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.

Date of Patent:
May. 16, 2023

Filed:

Jan. 07, 2020
Applicant:

Visa International Service Association, San Francisco, CA (US);

Inventors:

Michael Hsiang Tsung, Lafayette, CA (US);

Tselmeg Otgonbayar Shponko, San Francisco, CA (US);

Ratinder Bedi, San Ramon, CA (US);

Girish Ramachandran, Milpitas, CA (US);

Martin Charles Elliott, Redwood City, CA (US);

Assignee:

Visa International Service Association, San Francisco, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 20/00 (2019.01); G06Q 50/26 (2012.01); G06Q 20/40 (2012.01); G06N 20/20 (2019.01); G06Q 30/018 (2023.01); G06F 18/214 (2023.01); G06F 18/2413 (2023.01); G06F 18/243 (2023.01);
U.S. Cl.
CPC ...
G06Q 30/0185 (2013.01); G06F 18/214 (2023.01); G06F 18/24147 (2023.01); G06F 18/24323 (2023.01); G06N 20/00 (2019.01); G06N 20/20 (2019.01); G06Q 20/4016 (2013.01); G06Q 30/018 (2013.01); G06Q 50/265 (2013.01);
Abstract

Methods and systems are described. A method includes accessing training data samples that includes a plurality of transaction laundering associated features and generating random samples, training a first plurality of different models to identify transaction laundering merchants based on random samples generated from each of the training data samples, training a second plurality of different models to identify the transaction laundering merchants based on the training data, generating a transaction laundering classification for a merchant from each of the first plurality of models and each of the second plurality of models, generating a first model group classification based on a first majority vote on transaction laundering classifications from the first plurality of models, and generating a second model group classification based on a second majority vote on transaction laundering classifications from the second plurality of models. A likelihood that the merchant is a transaction launderer is determined.


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