InsurTech innovation using natural language processing
Fuente:
arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866908719339536384 |
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| author | Dong, Panyi Quan, Zhiyu |
| author_facet | Dong, Panyi Quan, Zhiyu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_21112 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | InsurTech innovation using natural language processing Dong, Panyi Quan, Zhiyu Computation and Language Machine Learning 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. |
| title | InsurTech innovation using natural language processing |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2507.21112 |