A Robust Model for Integrating Artificial Intelligence into Financial Risk Management: Addressing Compliance, Accuracy, and Scalability Issues

The integration of Artificial Intelligence (AI) into financial risk management has transformed the industry by enabling real-time analysis, enhanced decision-making, and predictive insights. However, challenges related to compliance with regulatory frameworks, the accuracy of AI models, and the scalability of these solutions persist. This study proposes a robust model that systematically integrates AI into financial risk management while addressing these critical issues. The model combines machine learning (ML) algorithms, natural language processing (NLP), and explainable AI (XAI) techniques to optimize risk assessment and mitigation. By leveraging supervised and unsupervised ML models, the framework achieves higher predictive accuracy in identifying risks such as fraud, credit default, and market volatility. To address compliance challenges, the model incorporates regulatory-aware AI components that ensure adherence to international financial standards, such as Basel III, GDPR, and other jurisdiction-specific requirements. These components employ real-time data analysis and automated reporting mechanisms to facilitate regulatory alignment. Furthermore, explainable AI methodologies, including SHAP (SHapley Additive exPlanations) values and LIME (Local Interpretable Model-agnostic Explanations), are employed to enhance transparency and interpretability, fostering trust among stakeholders and regulatory bodies. Scalability is achieved through the implementation of cloud-based AI infrastructure and edge computing technologies, enabling financial institutions to handle large datasets and high transaction volumes without compromising performance. The proposed model is validated using a hybrid dataset comprising real-world financial transactions, synthetic data, and regulatory guidelines. Results demonstrate significant improvements in predictive accuracy, regulatory compliance rates, and operational scalability. This research contributes to the field of financial risk management by providing a practical, scalable, and compliant framework for AI integration. It highlights the potential of AI to revolutionize risk management processes while mitigating the associated challenges. The study concludes with recommendations for future research to explore emerging technologies such as quantum computing and their applications in enhancing financial risk management systems.