公开(公告)号：US20230298031A1

公开(公告)日：2023-09-21

申请号：US18202516

申请日：2023-05-26

Applicant: STRIPE, INC.

Inventor： Ryan Drapeau ,  Feiyi Ouyang ,  Tianshi Zhu ,  David Abrahams ,  Joshua Rosen

IPC: G06Q20/40 ,  G06Q30/018 ,  G06N20/00

CPC classification number: G06Q20/4016 ,  G06Q30/0185 ,  G06Q20/4014 ,  G06N20/00 ,  H04L67/146

Abstract: A method and apparatus for fraud detection during transactions using identity graphs are described. A method includes receiving, at a commerce platform system, a transaction from a user having initial transaction attributes and transaction data. The method also includes determining, by the commerce platform system, an identity associated with the user associated with additional transaction attributes not received with the transaction. Furthermore, the method includes accessing a feature set associated with the initial transaction attributes and the additional transaction attributes that includes machine learning (ML) model features for detecting transaction fraud. The method also includes 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.

公开(公告)号：US20250094853A1

公开(公告)日：2025-03-20

申请号：US16917624

申请日：2020-06-30

Applicant: Stripe, Inc.

Inventor： Tianshi Zhu ,  Erik Osheim ,  Thomas Switzer ,  Stephanie Bian ,  David Abrahams ,  Susan Tu ,  Patrick Boykin

IPC: G06N20/00 ,  G06Q30/0202

Abstract: A machine learning framework and method for using the same are described. In one embodiment, the method for processing data with a machine learning framework comprises creating a plurality of features as independent features, each feature of the plurality features being based on one or more events that model a plurality of records related to payment processing information, creating a final feature that groups the plurality of features together, such that each feature of the plurality of features represents a sub-feature of the final feature, compiling the plurality of features and the final feature, computing, using a computing platform, each of the plurality of features as a separate job, including sending network related communications to access the payment processing information from one or more remote storage locations, and computing, using a computing platform, the final feature separately from computing the plurality of features, including grouping results of running each of the plurality of features together.

公开(公告)号：US11704673B1

公开(公告)日：2023-07-18

申请号：US16915477

申请日：2020-06-29

Applicant: Stripe, Inc.

Inventor： Ryan Drapeau ,  Feiyi Ouyang ,  Tianshi Zhu ,  David Abrahams ,  Joshua Rosen

IPC: G06N20/00 ,  G06Q20/40 ,  G06Q30/018 ,  H04L67/146 ,  G06Q50/26 ,  G06Q20/34

CPC classification number: G06Q20/4016 ,  G06N20/00 ,  G06Q20/4014 ,  G06Q30/0185 ,  G06Q20/34 ,  G06Q20/4012 ,  G06Q50/265 ,  H04L67/146

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.