AI-Enhanced Risk Assessment Models in Insurance

Abstract

Insurance, as a social and economic institution, has often been associated with uncertainty, risk, and the unpredictability of the future. As a part of this, the companies and individuals operating within this highly dynamic sector face an array of risks ranging from the type of product or service offered to operational ones such as customer default. To ensure long-term profitability and performance, better risk management improves decision processes, beneficially influencing an entire business operating ecosystem. Traditionally, the industry has attempted to adopt three different methodologies for constructing state-of-the-art business intelligence and data-driven risk assessment models. Namely, these are: expert systems using rule-based techniques, statistical methods, and optimization models. However, these conventional methodologies have proven to be high in effectiveness but expensive in terms of adaptability and efficiency, which makes their applicability complex in modern environments.