Walmart Apollo, LLC

Linhong Kang

Kazuo Matsumoto

Uday Akella

Premalatha Thangamani

Zhiping Tang

Jing Xia

Mayra Rocio Harmon

Saurabh Vivek Gagpalliwar

Charlie Berry

YiYi Zeng

Arthi Vijayakumar

Nathan Olds

John Fields

Kristin Danielle Piper

This application relates to apparatus and methods for identifying fraudulent payment transfers. In some examples, a computing device determines payment transfer initiation features, and payment transfer reception features, based on previous payment transfer data. The computing device may train a machine learning fraud detection model with the payment transfer initiation features, and may train a machine learning fraud detection model with the payment transfer reception features. Once trained, the computing device may employ the machine learning fraud detection models to identify fraudulent payment transfers. For example, the computing device may determine whether a payment transfer is fraudulent when the payment transfer is initiated. Assuming the payment transfer is allowed, the computing device may determine whether the payment transfer is fraudulent when the payment is being received. In some examples, the computing device prevents completion of the purchase transaction if the payment transfer is determined to be fraudulent.

Methods and apparatus for payment transfer fraud detection

This application relates to apparatus and methods for identifying fraudulent transactions. In some examples, a computing device generates a decision matrix to identify fraudulent transactions. To generate the decision matrix, the computing device may determine scores for a plurality of transactions, and may determine transaction categories for each transaction based on the scores. The computing device may also determine a number of predictable features based on applying machine learning techniques to the transactions. A risk category is then determined for the number of predictable features. The computing device generates the decision matrix based on the transaction categories and the risk categories. In some examples, the computing device applies the generated decision matrix to an ongoing purchase transaction to determine if the ongoing purchase transaction is fraudulent. In some examples, the computing device prevents completion of the purchase transaction if the purchase transaction is determined to be fraudulent.

Methods and apparatus for payment fraud detection

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Sunnyvale, CA, United States of America

Linhong Kang