Interpretable LLMs for credit risk: A systematic review and taxonomy
Expert Systems with Applications, cilt.306, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Derleme
- Cilt numarası: 306
- Basım Tarihi: 2026
- Doi Numarası: 10.1016/j.eswa.2025.130941
- Dergi Adı: Expert Systems with Applications
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC, Public Affairs Index
- Anahtar Kelimeler: Large language models (LLMs), Credit risk assessment explainable, Artificial intelligence (XAI), Financial text analysis, Interpretable transformers, Natural language processing (NLP), Taxonomy
- Boğaziçi Üniversitesi Adresli: Evet
Özet
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. To the best of our knowledge, this paper presents the first systematic review and taxonomy focusing on LLM-based 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.