Visa International Service Association

Xerox Corporation

Andrew Beams

Hao Yang

Eliud Robles Flores

Sunpreet Singh Arora

Mahdi Zamani

Peter Rindal

Srinivasan Raghuraman

Yuhang Wu

Yu Qi

Yizhen Wang

Sebastian Meiser

Panagiotis Chatzigiannis

Harshal Bhadreshkumar Shah

Judith Millner Vandewinckel

Methods and systems for performing federated private anomaly detection are disclosed. An anomaly detector computer can collaborate with an aggregator computer and an account management computer in order to train machine learning models, which can be used to classify events as not fraudulent or fraudulent. The anomaly detector computer can obliviously use private information (e.g., account flags and account flag values) held by the account management computer to train and use the machine learning models, such that the anomaly detector does not become aware of the account flags or account flags values. Such a system can be used, for example, for the detection and prevention of financial crime. The anomaly detector computer can use the account flag information possessed by the account management computer to identify fraudulent events performed by customers of the organization operating the account management computer.

Secure anomaly detection using federated learning

Growing community of inventors

Christiansburg, VA, United States of America

Andrew Beams