Abstract Details
Activity Number:
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79
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Type:
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Contributed
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Date/Time:
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Sunday, August 4, 2013 : 4:00 PM to 5:50 PM
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Sponsor:
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Section on Risk Analysis
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Abstract - #309970 |
Title:
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A Statistical Diagnosis of Customer Risk-Ratings in Anti-Money Laundering Surveillance
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Author(s):
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Bhojnarine R. Rambharat*+ and Andrew J Tschirhart
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Companies:
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U.S. Treasury (OCC) and U.S. Treasury (OCC)
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Keywords:
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Anti-money laundering ;
Customer ;
Risk-ratings ;
Ordinal panel data ;
Regression ;
Statistical diagnosis
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Abstract:
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A statistical framework is introduced to model customer risk-ratings used in anti-money laundering (AML) surveillance. We analyze data on a sample of 494 customers from a U.S. national bank, where customers are rated from Low to High over 13 time periods. We model the ratings using an ordinal panel data regression model with random effects using covariates provided by the bank. We derive the log-likelihood of the model and estimate all model parameters while ensuring our final results are robust. Our findings unveil key policy-related insights about AML surveillance. We argue for more granular monitoring of highly suspicious customers since this could optimize finite resources within a bank. A survival study is presented where we use a proxy for the date of an ``investigation occurrence'' to uncover where risk is prevalent in the bank. We also discuss quality assurance sampling techniques for evaluating customer risk-ratings. We argue that statistical diagnosis in AML surveillance may have fruitful benefits within the micro-sphere of a single financial institution and, more importantly, that these benefits could extend to the wider macro-sphere of the global community.
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Authors who are presenting talks have a * after their name.
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