AI-Powered Tools for Streamlining Insurance Underwriting Processes

Abstract

Insurance underwriting is an important part of the insurance industry. Underwriting aims to evaluate insurance applications to determine whether they can be accepted, declined, or accepted with special conditions. Underwriting has gone through several significant technological changes, but the underwriting process still involves significant manual activities. Therefore, underwriting automation is a hot topic for both the insurer and the insured. One approach explores the complex web of factors that underwriters must consider to specify what constitutes desirable or undesirable risks for a specific underwriting practice. This is not a decision support environment, but rather a formal arrangement of decision rule criteria.

There is increasing evidence about the importance of AI technologies and machine learning algorithms for making firms operate efficiently and effectively. This leads to a growing interest in the application of AI in underwriting, shares some good examples of the use of AI to enhance results, and summarizes some of the key issues and challenges. Underwriters' decisions are future outcomes based on the current underwriting criteria. Machine learning models enable organizations to combine a variety of factors and uncover hidden relationships that could serve as additional business underwriting factors to improve prediction accuracy. The paper aims to increase knowledge of the potential for AI tools to support insurers in underwriting decisions. It also seeks to explore the scope of the application areas and AI tools that could potentially be used to disrupt market practices in underwriting. The study focuses on the potential of AI to automate data heuristics. As a result, the study seeks to inform a wider audience about the potential changes in the underwriters' assessment procedures that will be feasible, and the impact of these tools on insurers and individuals.