用大模型自动分析保险理赔，提升处理效率与准确性
Claim Automation using Large Language Model
- 基于历史理赔数据微调大模型，生成结构化处理建议
- 80%的案例推荐结果与真实处理方案高度一致
- 适合需要合规性与可解释性的保险自动化场景
尽管大语言模型在通用语言任务中表现优异，但在保险等受监管、数据敏感的领域部署仍有限。利用数百万条历史保修理赔数据，我们提出一种本地部署的治理感知语言建模组件，从非结构化理赔描述中生成结构化的纠正措施建议。通过低秩适应（LoRA）微调预训练大模型，将其限定为理赔处理流程中的初始决策模块，以加速理赔员的判断。我们采用多维度评估框架，结合自动语义相似度指标与人工评估，严格检验其实际效用与预测准确性。结果显示，领域特定微调显著优于商用通用模型和提示工程方法，约80%的评估案例达到与真实纠正措施近乎完全匹配的效果。研究提供了理论与实证证据，证明领域自适应微调能更贴近真实业务数据分布，展现出作为可靠、可控保险应用模块的巨大潜力。
原文摘要 · Abstract (English)
While Large Language Models (LLMs) have achieved strong performance on general-purpose language tasks, their deployment in regulated and data-sensitive domains, including insurance, remains limited. Leveraging millions of historical warranty claims, we propose a locally deployed governance-aware language modeling component that generates structured corrective-action recommendations from unstructured claim narratives. We fine-tune pretrained LLMs using Low-Rank Adaptation (LoRA), scoping the model to an initial decision module within the claim processing pipeline to speed up claim adjusters' decisions. We assess this module using a multi-dimensional evaluation framework that combines automated semantic similarity metrics with human evaluation, enabling a rigorous examination of both practical utility and predictive accuracy. Our results show that domain-specific fine-tuning substantially outperforms commercial general-purpose and prompt-based LLMs, with approximately 80% of the evaluated cases achieving near-identical matches to ground-truth corrective actions. Overall, this study provides both theoretical and empirical evidence to prove that domain-adaptive fine-tuning can align model output distributions more closely with real-world operational data, demonstrating its promise as a reliable and governable building block for insurance applications.
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