A Controlled LLM Workflow for Evidence-Grounded Diagnosis of Risk Score Differences

Abstract

We propose a controlled Large Language Model (LLM) workflow for explaining risk score differences using similar historical transactions. The workflow combines reference retrieval and contrastive SHapley Additive exPlanations (SHAP) evidence with reference-based LLM reasoning and deterministic validation. Experiments on a test dataset show that the workflow produces more consistent, evidence-grounded and traceable diagnoses than the same LLM without structured workflow guidance.

arXiv commentAccepted for presentation at the International Conference on Electrical and Computer Engineering Researches (ICECER 2026), December 3-5, 2026