Adaptive Trade Exception Handling in Financial Institutions: A Reinforcement Learning Approach with Dynamic Policy Optimization

Abstract

Powerful trade execution systems have been mainly relied upon to manage transactions on financial institutions with ease. Still, the adverse market conditions as well as some system anomalies trigger adaptive exception handling of trades. This paper thus utilizes a reinforcement learning-based technique for dynamic optimization of policy for handling trade exceptions. The introduced model learns constantly from historical as well as real-time trade data in order to develop its skill of decision making, minimize false positives, and maximize operational effectiveness. Using machine learning algorithms such as Q-learning and DDPG, the system learns dynamically according to changes in the market and thus effectively handles trade exceptions. Our results were that adaptive reinforcement learning models are better than static rule-based systems in handling trade exception cases and thus may be considered as the scalable intelligent solution for financial organizations. This research casts a little light on the role of AI-driven automation in compliance monitoring, risk management, and trade surveillance.