AI

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

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 20/10/2026, 04:03:24 UTC.

Briefings

A new controlled LLM workflow was developed to explain risk score differences using historical transactions, combining reference retrieval, contrastive SHAP explanations, and deterministic validation. Experiments on a test dataset demonstrated that this structured approach yields more consistent, evidence-grounded, and traceable diagnoses compared to using the same LLM without such guidance.

Key points

• The workflow integrates reference retrieval, contrastive SHAP Additive exPlanations, and deterministic validation to explain risk score differences.
• Test results indicate the workflow produces more consistent, evidence-grounded, and traceable diagnoses than the unstructured LLM approach.

Context

This research addresses the challenge of explaining complex risk score variations by leveraging historical transaction data and structured LLM reasoning, aiming to improve diagnostic reliability.

Limitations

The study relies on a single test dataset; results may not generalize to all populations or risk scenarios. The workflow's effectiveness depends on the quality and availability of historical transaction data.

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