.01); H04M 3/51 (2006.01); H04W 12/08 (2021.01);
U.S. Cl.
CPC ...
G06Q 30/0631 (2013.01); G06F 40/30 (2020.01); G06N 3/006 (2013.01); G06N 5/01 (2023.01); G06N 5/04 (2013.01); G06N 20/00 (2019.01); G06Q 10/06315 (2013.01); G06Q 10/06316 (2013.01); G06Q 20/042 (2013.01); G06Q 20/24 (2013.01); G06Q 20/4016 (2013.01); G06Q 30/01 (2013.01); G06Q 30/018 (2013.01); G06Q 40/02 (2013.01); G06Q 40/03 (2023.01); G06Q 40/12 (2013.12); H04M 3/2218 (2013.01); H04M 3/5175 (2013.01); H04M 3/5191 (2013.01); H04W 12/08 (2013.01); G06Q 30/016 (2013.01); H04M 2203/403 (2013.01);
Abstract

A heuristic money laundering detection engine includes capabilities to collect an unstructured data set, such as a transaction record, and detect indications of money laundering activity. By detecting money laundering activity and feeding back indications of money laundering transactions, the heuristic algorithm may continue to learn and improve detection accuracy. Such indications may include correlations to sets of transaction activity among a number of financial accounts and past indications of money laundering activity. Indications of money laundering may allow generation of audit reports for reporting to regulatory authorities.

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