  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.
