The Transformation of P&C Underwriting Through Data and Analytics - Rahul Deb Chakladar (North American Journal of Engineering Research, 2023)
Enhancing underwriting performance through advanced data and analytics is essential for competitiveness in the P&C insurance sector in Europe and North America. Leading insurers leverage sophisticated data-driven underwriting models to improve loss ratios, increase new business premiums, and boost retention in profitable segments. This paper examines how insurers utilize data and analytics across personal and commercial lines to proactively shape strategic outlooks, similar to hedge funds in capital markets. It explores best practices for integrating advanced analytics within underwriting, focusing on agile, cross-functional teams, skill development, training, and feedback mechanisms. Leading insurers employ agile and predictive stack capabilities, leveraging extensive third-party data and advanced technological infrastructures to transform risk assessment and enhance decision-making. They perform granular risk segmentations using machine learning models, improving pricing accuracy and reducing losses. In small commercial lines, digital platforms with analytics-based underwriting models offer a superior broker-agent experience, achieving high straight-through processing rates and enhancing agent efficiency. The transition to AI-powered underwriting addresses the inefficiencies of manual processes, offering improved efficiency, risk assessment, and customer satisfaction. Adopting AI is crucial for maintaining competitiveness, attracting top talent, and achieving strategic goals. The modernization of underwriting through AI is a vital initiative for forward-thinking insurers, enabling them to capitalize on emerging opportunities and ensure long-term success in a competitive landscape.