Abstract

P&C insurers have an important role in addressing financial risk management needs but now struggle to respond to the new forms of risk. Historical analysis and actuarial calculations, which form the backbone of classical approaches to risk measurement and management, are not well suited to such new kinds of risks as climate change, cyber risks, and business cycle risks. These conventional approaches are also a static method for selling, which has limited potential in changing quickly with new market and consumer changes. However, the development of AI, big data, or advanced predictive analytics has provided a new paradigm to such challenges to cope with and analyze big data dynamically to derive Applying a combination of best Machine Learning (ML), natural language processing (NLP) and other AI techniques, it will become possible to predict risks exposures, as well as optimize underwriting and claims processing. In this paper, the author investigates the opportunities that AI carries within the sphere of reformation of risk evaluation approaches in P&C insurance. In particular, it discusses the ever-emerging field with AI models integrated into operations, underwriting, fraud detection, claims modelling, and loss computation. The applied research uses a comprehensive bibliographical review to present past and present practices and assess modern AI implementations. This paper uses research approaches and case studies to prove that AI can solve some problems and bring cost-saving solutions. Moreover, it emphasizes the ability of predictive analytics to enhance customer satisfaction at the policy level as well as optimize operations. However, the paper also explores the dirty side of implementation, including the stipulations surrounding compliance and data privacy and thus provides an objective analysis of the future of AI use in the P&C Insurance Industry.