Operationalizing ML/AI in the Fraud Mitigation Space
Fraud prevention represents a dynamic and challenging area for data science and analytics. Building and assessing performance of machine learning solutions often is the tip of the iceberg in terms of the having an impact in preventing fraud. Deployment within the real-time architecture and decision rules constitute the bulk of the work. Given this context it is critical to understand how to evaluate the performance of individual machine learning models, and in developing comprehensive, data driven, fraud prevention engines. This discussion will seek to provide an overview of how we 1) assess value of individual solutions, 2) operationalize multiple machine learning solutions, and 3) using machine learning to create optimal decision rules.
Brooks Bourland is the Head of Global Digital Fraud Data Science & Analytics at TransUnion. He leads the data science operations for Device Recognition and Fraud Prevention on the digital channel supporting companies across the globe. Prior to taking this role he led fraud data science at USAA Bank. Brooks has degrees from the University of Oklahoma and University of Denver, and he currently lives in the Denver metro area with his wife, Jen, and son, Oliver.
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