首份系统综述，梳理大模型在信贷风险评估中的可解释性方法。
Interpretable LLMs for Credit Risk: A Systematic Review and Taxonomy
- 基于PRISMA策略筛选60篇论文，构建四大分类体系。
- 聚焦可解释机制，涵盖思维链提示与自然语言解释等技术。
- 适合金融AI研究者参考，指明未来研究方向与空白。
近年来，大型语言模型（LLM）通过分析分析师报告、企业披露等财务文本，实现了信贷风险评估。本文首次提出针对基于LLM的信贷风险估计方法的系统综述与分类体系。采用PRISMA研究策略，筛选2020–2025年间发表的60篇相关论文，分析其模型架构、数据类型（如信用违约预测、风险分析场景）、可解释性机制（包括解释性方法、思维链提示、自然语言理由生成）及应用领域。分类体系涵盖四大主类：模型架构、数据类型、可解释性机制与应用场景。基于分析结果，指出基于LLM的信贷评分系统未来主要趋势与研究空白。本论文旨在为人工智能与金融研究者提供参考。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。