Integrating Client Profiling in an Anti money Laundering Multi agent Based System
Continuing previous work by the authors, where an Anti Money Laundering (AML) agent based system was introduced, we now provide some detail on one of the elements of this system the learning component. The system we are developing focuses on how a financial institution, a bank, can obtain better results in AML initiatives. More specifically, we're trying to improve the suspicious transaction signaling process and the subsequent final decision. For this, it is critical to model client behavior, having a clear definition of the different client profiles. Having available a real world data set of bank transactions, we explain in this contribution how some data mining techniques were used in order to build the needed client profiles, and how the results obtained can be integrated in the system.
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