Large Language Models in Digital Banking: A Systematic Review and Implications for Financial Inclusion (2015–2025)

Mohamed Djafar Henni
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Mohamed Djafar Henni: Department of Economics, College of Business, Islamic University of Madinah, Saudi Arabia

IJEP, 2026, vol. 9, issue 01, Pages : 20-52

Abstract: The rapid emergence of Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) is fundamentally transforming the financial sector by enabling advanced capabilities in processing large volumes of unstructured textual data. While previous studies have explored broad applications of artificial intelligence in finance, a dedicated synthesis focusing specifically on banking management and its implications for digital banking transformation remains limited. In response, this article provides a systematic state-of-the-art review of 91 studies published between 2015 and 2025, identified through the Web of Science and Scopus databases using a PRISMA-based methodology.The review synthesizes the literature across five key domains: (1) Policy Interpretation and Sentiment Analysis, (2) Risk Management and Financial Prediction, (3) Regulatory Technology (RegTech) and Compliance, (4) Operational Efficiency and Process Management, and (5) Customer-Facing Applications and Advisory Services. Bibliometric evidence reveals a rapid acceleration of scholarly interest beginning in 2023, with Risk Management and Financial Prediction representing the most prominent research stream (28.7% of publications). The findings demonstrate how LLM-driven tools are redefining traditional banking management practices by enabling contextual interpretation of central bank communications, improving financial risk forecasting, automating regulatory compliance processes, and enhancing operational decision-making. digital banking services in improving customer interaction, expanding access to financial information, and potentially supporting broader financial inclusion within increasingly digitalized financial systems. Finally, the study identifies critical limitations in the existing literature particularly regarding data privacy, model interpretability, and geographic bias and proposes a strategic roadmap outlining future research directions for responsible and inclusive AI adoption in banking.

Keywords: Large Language Models; Banking Management; Generative AI; Fintech; Systematic Review; Risk Management and Financial Prediction (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:bjm:ijep00:v:9:y:2026:i:01:id:400

DOI: 10.54241/2065-009-001-002

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