Adaptive reinforcement learning for automated corporate financial decision making

With the increasing complexity of corporate financial decision-making and the growing need for compliance and adaptability to market volatility, this paper introduces a reinforcement learning (RL) approach tailored for automating financial decision-making in corporate settings. The proposed algorithm features an adaptive reward structure that optimally balances profitability with compliance, ensuring that corporate operations remain within regulatory boundaries while maximizing returns. Additionally, it incorporates a meta-learning-based policy adjustment mechanism that enhances the adaptability of the RL agent, enabling it to respond effectively to changes in market dynamics. To address data scarcity issues, a synthetic data generation module is integrated, providing diverse scenarios for model training and improving its robustness. Experimental results illustrate that the proposed approach surpasses traditional RL models by achieving superior performance in terms of profitability, regulatory compliance, adaptability, and data efficiency. These capabilities make the algorithm highly applicable for dynamic corporate finance environments where regulatory constraints and market conditions frequently shift.