Design, Results and Industry Implications of the World's First Insurance Large Language Model Evaluation Benchmark

2025-11-11

AI Summary

The insurance domain has long lacked a specialized large language model (LLM) evaluation benchmark; existing general-purpose models exhibit significant limitations in actuarial reasoning and regulatory compliance, while domain-specific models suffer from insufficient business adaptability and compliance robustness. Method: We introduce CUFEInse v1.0—the first multidimensional insurance-specific evaluation benchmark—comprising five dimensions: domain expertise, industry understanding, safety and compliance, intelligent agent capabilities, and logical rigor. It includes 54 fine-grained metrics and 14,430 high-quality questions, underpinned by a novel “quantification-oriented, expert-driven, multi-stakeholder-validated” evaluation paradigm integrating structured knowledge modeling, iterative expert verification, and mixed qualitative–quantitative analysis. Contribution/Results: Comprehensive evaluation of 11 state-of-the-art models reveals systematic weaknesses in underwriting/claims reasoning and compliant document generation. Empirical results further validate the efficacy—and delineate the limits—of domain-adaptive fine-tuning.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Safety and Robustness
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

This paper comprehensively elaborates on the construction methodology, multi-dimensional evaluation system, and underlying design philosophy of CUFEInse v1.0. Adhering to the principles of"quantitative-oriented, expert-driven, and multi-validation,"the benchmark establishes an evaluation framework covering 5 core dimensions, 54 sub-indicators, and 14,430 high-quality questions, encompassing insurance theoretical knowledge, industry understanding, safety and compliance, intelligent agent application, and logical rigor. Based on this benchmark, a comprehensive evaluation was conducted on 11 mainstream large language models. The evaluation results reveal that general-purpose models suffer from common bottlenecks such as weak actuarial capabilities and inadequate compliance adaptation. High-quality domain-specific training demonstrates significant advantages in insurance vertical scenarios but exhibits shortcomings in business adaptation and compliance. The evaluation also accurately identifies the common bottlenecks of current large models in professional scenarios such as insurance actuarial, underwriting and claim settlement reasoning, and compliant marketing copywriting. The establishment of CUFEInse not only fills the gap in professional evaluation benchmarks for the insurance field, providing academia and industry with a professional, systematic, and authoritative evaluation tool, but also its construction concept and methodology offer important references for the evaluation paradigm of large models in vertical fields, serving as an authoritative reference for academic model optimization and industrial model selection. Finally, the paper looks forward to the future iteration direction of the evaluation benchmark and the core development direction of"domain adaptation + reasoning enhancement"for insurance large models.

Problem

• Evaluates insurance domain capabilities of large language models
• Identifies bottlenecks in actuarial and compliance adaptation
• Provides professional benchmark for insurance industry applications

Innovation

• Created insurance-specific benchmark with 14430 questions
• Evaluated 11 models across 5 core insurance dimensions
• Identified domain adaptation and reasoning enhancement needs