Walmart Apollo, LLC

Uday Akella

Zhiping Tang

Hui-Min Chen

Linhong Kang

Mayra Rocio Harmon

Kristin Danielle Piper

Charlie Berry

John Fields

YiYi Zeng

Arthi Vijayakumar

Nathan Olds

Yitao Yao

Pei Wang

Sangita Fatnani

Vidhya Raman

Jingru Zhou

Tzu-Yen Wang

Vivek Crasta

Jaya Kolhatkar

Chuying Ma

Ashley Renee Renee Greehey

David Andrew Splittorff

Catherine Antill

Guoyu Zhu

Kevin Philip Clay

This application relates to systems and methods for auditing registers. In some examples, a system comprises registers, a recycling device, handheld computing devices, and retailer computing devices. The retailer computing devices may be configured to store a decision model, receive a calculated monetary position and activity data for one of the registers, generate a first associate record based at least on the decision model and one or more of the calculated monetary position and the activity data, generate a second associate record based at least on the decision model and historical behavioral data for one or more associates, generate a first audit alert record based at least on the second associate record and the decision model, send audit alerts to the handheld computing devices, and send the first audit alert record to the database.

Systems and methods for auditing registers

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

A method and system for identifying and labeling fraudulent store return activities includes receiving, by a server, retailer events from an online transaction system of a retailer, the retailer events comprising records of transactions between customers and the retailer, including sale, exchange and return activities across multiple stores. The retailer events are processed to build a network that associates stores, transactions, payment instruments, and customer identification over related activity sequences of transactions. Return fraud labels are generated for the retailer events representing returns based on identified fraud characteristics of the related activity sequences by: representing behavior variables extracted from activity sequences by respective signature vectors; calculating pairwise similarity between the signature vectors; identifying clusters of the signature vectors having common behavior patterns based on the calculated pairwise similarity; and labeling the identified clusters of signature vectors as non-fraudulent behavior or fraudulent behavior.

Identifying and labeling fraudulent store return activities

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San Carlos, CA, United States of America

Uday Akella