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InsurTech innovation using natural language processing

Panyi Dong, Zhiyu Quan

TL;DR

The paper tackles the challenge of turning unstructured InsurTech text into actionable actuarial insight. It couples foundational NLP techniques with real-world datasets to demonstrate feature de-biasing, context-aware embeddings for high-cardinality categoricals, and automated industry classification, while also exploring standalone NLP applications. The work highlights data enrichment through external textual sources as a means to improve pricing, underwriting, and risk assessment, and compares unsupervised industry classification with LLM baselines, showing practical efficiency and interpretability advantages. Overall, NLP is presented as a foundational, data-efficient tool that can transform underwriting accuracy, fairness, and operational agility in the insurance sector.

Abstract

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.

InsurTech innovation using natural language processing

TL;DR

The paper tackles the challenge of turning unstructured InsurTech text into actionable actuarial insight. It couples foundational NLP techniques with real-world datasets to demonstrate feature de-biasing, context-aware embeddings for high-cardinality categoricals, and automated industry classification, while also exploring standalone NLP applications. The work highlights data enrichment through external textual sources as a means to improve pricing, underwriting, and risk assessment, and compares unsupervised industry classification with LLM baselines, showing practical efficiency and interpretability advantages. Overall, NLP is presented as a foundational, data-efficient tool that can transform underwriting accuracy, fairness, and operational agility in the insurance sector.

Abstract

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.

Paper Structure

This paper contains 23 sections, 16 equations, 11 figures, 8 tables.

Table of Contents

  1. Introduction
  2. A Brief Survey of NLP
  3. Text to Numbers
  4. Text Preprocessing
  5. Text Cleaning
  6. Lexicon Normalization: Tokenizing, Stemming, Lemmatization
  7. Embedding
  8. Vector Space Model
  9. Statistical Language Model
  10. Neural Language Model
  11. NLP in Action: Use-cases Leveraging InsurTech Datasets
  12. InsurTech Data
  13. Data Enrichment for Machine Learning Tasks
  14. Data Enrichment Using Text
  15. De-Biasing Features Through Sentiment Analysis
  16. ...and 8 more sections

Figures (11)

  • Figure 1: Neural LM Architectures
  • Figure 2: Summary of Text to Numbers Solutions
  • Figure 3: Comparison of Review Polarity Distribution by Star Ratings: California vs. Other States
  • Figure 4: Comparison of Review Polarity Distribution by Star Ratings: Accommodation and Food Services vs. Other Industry
  • Figure 5: Examples of Category Embedding Vectors in First Two Principal Components
  • ...and 6 more figures