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Professional

Track: Emerging Analytics

Approaches and Open Questions in Fraud Detection: A Call to Action for the Analytics Community

Wednesday, April 14, 1-1:40pm EDT

Fraud has been identified by the Global Economic Forum and other international organizations as one of the greatest risks to the economic stability and continued growth of the world economy, with various estimates of the annual global cost exceeding $4 trillion. The core ideas of this talk are focused on how analytics can have an impact on this problem, the methods that work (and how well), and open and pervasive problems that are yet unsolved.

Because of the sensitive nature of this work, the talk will focus on information available publicly and will include examples of fraud in many industries, such as banking, insurance, healthcare, online retail, and public sector. We discuss the characteristics of fraud behavior and how these can be modeled, and we will explain differences in identifying, interdicting, stopping, or recovering money stolen depending on the stage of the activity.

We will also provide a description of the levels of organizational maturity (awareness, data availability, knowledge of methods and tools, and processes and procedures in place) that can be used to differentiate the likelihood of an organization’s success in combating fraud.

The talk will also contain details of analytical approaches focused on different stages of an organization’s maturity (and data availability) including rule-based systems (which are often the only tools available in early stages of an organization’s anti-fraud maturity) and more advanced analytical tools including graph theory, machine learning, supervised and unsupervised data mining, natural language processing in social media spaces, and network-based investigative tools.

Finally, we will discuss a number of open problems, including the challenge of emerging fraud (the new fraud approaches that are constantly being developed), including strategies that have been used and research done on this very challenging problem.

Arnie Greenland image

Arnie Greenland

Arnie Greenland

Professor of the Practice at the Robert H. Smith School of Business, University of Maryland

Dr. Arnie Greenland is Professor of the Practice at the Robert H. Smith School of Business, University of Maryland where he teaches courses in Business Analytics. He retired in 2014 from the IBM Corporation, where he was an IBM Distinguished Engineer. Arnie worked in a wide variety of analytical areas and for a broad range of clients, though the last years of his career was focused on public agencies engaged in fighting against fraud, waste and abuse. He holds numerous patents and authored several professional articles in this area. Arnie is an active member of INFORMS, was involved in the initial development of the CAP certification program and was one of the initial grantees of that credential. In addition, Arnie has worked on numerous INFORMS constituent organizations including the INFORMS Roundtable and a number of committees including the Analytics Certification Board, ProBono Analytics Committee, the Wagner, Edelman, and Syngenta Award committees, and many others. Arnie holds a Ph.D. in Mathematics.