ENHANCING PERSONALIZED UNDERWRITING THROUGH UNSTRUCTURED TEXT ANALYTICS: AN NLP APPROACH TO POLICY DOCUMENT AND CLAIM HISTORY REVIEW

Unstructured text analytics has emerged as a powerful tool for transforming underwriting practices in the insurance industry. Traditional underwriting relies heavily on manual review of policy documents, proposals, and historical claims, which can be time-consuming, inconsistent, and prone to human error. Natural Language Processing offers advanced capabilities to extract, classify, and interpret information from unstructured text, enabling more accurate risk assessment and personalized underwriting decisions. This chapter examines how NLP techniques such as text mining, named entity recognition, sentiment analysis, topic modelling, and document classification can enhance the underwriting process by automating data extraction and identifying hidden patterns in policy and claims data. Intelligent text analytics supports insurers in identifying high-risk indicators, detecting fraudulent claims, assessing customer behaviour, and tailoring policy terms to individual risk profiles. While these advancements promise improved efficiency and decision quality, they also introduce challenges related to data quality, model interpretability, regulatory compliance, and ethical considerations. Through a conceptual analysis of emerging research and industry practices, this chapter highlights the transformative potential of NLP-driven unstructured text analytics in developing personalized underwriting frameworks. It concludes with recommendations for practitioners and identifies opportunities for future research in responsible and scalable AI adoption in insurance operations.