Stripe, Inc.

Ryan Drapeau

Feiyi Ouyang

Andrew Bullen

Joseph Patrick Kirwin

Leela Senthil Nathan

Arash Rai

Joshua Rosen

Kefeng Chen

Ryan Koven

David Abrahams

Tianshi Zhu

Mathieu Hofman

Systems and methods for smart remediation of transactions are disclosed. A payment processing system receives information of a first transaction with a merchant. The system approves the first transaction and identifies a monitoring period which may be a set amount of time after the approval of the first transaction. The system collects data of one or more second transactions during the monitoring period, and provides the data to a machine learning model. The machine learning model determines a first fraud score indicative of fraud for the first transaction. A fraud threshold may be identified based on one or more merchant profiles. If the first fraud score is above the first fraud threshold, a refund may be provided for the first transaction.

Systems and methods for smart remediation for transactions

A method and apparatus for hard deletion of user data are described. The method may include receiving a request from a user computer system to delete user data. The method may also include determining a unique user identifier associated by a system with a user making the request. The method may also include determining whether a data partition, in which data generated by a job or subsystem of the computer system is stored, is predicted to contain a record having the unique user identifier. Then, the method may include searching, when the data partition is predicted to contain a record having the unique user identifier, data records stored in the data partition for a user data record based on the unique identifier, and performing a hard deletion of the user data record from the partition when found during the searching.

Systems and methods for hard deletion of data across systems

A method and apparatus for using cryptographically signed secure identifiers to detect fraud during network based transactions are described. The method may include receiving a transaction from a user device, the transaction comprising a secure identifier purported to be associated with the user device, and the transaction further comprising transaction data. The method may also include extracting a first identifier and a second identifier from the secure identifier purported to be associated with the user device, and then regenerating a true second identifier using the first identifier. Furthermore, when it is determined that the true second identifier matches the second identifier extracted from the secure identifier purported to be associated with the user device, the method may include determining that the secure identifier is valid, and processing the transaction based at least in part on the determination that the secure identifier is valid.

Systems and methods for secure identifiers for electronic transactions

A method and apparatus for performing privacy preserving fraud detection in network based transactions are described. The method may include receiving a fraud detection message during a transaction between a user system and a merchant system, the message having a set of cryptographically transformed universal resource locator (URL) components generated from a URL of a web page of the merchant system on which the transaction is to occur. The method may also include generating one or more secure and anonymous fraud detection features, each fraud detection feature comprising a select subset of the cryptographically transformed URL components. The method may also include performing fraud detection for the web page using the one or more secure and anonymous fraud detection features to determine a likelihood that fraud is occurring in the transaction.

Systems and methods for privacy preserving fraud detection during electronic transactions

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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Seattle, WA, United States of America

Ryan Drapeau