Generative AI and banking information systems: a systematic literature review of enablers and emerging opportunities.

Abstract

The rapid pace of Generative Artificial Intelligence (GenAI) integration is transforming the business environment in the world banking market. Although the scholarly attention is increasing, a dedicated comprehension of the specific way GenAI is implemented into the Banking Information Systems (BIS) is still scattered. This paper fills that gap by developing a systematic literature review (SLR) of 228 peer-reviewed articles published between 2018 and 2025, based on the PRISMA protocol. The study is based on Dynamic Capabilities Theory, Resource-Based View, and Organizational Learning Theory and recognizes nine major GenAI technology enablers, including Large Language Models (LLMs) up to Synthetic Data Generation, and aligns them with 80 Banking Information Systems (BIS) functions. These encompass some of the key areas like detection of fraud, credit risk evaluation, adherence to regulations, individual customer interactions, and next generation banking analytics. This review provides the strategic framework of risk-resilient, inclusive, and innovation-based BIS development by connecting the emergent GenAI technologies to the real-time banking workflow. Its results provide practical recommendations to banks, policymakers, and fintech creators who want to manage the AI-driven revolution in the financial service industry.