Generative AI in banking: empirical insights on integration, challenges and opportunities in a regulated industry

Abstract

Purpose: This study aims to fill critical research gaps by providing empirical evidence on the practical application of generative AI in the banking sector. It explores managerial preparedness, regulatory compliance and data privacy challenges in implementing this technology, offering insights into its operational effectiveness and potential in financial services. Design/methodology/approach: The research employs a qualitative approach, conducting in-depth interviews with bank managers and industry experts. These interviews are analysed to identify key factors influencing the integration of generative AI in financial institutions. Findings: The study identifies five critical factors – recognition, requirement, reliability, regulatory and responsiveness – that collectively impact the adoption and operational effectiveness of generative AI in banking. These factors highlight the challenges and opportunities of integrating this technology within the highly regulated financial industry. Practical implications: The findings have significant theoretical and managerial implications. Theoretically, the research contributes to understanding AI integration in regulated industries, particularly financial services. Managerially, it provides a roadmap for financial institutions to adopt generative AI responsibly, balancing innovation with regulatory compliance and ethical considerations. Originality/value: This study is among the first to provide empirical data on generative AI’s practical application in the banking sector, addressing the lack of real-world evidence and offering a comprehensive analysis of the factors influencing its successful implementation in a highly regulated environment.