Abstract: A system and method for measuring and mitigating risk from anomalous financial transactions. The system assesses static and dynamic risks for financial assets, actors and transactions. Using both general information for context and specific information about a financial institution, the system includes a Context Generator, a Feature Generator, and an Analytics Engine which use machine learning features to produce forensic results about transactions and customers. The invention includes an interactive Sensemaker to assist users and analysts in compliance checking, reporting, and to identify anomalous transactions with minimal human operator intervention and low false positive results.

Abstract: An approach for real time, round trip pseudonymization (a.k.a. anonymization or tokenization) of data on the fly, in real time, enabling remote secure processing of sensitive data such as by a cloud service. Sensitive data remains on premises with the client at all times. A user may thus run extensive queries that return sensitive data without noticing that such data was pseudonymized in transit.