Presenting a methodology based on the self-organizing maps and multi-layer neural networks for suspected money laundering events at bank branches

Given the importance of banking systems and the misuse of this platform for money laundering purposes, the urgent need for the implementation of anti-money laundering systems by governments and policy makers in economic affairs is important. Also, due to the growth of terrorism and organized fraud, and the passage of numerous laws against these cases, the need for these systems is increasing. On the other hand, the complexity of money laundering suspicious behaviors is such that no significant action can be taken to detect money laundering without intelligent and data-driven tools. An important and perhaps practical point in Iran is the proximity of these systems to anti-bribery, fraud, violation and inspection systems, which can be considered as an efficient tool for the bank's inspection unit. This paper presents an approach based on data analysis and processing. In this approach, using self-organizing maps, bank branches are clustered based on similar behaviors, then the process of labeling branches is performed using a linear index. In the next step, using the training of a multi-layer neural network, a model for identifying bank branches in which suspicious money laundering processes take place is introduced.