Um Sistema Multiagente no Combate ao Braqueamento de Capitais

TL;DR

This paper introduces a multiagent system that leverages data mining and rule-based analysis to assist human experts in detecting and analyzing suspicious financial transactions related to money laundering.

Contribution

It presents a novel multiagent system that enhances anti-money laundering efforts by automating behavioral profiling and suspicious transaction analysis beyond simple signaling.

Findings

The system effectively identifies suspicious transactions requiring further review.

It combines data mining with legal rule application for improved detection accuracy.

The approach reduces human workload in final transaction evaluation.

Abstract

Money laundering is a crime that makes it possible to finance other crimes, for this reason, it is important for criminal organizations and their combat is prioritized by nations around the world. The anti-money laundering process has not evolved as expected because it has prioritized only the signaling of suspicious transactions. The constant increasing in the volume of transactions has overloaded the indispensable human work of final evaluation of the suspicions. This article presents a multiagent system that aims to go beyond the capture of suspicious transactions, seeking to assist the human expert in the analysis of suspicions. The agents created use data mining techniques to create transactional behavioral profiles; apply rules generated in learning process in conjunction with specific rules based on legal aspects and profiles created to capture suspicious transactions; and analyze these suspicious transactions indicating to the human expert those that require more detailed analysis.