A Robust Hybrid Model Integrating GANs, XGBoost, and Reinforcement Learning (RL)

Abstract

This study introduces a robust hybrid model that integrates Generative Adversarial Networks (GANs), eXtreme Gradient Boosting (XGBoost), and Reinforcement Learning (RL) to enhance predictive analysis and anomaly detection in financial data, specifically targeting fraud detection and trend forecasting. Leveraging the unique strengths of each component, GANs for generating high-quality synthetic data to address class imbalance, XGBoost for precise prediction models, and RL for dynamic decision-making based on evolving data patterns, this unified framework offers a novel and ambitious approach to financial security. We detail the design, implementation, and comprehensive evaluation of this model using real-world financial datasets, demonstrating significant improvements in accuracy and decision-making speed within complex economic contexts. The proposed methodology addresses critical challenges such as data imbalance, evolving fraud patterns, and the need for adaptive decision-making, providing a scalable and effective solution for enhanced financial security. Experimental results demonstrate that the hybrid model achieves superior performance compared to individual components, with XGBoost achieving % accuracy, RL demonstrating 93.2% accuracy with excellent adaptability, and GANs providing effective data augmentation with 90.35% recall for fraud detection.