Predicting Corporate Misconduct

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Bennett B. Borden

USA

: J Comput Eng Inf Technol

Abstract

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Corporate misconduct costs $3 Trillion worldwide every year to prevent, detect and deal with its consequences. If we can predict when someone will purchase a product, click on an ad, or how they will vote for a candidate, why can’t we predict when he or she will engage in some form of fraud or other misconduct? Well, perhaps we can. In this session, Chief Data Scientist Bennett Borden, from the law firm Drinker Biddle & Reath will present his work on developing algorithms to predict corporate misconduct and how this technology is being used today and how it will likely be used in the future.

Biography

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Bennett B Borden is a Chief Data Scientist at Drinker Biddle & Reath. He is a globally recognized authority on the legal, technology and policy implications of information. His ground-breaking research into the use of machine-based learning and unstructured data for organizational insight is now being put to work in data-driven early warning systems for clients to detect and prevent corporate fraud and other misconduct. He received his Master of Science in Data Analytics at New York University and his JD at Georgetown University.