Application of Machine Learning Techniques in Insurance Underwriting

Abstract

Purpose: The general purpose of the study was to examine the application of machine learning techniques in insurance underwriting.

Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive's time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library.

Findings: The findings reveal that there exists a contextual and methodological gap relating to the application of machine learning techniques in insurance underwriting. The study on the application of machine learning techniques in insurance underwriting concludes that these algorithms offer significant potential to enhance accuracy, efficiency, and risk assessment capabilities in the industry. By leveraging advanced analytics and innovative technologies such as telematics data and natural language processing, insurers can make more informed underwriting decisions and pricing strategies. However, challenges remain in ensuring model interpretability, fairness, and regulatory compliance. Continued research and development efforts are essential to address these challenges and unlock the transformative benefits of machine learning in underwriting while fostering a culture of trust and accountability in AI adoption.

Unique Contribution to Theory, Practice and Policy: The Decision theory, Information theory and Bayesian Decision theory may be used to anchor future studies on insurance underwriting. The study provided several recommendations to enhance underwriting processes and improve risk assessment accuracy. It recommended integrating ensemble learning methods, emphasizing feature selection and dimensionality reduction, continuously monitoring and updating models, enhancing data governance and privacy protections, and investing in talent and training. These recommendations aimed to optimize underwriting accuracy, efficiency, and risk management capabilities in an evolving data-driven landscape, ensuring insurers remained competitive and compliant with regulatory standards.