InsurTech innovation using natural language processing

TL;DR

This paper explores how natural language processing (NLP) can transform unstructured insurance data into valuable structured insights, enhancing risk assessment and pricing in InsurTech applications through practical case studies.

Contribution

It provides a conceptual overview and demonstrates practical NLP techniques applied to insurance data, introducing novel industry classification and feature de-biasing methods.

Findings

NLP techniques improve insurance data analysis and risk assessment.

Enriched text data enhances traditional insurance rating factors.

NLP is a foundational element in modern insurance analytics.

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.