AI-Driven Risk Assessment and Management in Banking: Balancing Innovation and Security

The integration of artificial intelligence (AI) and data analytics in finance is transforming risk management practices within financial institutions, enabling them to navigate the complexities of an ever-evolving regulatory landscape.This paper explores how AI-driven models and advanced data analytics techniques can enhance the identification, assessment, and mitigation of financial risks.It begins with a comprehensive overview of the types of risks faced by financial institutions, including credit risk, market risk, operational risk, and regulatory compliance.The discussion highlights the role of machine learning algorithms in improving risk prediction accuracy and optimizing decision-making processes.Furthermore, the paper examines the application of big data analytics in gathering insights from diverse data sources, enhancing risk modelling capabilities, and ensuring a proactive approach to risk management.Real-world case studies demonstrate how financial institutions are leveraging AI technologies to strengthen their risk management frameworks, reduce losses, and comply with regulatory requirements.The paper also addresses challenges related to data privacy, ethical considerations, and the need for robust governance frameworks when implementing AI in risk management.Ultimately, this research underscores the potential of AI and data analytics as transformative tools for enhancing the resilience and adaptability of financial institutions in a dynamic risk environment.