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

Author

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  • Mohamed Djafar Henni

    (Department of Economics, College of Business, Islamic University of Madinah, Saudi Arabia)

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.

Suggested Citation

  • Mohamed Djafar Henni, 2026. "Large Language Models in Digital Banking: A Systematic Review and Implications for Financial Inclusion (2015–2025)," International Journal of Economic Performance (IJEP), ALPEC The performance of Algerian economic institutions in light of international economic mobility , Boumerdes University, vol. 9(01), pages :20-52, July.
    • Handle: RePEc:bjm:ijep00:v:9:y:2026:i:01:id:400
    DOI: 10.54241/2065-009-001-002
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    References listed on IDEAS

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    1. Zongxiao Wu & Yizhe Dong & Yaoyiran Li & Baofeng Shi, 2025. "Unleashing the power of text for credit default prediction: Comparing human-written and generative AI-refined texts," Papers 2503.18029, arXiv.org.
    2. Kamesh Korangi & Christophe Mues & Cristi'an Bravo, 2021. "A transformer-based model for default prediction in mid-cap corporate markets," Papers 2111.09902, arXiv.org, revised Apr 2023.
    3. Tsionas, Mike & Mamatzakis, Emmanuel C., 2017. "Corrigendum to ‘Adjustment costs in the technical efficiency: An application to global banking’ [European Journal of Operational Research 256 (2017) 640–649]," European Journal of Operational Research, Elsevier, vol. 257(2), pages 704-704.
    4. Wu, Zongxiao & Dong, Yizhe & Li, Yaoyiran & Shi, Baofeng, 2025. "Unleashing the power of text for credit default prediction: Comparing human-written and generative AI-refined texts," European Journal of Operational Research, Elsevier, vol. 326(3), pages 691-706.
    5. Korangi, Kamesh & Mues, Christophe & Bravo, Cristián, 2023. "A transformer-based model for default prediction in mid-cap corporate markets," European Journal of Operational Research, Elsevier, vol. 308(1), pages 306-320.
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