ADOPTION OF ARTIFICIAL INTELLIGENCE FOR RISK MANAGEMENT IN THE BANKING SECTOR OF SRI LANKA: A QUALITATIVE STUDY

Abstract

1. Introduction In the digital transformation era, AI influences the overall financial sector; thus, this paper examines the influence of AI on Risk Management in the Banking Sector of Sri Lanka. The purpose of this study is to uncover the effective usage of AI-driven risk management, identify the challenges and drivers, and propose strategies to address these challenges. 2. Research Methodology A qualitative-deductive research approach was used to address research questions. To collect data, 17 executive/managerial level managers related to risk/IT management representing ten licensed commercial banks in Sri Lanka were interviewed. The study employed thematic analysis through Dedoose software to identify themes and patterns from data collected through semi-structured interviews. 3. Findings and Discussion The findings indicate bank managers have favourable attitudes toward the effectiveness of AI usage in banks. Further, identified that lack of skills, data security and privacy, lack of regulatory framework, data unavailability, cost factors, and language differences as the key challenges for AI adoption for risk management. They recommend providing proper training, connecting with advisors, recruiting skilled employees, updating the school curriculum, aligning with applicable security frameworks, establishing a proactive regulatory framework, and maintaining regular information systems as strategies to mitigate these challenges. 4. Conclusion and Implications The study reveals the crucial role of AI in risk management, reshaping bank performance positively and theoretically while emphasizing the need for exploitation and exploration of organizational performance by addressing the ambidextrous theory. The study highlights the role of AI in risk management for banks, aiming to enhance performance by reducing risks and improving customer satisfaction and trust and to survive in the industry