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:
Jul. 18, 2023

Filed:

Jun. 29, 2020
Applicant:

Stripe, Inc., San Francisco, CA (US);

Inventors:

Ryan Drapeau, Seattle, WA (US);

Feiyi Ouyang, Seattle, WA (US);

Tianshi Zhu, San Francisco, CA (US);

David Abrahams, San Francisco, CA (US);

Joshua Rosen, Berkeley, CA (US);

Assignee:

Stripe, Inc., San Francisco, CA (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06N 20/00 (2019.01); G06Q 20/40 (2012.01); G06Q 30/018 (2023.01); H04L 67/146 (2022.01); G06Q 50/26 (2012.01); G06Q 20/34 (2012.01);
U.S. Cl.
CPC ...
G06Q 20/4016 (2013.01); G06N 20/00 (2019.01); G06Q 20/4014 (2013.01); G06Q 30/0185 (2013.01); G06Q 20/34 (2013.01); G06Q 20/4012 (2013.01); G06Q 50/265 (2013.01); H04L 67/146 (2013.01);
Abstract

A method and apparatus for fraud detection during transactions using identity graphs are described. The method may include receiving, at a commerce platform system, a transaction from a user having initial transaction attributes and transaction data. The method may also include determining, by the commerce platform system, an identity associated with the user, wherein the identity is associated with additional transaction attributes not received with the transaction. Furthermore, the method may include accessing, by the commerce platform system, a feature set associated with the initial transaction attributes and the additional transaction attributes, wherein the feature set comprises machine learning (ML) model features for detecting transaction fraud. The method may also include performing, by the commerce platform system, a machine learning model analysis using the feature set and the transaction data to determine a likelihood that the transaction is fraudulent, and performing, by the commerce platforms system, the transaction when the likelihood that the transaction is fraudulent does not satisfy a transaction fraud threshold.


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