Sustainable FinTech Risk Management Using Reinforcement Learning and Predictive Analytics

Abstract

 The traditional methods of risk management are becoming less effective in the face of new threats, including fraud, cyber, credit default, market volatility and changing regulations, that financial technology (FinTech) ecosystems are dealing with. The study aims at developing a sustainable FinTech risk management framework based on the synergy between Reinforcement Learning (RL) and Predictive Analytics (PA) for adaptive, intelligent and real-time financial risk management. The methodology adopts data preprocessing, feature engineering, anomaly detection, prediction using gradient boosting and a Deep Q-Network reinforcement learning agent which learns and implements the best mitigation policies in the changing financial landscape. The proposed approach is a combination of financial activities, regulatory compliance and sustainability of operations, which will enhance decision making under uncertainty. Experimental results shows that the framework could yield a high accuracy of 95.76%, precision of 94.80%, recall of 94.32%, F1-score of 94.56%, and ROC-AUC of 96.41% compared with the traditional machine learning techniques which could reduce financial risk exposure by 30.6% and fraud detection efficiency by 18.9%, while decrease decision latency by 23.4%. Comparative analysis proves the superior adaptability, portfolios stability, and prediction of risk, in the ever-changing patterns of transactions. This work is novel in that it combines the power of the predictive analytics and reinforcement learning for self-adaptive and sustainable financial risk governance. The proposed framework brings an intelligent, scalable and explainable decision support solution that will improve the ability to be resilient, efficient and sustainable in contemporary digital financial ecosystems.