Um Sistema Multiagente no Combate ao Braqueamento de Capitais
This addresses inefficiencies in anti-money laundering processes for financial institutions and regulators, though it appears incremental by building on existing signaling methods.
The paper tackles the problem of human overload in anti-money laundering by proposing a multiagent system that assists experts in analyzing suspicious transactions, using data mining and rules to prioritize cases for detailed review.
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.