Integrating Client Profiling in an Anti-money Laundering Multi-agent Based System

Abstract

We present a data mining approach for profiling bank clients in order to support the process of detection of antimoney 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.

Anti-money laundering systems: a systematic literature review

“…The first phase in Alexandre and Balsa's (2016) study aimed to reprocess and cluster customer profiles by using the silhouette coefficient and the sum of squared error (SSE). Next, the PART algorithm was used for rule generation as well as for implementing the C4.5 DT to reveal the best leaf technique.…”

“…The first agent is used to capture and report on suspicious transactions, whereas the second agent is used to analyze suspicious transactions detected by the first agent and to make a decision (Alexandre and Balsa, 2015). Alexandre and Balsa (2016) developed the system proposed in Alexandre and Balsa (2015) by using classification Anti-money laundering systems and clustering algorithms. Table 1 and Figure 2 summarize all the ML methods used by the collected articles.…”

“…Jayasree and Siva Balan (2017) used a data set pertaining to Statlog German credit from the UCI repository. The data set used when developing a multi-agent system in Alexandre and Balsa's (2016) was from a financial institution and it described the activity of accounts through three months.…”

A Multi-Agent System Based Approach to Fight Financial Fraud: An Application to Money Laundering

“…The large amount of money involved in this crime and the social issues involved, justify the prioritisation in anti-money laundering (AML) [2]. The stages and a graphical scheme of a typical money laundering process was shown and explained in [3].…”

“…used services, the amount sent to other banks and to the accounts of the own bank, and the number of movements divided into six ranges of values. A twelfth attribute was created and named debt percentage, representing, in a weighted way in the period analysed, the time that the money remained in the customer account[20] [3] (Algorithm 1 -Step 2). This database table with active customer profiles in the analysed year has 2.4 million lines, each line representing a unique element formed by 3-tuple: client, agency and account.…”