Palantir Technologies Inc.

Rootul Patel

William Seaton

Myles Novick

Clemens Wiltsche

Harkirat Singh

Matthew Julius Wilson

Nicolas Prettejohn

Richard Deitch

Ben Regev

Maciej Biskupiak

Jorien Van Den Bergh

Juan Jimenez Puig

Eugene Mak

Stephen Heitkamp

Alexander Maass

Duncan Hoffman

Marissa Miracolo

Yevhen Shevchuk

Joshua Aschheim

Kai Kamberger

Elise Norman

Elizabeth Patitucci

Patrick Walter

Systems and methods are provided for reducing failure rates of a manufactured products. Manufactured products may be clustered together according to similarities in their production data. Manufactured product clusters may be analyzed to determine mechanisms for failure rate reduction, including adjustments to test quality parameters, product formulas, and product processes. Recommended product adjustments may be provided.

Systems and methods for reducing manufacturing failure rates

A customer risk trigger associated with a customer may be identified. A response to the customer risk trigger may be detected. First risk analysis data related to the customer risk trigger may be gathered, based on the response, from a first datastore. Second risk analysis data related to the customer risk trigger may be gathered, based on the response, from a second datastore. A customer risk profile to model risk attribute(s) of the customer may be gathered. The risk attributes may represent a risk correlation between the customer and a prohibited act. Customer risk visualization tool(s) configured to facilitate visual user interaction with the customer risk profile may be gathered. The customer risk visualization tools may be rendered in a display of the computing system. The customer risk visualization tools provide a customer-centric view of risk for various applications, including anti-money laundering applications.

Automated risk visualization using customer-centric data analysis

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New York, NY, United States of America

Rootul Patel