Integrating AI in financial risk management: Evaluating the effects of machine learning algorithms on predictive accuracy and regulatory compliance

This research focuses on adopting ML models in risk management and how such factors influence predictive abilities and compliance with relevant rules. With more financial institutions using some of these advanced AI technologies in their decision-making capacities, a clear understanding of their effectiveness and what legal compliance would mean for their growth becomes vital. This research presents a comprehensive literature review of traditional risk management methods compared to the newer, AI-based methodologies by meticulously evaluating difficult standard measurements, including accuracy, precision, and recall. Further, the research analyses the compliance risks that arise with AI, especially concerning significant regulations such as Basel III and GDPR, which are essential in preserving financial stability and customer confidence. The study shows that applying AI approaches enhances predictive efficiency to a very high degree and the pressing and major legal concerns that institutions face. Moreover, the studies reveal the beneficial sectors for applying machine learning for operational risk management and provide guidelines for employing AI. To improve and strengthen risk management approaches and guarantee strict compliance with current and future implementing regulations, this study offers pertinent information to current discourses regarding the future of finance within the rising context of technological advancements.