Smart Financial Security Systems: A Reinforcement Learning Approach to Corporate Risk Management

Abstract

The present political, military, financial, and social risk profile worsens worries regarding systematic risks, hence compromising companies' financial situation. Apart from national interests and state security, these risks threaten the commercial success and financial stability of businesses. Companies' production and balance sheets are greatly affected by political turmoil and technological advances as well as market fluctuations and business cycles. Incorporating Artificial Intelligence, particularly Reinforcement Learning (RL), is transforming financial risk management in businesses. Through trial and error, reinforcement learning offers a dynamic and adaptive approach to controlling financial volatility by learning best decision approaches. To solve several corporate dangers like credit risk, fraud, liquidity gaps, and market volatility, this work presents a smart financial security system architecture powered by RL. Using Deep Q Networks (DQN), mimic the deployment of RL agents in financial decision scenarios. Real-world datasets from banking and enterprise financial situations are used to assess the system. Results show that RL models outperform static rule-based systems, lower financial loss exposure, and react rapidly to market swings. To meet legal rules, the intelligent system also has explainability layers and real-time feedback systems. Comparison of this model with rule base and supervised learning models shows how far better efficient it is. The study highlights the potential of reinforcement learning in automating and optimizing enterprise level financial defence mechanisms. The paper ends by suggesting a modular, scalable architecture for future integration across several financial domains.