A proposal to use reinforcement learning to optimize decision-making in the field of counteracting money laundering and terrorist financing (Part 2)

Abstract

Reinforcement learning focuses not only on teaching a single agent, but also the use of this method is reflected in multi-agent operation. This is an important issue from the point of view that the decision-making process and information management in the AML/CFT system for the obligated institution remains an increasingly complex process. Consequently, if we want to use the reinforcement learning method, we must also introduce a multiplicity of agents both in relation to the environment and in relation to each other. Given this type of solutions, it is possible to use multi-agent reinforcement learning or the concept of a semi-independent policy training method with a shared representation for heterogeneous, multi-agent reinforcement learning. Bearing in mind the fact that the AML/CFT decision-making process only derives solutions from artificial intelligence, the human factor also remains essential in this management system. Given these types of needs, the initial solution can be Reinforcement Learning from Human Feedback, which ensures the human factor in learning.

Keywords

• reinforcement learning
• money laundering
• multi-agent
• training set
• feedback