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Activity Number:
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478
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Type:
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Contributed
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Date/Time:
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Wednesday, August 1, 2007 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistical Computing
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| Abstract - #309318 |
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Title:
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Statistical Methods for Detecting Financial Crimes: Applications and Challenges
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Author(s):
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Agus Sudjianto*+ and Sheela Nair
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Companies:
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Bank of America and University of California, Los Angeles
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Address:
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201 N Tryon, Charlotte, NC, 28255,
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Keywords:
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Financial crime ; fraud ; anti-money laundering ; machine learning
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Abstract:
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Financial crimes affect millions of people every year, and financial institutions must employ methods to protect themselves and their customers. Fraud and money laundering are two common types of crimes, and there has been extensive research to develop algorithms to detect these crimes. Detection algorithms face a large set of challenges, including prediction of rare events, overlap between classes, and criminals who alter their strategy to trick current detection techniques. We will discuss some existing methodologies in practice as well as some challenges to stimulate further research.
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