Visa International Service Association

Shubham Agrawal

Chiranjeet Chetia

Yuhang Wu

Navendu Misra

Claudia Carolina Barcenas Cardenas

Fei Wang

Shi Cao

Hangqi Zhao

Yu-San Lin

Deepam Tiwan

Sheng Wang

Ishita Bindlish

Shizhe Ma

Linyun He

David Stoddard Lambertson

Beatrice-Atena Faurescu

Karankumar Hiteshbha Patel

Systems, methods, and computer program products for multi-domain ensemble learning based on multivariate time sequence data are provided. A method may include receiving multivariate sequence data. At least a portion of the multivariate sequence data may be inputted into a plurality of anomaly detection models to generate a plurality of scores. The multivariate sequence data may be combined with the plurality of scores to generate combined intermediate data. The combined intermediate data may be inputted into a combined ensemble model to generate an output score. In response to determining that the output score satisfies a threshold, at least one of an alert may be communicated to a user device, the multivariate sequence data may be inputted into the feature-domain ensemble model to generate a feature importance vector, or at least one of a model-domain, a time-domain, a feature-domain, or the combined ensemble model may be updated.

System, method, and computer program product for multi-domain ensemble learning based on multivariate time sequence data

Described are a system, method, and computer program product for breach detection using convolutional neural networks. The method includes receiving transaction data associated with a plurality of transactions completed in a first time period. The method also includes identifying a set of suspected fraudulent transactions of the plurality of transactions based on inputting at least one parameter of the transaction data into a fraud evaluation model. The method further includes generating an image comprising a field of points, wherein each point is associated with at least one transaction of the set of suspected fraudulent transactions, and wherein an x-axis position in the image of each point in the field of points is associated with a time subperiod of the first time period in which the at least one transaction occurred. The method further includes detecting a breach event by processing the image with a convolutional neural network (CNN) model.

System, method, and computer program product for breach detection using convolutional neural networks

Described herein are techniques and systems directed to using context data provided by supplemental devices to reduce false declines during transaction authorization. In some embodiments, upon a transaction being declined based on a level of risk associated with the transaction, a service provider may obtain context data from a number of supplemental devices associated with a user, resource provider, location, or other suitable factor. This context data may then be assessed by the service provider to determine a degree to which the context data supports that the user conducting the transaction is the owner of the account used to conduct the transaction. The level of risk may then be adjusted based on that degree and the adjusted level of risk is used to determine whether to approve or decline the transaction.

System to reduce false declines using supplemental devices

Provided herein is a computer-implemented method for settling an outstanding invoice issued by a payee, including the steps of capturing a digital image of an invoice issued by a payee to a payor, processing the digital image to identify invoice data and a network location associated with the payee, automatically establishing communication with the network location identified in the digital image, and automatically inputting payment information into one or more fields of the webpage at the network location.

System and method for mobile pay

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Round Rock, TX, United States of America

Shubham Agrawal