Visa International Service Association

Amazon Technologies, Inc.

Hangqi Zhao

Chiranjeet Chetia

Claudia Carolina Barcenas Cardenas

Jianhua Huang

Punit Kumar Rajgarhia

Fan Yang

Qiang Zhou

Sengamedu Hanumantha Rao Srinivasan

Shubham Agrawal

Sheng Wang

Shizhe Ma

Provided is a computer-implemented method for monitoring and improving data quality of transaction data that may include receiving transaction data associated with a plurality of payment transactions from an acquirer system. The transaction data may include a transaction record associated with each payment transaction of the plurality of payment transactions. Each transaction record may include a plurality of data fields. Each respective data field of the plurality of data fields may be categorized into a respective type of a plurality of types. A data quality score for each respective data field of the plurality of data fields may be determined based on the respective type of the respective data field. A system and computer program product are also provided.

System, method, and computer program product for monitoring and improving data quality

A system identifies a pattern in source code. The pattern is identified based, at least in part, on correlation between units of code, or on the derivation of a rule from a recurring sequence in the source code. The system identifies a portion of the source code that at least partially matches the pattern, and determines that this portion includes at least one deviation from the pattern. The system then generates an error message to describe the deviation.

Inconsistency-based bug detection

A method for detecting fraudulent interactions may include receiving interaction data, including a first plurality of interactions with (first) fraud labels and a second plurality of interactions (without fraud labels). Second fraud label data for each of the second plurality of interactions may be generated with a first neural network (e.g., classifying whether each interaction is fraudulent or not). Generated interaction data and generated fraud label data may be generated with a second neural network. Discrimination data for each of the second plurality of interactions and generated interactions may be generated with a third neural network (e.g., classifying whether the respective interaction is real or not). Error data may be determined based on the discrimination data (e.g., whether the respective interaction is correctly classified). At least one of the neural networks may be trained based on the error data. A system and computer program product are also disclosed.

Method, system, and computer program product for detecting fraudulent interactions

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

Growing community of inventors

Austin, TX, United States of America

Hangqi Zhao