Client Profiling for an Anti-Money Laundering System
This work addresses anti-money laundering detection for financial institutions, but it appears incremental as it applies existing data mining methods to a specific domain without claiming major breakthroughs.
The paper tackled the problem of detecting anti-money laundering operations by developing a data mining approach to profile bank clients, resulting in the generation of classification rules from real-world financial data that grouped clients into clusters.
We present a data mining approach for profiling bank clients in order to support the process of detection of anti-money laundering operations. We first present the overall system architecture, and then focus on the relevant component for this paper. We detail the experiments performed on real world data from a financial institution, which allowed us to group clients in clusters and then generate a set of classification rules. We discuss the relevance of the founded client profiles and of the generated classification rules. According to the defined overall agent-based architecture, these rules will be incorporated in the knowledge base of the intelligent agents responsible for the signaling of suspicious transactions.