Gradient Boosting and Explainable AI for Financial Risk Management: A Comprehensive Review

Financial risk management has increasingly adopted machine learning (ML) techniques, particularly Gradient Boosting Machines (GBMs), due to their high predictive accuracy. However, their "black-box" nature poses challenges for interpretability and regulatory compliance. This paper reviews the integration of Explainable AI (XAI) methods, such as SHAP and LIME, with GBMs to enhance transparency in financial risk assessment. We synthesize findings from recent studies, highlighting the trade-offs between accuracy and interpretability in applications like credit scoring, fraud detection, and crisis prediction. The review demonstrates how XAI techniques enable financial institutions to comply with regulations like the EU's AI Act while maintaining model performance. Key results from literature reviewed show GBMs achieving over 95\% accuracy when combined with SHAP explanations, with feature importance analysis revealing critical risk factors like credit utilization and macroeconomic indicators. The paper also addresses challenges including computational costs, dynamic market adaptations, and regulatory heterogeneity. By examining case studies and quantitative metrics, we propose that hybrid approaches combining GBMs with XAI offer a balanced solution for trustworthy AI in finance. The conclusions emphasize the need for standardized benchmarks and real-time interpretability methods to support wider adoption of transparent AI systems in financial risk management. The paper also addresses regulatory considerations and proposes future directions for scalable, trustworthy AI solutions in finance.By leveraging Gradient Boosting Machines (GBMs) and ensemble methods, this study demonstrates how XAI can bridge the gap between sophisticated machine learning models and regulatory compliance. Empirical results highlight improved transparency, stakeholder trust, and regulatory adherence in financial institutions. The increasing adoption of Artificial Intelligence (AI) and Machine Learning (ML) in financial risk management, particularly in credit scoring and default prediction, offers enhanced predictive power. This paper reviews the current trends in applying Explainable AI (XAI) techniques to credit risk management. We examine various XAI methods discussed in recent literature, focusing on their ability to provide insights into model decisions, enhance trust, and address fairness concerns.