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cloud.google.com·8h ago
AI Research
arXiv CS.AI·Aug 19·5 min read
Communicating Credit Risk with Large Language Models: Evaluation of Explanations from Standard and Alternative Data-Based Models
TL;DR
Large Language Models (LLMs) can effectively translate complex credit risk model explanations into accessible narratives for stakeholders. This study reveals that the quality of explanations is more dependent on evidence representation than the model used.
✦ Why It Matters
Consider integrating LLMs to enhance communication of credit risk assessments to stakeholders today.
Key Takeaways
- Utilize LLMs to improve stakeholder communication in credit risk.
- Focus on evidence representation to enhance explanation quality.
- Train LLMs on domain-specific data for better alignment.
- Recognize the differing evidentiary standards of professionals versus non-professionals.
Full Summary
Credit decisioning requires accurate and explainable model outputs to ensure compliance and fairness. This study explores whether Large Language Models (LLMs) can simplify explanations from advanced credit risk models like eXtreme Gradient Boosting (XGBoost) and Graph Neural Networks (GNNs).
Using Freddie Mac loan-level data, three pipelines were developed: one using standard tabular data and two utilizing alternative data sources. Explanations were generated with different LLM configurations, including a small fine-tuned model and a large commercial model.
Findings indicate that the representation of evidence significantly impacts explanation quality, while LLMs reliably identify influential factors but struggle with the direction of influence. Additionally, credit risk professionals demand stricter evidence standards compared to non-professionals.
These insights highlight the importance of domain-aligned LLMs in regulated credit environments.