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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 - #309633 |
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Title:
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An Efficient Sequential Design Method for Detecting Money Laundering
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Author(s):
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Xinwei Deng*+ and Roshan Joseph and Agus Sudjianto and Chien-Fu Jeff Wu
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Companies:
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Georgia Institute of Technology and Georgia Institute of Technology and Bank of America and Georgia Institute of Technology
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Address:
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765 Ferst Drive NW, Atlanta, GA, 30332,
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Keywords:
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Sequential design ; Logistic models ; Anti-money laundering
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Abstract:
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Anti-money laundering is an important responsibility for financial institutions. There are millions of transactions happening each day. Investigating each of the transactions and classifying an account to be suspicious or not is time-consuming and tedious. In this article, we propose a Bayesian sequential design method to detect money-laundering accounts. The sequential nature of the method helps to identify the suspicious accounts with minimal time and effort. The method uses logistic models and a combination of c- and D- optimality criteria to judiciously select the accounts. An application to real banking data is used to demonstrate the performance of the proposed method. Simulation study shows the efficiency and accuracy of the proposed method, as well as its robustness to model assumptions.
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