Impact of AI in the General Insurance underwriting factors

Abstract

The paper discusses the impact of artificial intelligence (AI) on the insurance underwriting process, highlighting the benefits of predictive analytics in better understanding risk and providing real-time data for quotes on demand. The use of AI algorithms in underwriting can help underwriters to focus on more complex and strategic aspects of their work while automating tedious underwriting tasks.

The paper presents the AI Based Risk Intelligence Model (RIM), which combines data analytics, machine learning algorithms, and predictive modelling to provide a comprehensive view of an insurer's risk exposure. The RIM consists of four key components: Data Collection and Integration, Risk Analysis and Assessment, Predictive Modelling and Scenario Analysis, and Risk Management and Monitoring.

The RIM is a valuable tool for improving overall risk management in the industry, enabling insurers to identify and manage risks more effectively and make informed underwriting decisions. The model provides a comprehensive analysis of risk factors beyond traditional methods of risk assessment, such as age, gender, and occupation and habits, taking into account a wide range of variables, such as lifestyle choices, health behaviours, and environmental factors.

Implementing appropriate model with the use of AI the insurers can accurately assess moral and morale hazards and determine an appropriate strategy for their clients. This can result in the better risk assessment and  more accurate pricing for insurance products and can also help insurers to identify reinsurance arrangements. The RIM can be customized to meet the specific needs of different insurers and can be a valuable tool for improving overall risk management in the industry.