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.
Cite
@article{arxiv.1510.00878,
title = {Client Profiling for an Anti-Money Laundering System},
author = {Claudio Alexandre and João Balsa},
journal= {arXiv preprint arXiv:1510.00878},
year = {2016}
}