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[2602.18895] Could Large Language Models work as Post-hoc Explainability Tools in Credit Risk Models?
Summary
This paper explores the potential of large language models (LLMs) as post-hoc explainability tools in credit risk models, evaluating their effectiveness in translating complex model outputs for non-technical stakeholders.
Why It Matters
With the increasing complexity of credit risk models, effective communication of model outputs to stakeholders is crucial. This research highlights how LLMs can serve as a bridge between technical data and stakeholder understanding, enhancing transparency and governance in financial decision-making.
Key Takeaways
- LLMs can effectively translate complex model outputs for non-technical stakeholders.
- Few-shot prompting improves feature overlap in some models but not consistently across all types.
- LLMs should be viewed as narrative interfaces rather than replacements for traditional explainability tools.
Quantitative Finance > Risk Management arXiv:2602.18895 (q-fin) [Submitted on 21 Feb 2026] Title:Could Large Language Models work as Post-hoc Explainability Tools in Credit Risk Models? Authors:Wenxi Geng, Dingyuan Liu, Liya Li, Yiqing Wang View a PDF of the paper titled Could Large Language Models work as Post-hoc Explainability Tools in Credit Risk Models?, by Wenxi Geng and 3 other authors View PDF HTML (experimental) Abstract:Post-hoc explainability is central to credit risk model governance, yet widely used tools such as coefficient-based attributions and SHapley Additive exPlanations (SHAP) often produce numerical outputs that are difficult to communicate to non-technical stakeholders. This paper investigates whether large language models (LLMs) can serve as post-hoc explainability tools for credit risk predictions through in-context learning, focusing on two roles: translators and autonomous explainers. Using a personal lending dataset from LendingClub, we evaluate three commercial LLMs, including GPT-4-turbo, Claude Sonnet 4, and Gemini-2.0-Flash. Results provide strong evidence for the translator role. In contrast, autonomous explanations show low alignment with model-based attributions. Few-shot prompting improves feature overlap for logistic regression but does not consistently benefit XGBoost, suggesting that LLMs have limited capacity to recover non-linear, interaction-driven reasoning from prompt cues alone. Our findings position LLMs as effective narrative i...