The Dual-Faceted Integration Of Generative AI In Banking: Balancing Innovation And Governance

Abstract

This article examines the transformative impact of generative AI and Large Language Models (LLMs) in the banking sector, analyzing both the opportunities and challenges of this technological evolution. The article traces AI's progression in financial services from early rule-based systems to today's sophisticated generative models, identifying key strategic applications across customer-facing services, operational efficiency, risk management, and regulatory compliance. The implementation barriers including data privacy concerns, algorithmic bias, explainability challenges, and varying cost-benefit considerations across institution sizes, the research proposes a structured integration framework tailored to different banking segments. This framework encompasses phased implementation strategies, governance protocols, human-AI collaboration models, specialized training methodologies, and partnership ecosystems. The article concludes by exploring emergent trends, research gaps, policy recommendations, strategic considerations for executives, and projections for long-term industry transformation, providing a balanced assessment of how generative AI is reshaping the banking landscape.