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Vol. 4 No. 1 (2023)
2023 Edition

Articles

Reinforcement Learning-Driven Proximal Policy Optimization for Adaptive Compliance Workflow Automation in High-Dimensional Banking Systems

Published 2023-01-25

  • Raj Sonani

Raj Sonani
SC Johnson College of Business, Cornell University, New York, USA

Abstract

The banking industry is a dynamic environment that processes the ever-growing number of complex financial transactions. It is, therefore, important to have more efficient compliance workflow automation systems that are easy to manage. Static rule-based systems, traditional as they are, rely on predefined rules and fixed sequences to facilitate their operation. Despite their inherent merits, they fail to cope with the variability of the environment and the modification of the regulations, thus leading to inefficiencies, increased operational costs, and potential compliance risks. This research deals with these challenges by creating a Reinforcement Learning (RL) framework which is based on Proximal Policy Optimization (PPO) to dynamically optimize compliance workflows in a high-dimensional banking system.

Keywords

Compliance Workflow Automation, Reinforcement Learning, Proximal Policy Optimization (PPO), High-Dimensional Banking Systems, Regulatory Adherence

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