Generative AI and LLMs in Banking: A Technical Roadmap

Abstract

This comprehensive article examines the transformative impact of generative AI and Large Language Models (LLMs) in the banking sector, focusing on implementation strategies, operational challenges, and solution frameworks. It provides an in-depth analysis of how financial institutions are leveraging these technologies to enhance customer service, risk management, and operational efficiency while addressing critical concerns regarding data privacy, model governance, and system integration. Through a detailed examination of current banking applications and emerging trends, this article presents a structured framework for implementing AI solutions that balance innovation with regulatory compliance and risk management. The analysis encompasses key aspects of technical implementation, including system architecture, data management, and performance optimization, while addressing the human factors crucial for successful adoption. It highlights the importance of strategic partnerships, regulatory collaboration, and continuous improvement in achieving sustainable AI implementation. By examining both current applications and future possibilities, this study provides banking professionals and technology leaders with actionable insights for leveraging generative AI while maintaining security, compliance, and customer trust. It also suggests that successful AI implementation in banking requires a methodical approach that balances technological advancement with risk management, ultimately leading to enhanced operational efficiency and improved customer experience. This article contributes to the growing body of knowledge on AI implementation in regulated industries and provides a roadmap for financial institutions navigating their digital transformation journey.