A proposal to use reinforcement learning to optimize decision-making in the field of counteracting money laundering and terrorist financing (Part 1)

Abstract

Reinforcement learning is a proposal for solving the problems of identifying and verifying customers of mandatory institutions who may be connected with money laundering or terrorist financing. Its application can take place both at the level of verification activities but also at the level of monitoring of the institution’s client. The reinforcement learning model allows the results of an agent’s actions to be obtained as not only a consequence of his learning, but also of his own decision-making aimed at obtaining the greatest possible reward. Supporting this type of action is not only the provision of technical data but also the collaboration with the human agent in Reinforcement Learning from Human Feedback. In addition to the very idea of incorporating this type of machine thinking model into the analytical level of the obligated institution, it remains to obtain results through it in the form of predictive threat detection related to the possibility of legalizing criminal funds and investing them in terrorist activities.

Keywords

• reinforcement learning
• money laundering
• markov model
• agent
• training set
• feedback