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Interpretable LLMs for Credit Risk: A Systematic Review and Taxonomy

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  • Muhammed Golec
  • Maha AlabdulJalil

Abstract

Large Language Models (LLM), which have developed in recent years, enable credit risk assessment through the analysis of financial texts such as analyst reports and corporate disclosures. This paper presents the first systematic review and taxonomy focusing on LLMbased approaches in credit risk estimation. We determined the basic model architectures by selecting 60 relevant papers published between 2020-2025 with the PRISMA research strategy. And we examined the data used for scenarios such as credit default prediction and risk analysis. Since the main focus of the paper is interpretability, we classify concepts such as explainability mechanisms, chain of thought prompts and natural language justifications for LLM-based credit models. The taxonomy organizes the literature under four main headings: model architectures, data types, explainability mechanisms and application areas. Based on this analysis, we highlight the main future trends and research gaps for LLM-based credit scoring systems. This paper aims to be a reference paper for artificial intelligence and financial researchers.

Suggested Citation

  • Muhammed Golec & Maha AlabdulJalil, 2025. "Interpretable LLMs for Credit Risk: A Systematic Review and Taxonomy," Papers 2506.04290, arXiv.org, revised Jun 2025.
    • Handle: RePEc:arx:papers:2506.04290
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    References listed on IDEAS

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    1. Liu, Yingnan & Bu, Ningbo & Li, Zhiqiang & Zhang, Yongmin & Zhao, Zhenyu, 2025. "AT-FinGPT: Financial risk prediction via an audio-text large language model," Finance Research Letters, Elsevier, vol. 77(C).
    2. Julian Junyan Wang & Victor Xiaoqi Wang, 2025. "Assessing Consistency and Reproducibility in the Outputs of Large Language Models: Evidence Across Diverse Finance and Accounting Tasks," Papers 2503.16974, arXiv.org, revised Sep 2025.
    3. Yuqi Nie & Yaxuan Kong & Xiaowen Dong & John M. Mulvey & H. Vincent Poor & Qingsong Wen & Stefan Zohren, 2024. "A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges," Papers 2406.11903, arXiv.org.
    4. Jaskaran Singh Walia & Aarush Sinha & Srinitish Srinivasan & Srihari Unnikrishnan, 2025. "Predicting Liquidity-Aware Bond Yields using Causal GANs and Deep Reinforcement Learning with LLM Evaluation," Papers 2502.17011, arXiv.org.
    5. Xue Wen Tan & Stanley Kok, 2024. "Explainable Risk Classification in Financial Reports," Papers 2405.01881, arXiv.org, revised Dec 2024.
    6. Khaoula Idbenjra & Kristof Coussement & Arno de Caigny, 2024. "Investigating the beneficial impact of segmentation-based modelling for credit scoring," Post-Print hal-04543449, HAL.
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    Cited by:

    1. Hanyong Cho & Jang Ho Kim, 2026. "Constructing a Portfolio Optimization Benchmark Framework for Evaluating Large Language Models," Papers 2603.09301, arXiv.org.

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