Application of Reinforcement Learning to Optimize Business Processes in the Bank

Abstract

This article describes the application of reinforcement learning (q-learning, genetic algorithm, cross-entropy) to define the optimal structure of business processes in the bank. It describes the principle of creation of the environment, loss, and reward. Setting of hyperparameters for each method is considered in depth. Besides, it offers the variant of calculation of the maximum potential for saving, which can be arrived at through the business process optimization.