Walmart Apollo, LLC

Vidhya Raman

Hui-Min Chen

Sangita Fatnani

Lian Liu

Yitao Yao

Pei Wang

Uday Akella

Zhiping Tang

Vivek Crasta

Paria Ghorbani

Jaya Kolhatkar

Guoyu Zhu

Pushpa Raghani

This application relates to apparatus and methods for generating target labels from multi-dimensional time series data. In some examples, a computing device receives parameters that correspond to each one of a plurality of sellers, and gathers a set of relevant time series metrics for each one of the sellers based on the respective seller's parameters over a time period. The computing device calculates aggregated metrics for each corresponding subperiod based on each seller's respective set of relevant time series metrics. The computing device generates seller persona clusters based at least in part on the calculated aggregated metrics for each seller. The computing device calculates a score for the generated seller persona clusters, and when the score is greater than or equal to a predetermined threshold value, generates a respective target label for each of the clusters, and applies each respective target label to the sellers within each respective cluster.

Methods and apparatus for generating target labels from multi-dimensional time series data

This application relates to apparatus and methods for identifying fraudulent transactions. A computing device receives return data identifying the return of at least one item. The computing device obtains modified strategy data identifying at least one rule of a modified strategy. The rule may be based on the application of at least one discrete stochastic gradient descent algorithm to an initial strategy. The computing device applies the modified strategy to the received return data identifying the return of the at least one item, and determines whether the return of the at least one item is fraudulent based on the application of the modified strategy. The computing device generates fraud data identifying whether the return of the at least one item is fraudulent based on the determination, and may transmit the fraud data to another computing device to indicate whether the return is fraudulent.

Methods and apparatus for fraud detection

This application relates to apparatus and methods for identifying fraudulent transactions. A computing device receives return data identifying the return of at least one item. The computing device obtains modified strategy data identifying at least one rule of a modified strategy. The rule may be based on the application of at least one dimensionality reduction algorithm to an initial strategy. The computing device applies the modified strategy to the received return data identifying the return of the at least one item, and determines whether the return of the at least one item is fraudulent based on the application of the modified strategy. The computing device generates fraud data identifying whether the return of the at least one item is fraudulent based on the determination, and may transmit the fraud data to another computing device to indicate whether the return is fraudulent.

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

Systems and methods of evaluating a third-party participant for inclusion in a networked environment are disclosed. A plurality of features representative of a third-party participant are received and at least one pairwise feature score is calculated for a first feature and a second feature selected from the plurality of features. The third-party participant is classified into one of a plurality of predetermined categories by a trained model based on the at least one pairwise feature score.

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San Jose, CA, United States of America

Vidhya Raman