用自然语言处理从文本中提取保险风险信息，提升定价精准度。
InsurTech innovation using natural language processing
- 通过NLP将非结构化文本转为可分析数据，增强保险评估能力。
- 实现特征去偏、压缩与行业分类，提升商业保险定价精度。
- 适合关注智能风控与数据驱动的保险科技从业者参考。
随着保险科技的快速发展，传统保险公司正探索替代数据源与先进技术以保持竞争力。本文结合概念框架与实际案例，探讨自然语言处理（NLP）在保险运营中的新兴应用，重点在于将原始非结构化文本转化为适用于精算分析与决策的结构化数据。基于一家保险科技合作方提供的真实替代数据，我们应用多种NLP技术，实现了商业保险场景下的特征去偏、特征压缩与行业分类。这些由文本衍生的洞察不仅补充并优化了传统的保险定价因子，还通过新型行业分类方法提供了评估潜在风险的新视角。研究表明，NLP不仅是辅助工具，更是现代数据驱动保险分析的核心基础。
原文摘要 · Abstract (English)
With the rapid rise of InsurTech, traditional insurance companies are increasingly exploring alternative data sources and advanced technologies to sustain their competitive edge. This paper provides both a conceptual overview and practical case studies of natural language processing (NLP) and its emerging applications within insurance operations, focusing on transforming raw, unstructured text into structured data suitable for actuarial analysis and decision-making. Leveraging real-world alternative data provided by an InsurTech industry partner that enriches traditional insurance data sources, we apply various NLP techniques to demonstrate feature de-biasing, feature compression, and industry classification in the commercial insurance context. These enriched, text-derived insights not only add to and refine traditional rating factors for commercial insurance pricing but also offer novel perspectives for assessing underlying risk by introducing novel industry classification techniques. Through these demonstrations, we show that NLP is not merely a supplementary tool but a foundational element of modern, data-driven insurance analytics.
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