Can Zero-Shot Large Language Models Be Trusted for Credit-Risk Prediction? An Explainability Audit Against LightGBM on Loan Default Data

Zero-shot large language models (LLMs) offer a low-barrier route to credit-risk prediction, but regulated use requires evidence on discrimination, probability reliability, and auditable decision patterns. We compare four open-source LLMs (Gemma-2-9B, Llama-3.2-3B, Mistral-7B-v0.3, and Qwen2.5-7B) with LightGBM on 396,030 U.S. LendingClub loans issued from 2007 to 2016. Treating loans as independent and identically distributed observations from this pooled cross-sectional population, we evaluate every model on the same random 20% holdout. All models receive the same 21 structured predictors and output repayment probabilities. We audit empirical decision patterns with SHapley Additive exPlanations (SHAP); separately, each LLM supplies one context-free positive, negative, or neutral direction per feature, which we treat as its feature-level self-explanation. LightGBM achieved ROC-AUC 0.724, PR-AUC 0.907, and Brier score 0.141, compared with 0.669, 0.880, and 0.204 for the best-discriminating LLM, Gemma-2-9B. An equal-weight LightGBM–Gemma ensemble did not improve any metric. In an eight-cycle SHAP audit, each LLM shared six or seven of LightGBM’s ten most important features; these self-explanations agreed with empirical SHAP directions for only 33–67% of assessable top features, revealing self-explanation unfaithfulness under this directional criterion. A nested four-cycle estimate reproduced every LLM’s top-10 set and nearly identical importance ranks, although small magnitude and distribution shifts were statistically detectable. These results caution against standalone zero-shot LLM use in credit risk and support calibration checks, explainability audits, interpretable benchmarks, and human oversight before deployment.