Explainable Artificial Intelligence (XAI) in Insurance

Explainable Artificial Intelligence (XAI) makes human-machine interactions more transparent and understandable. Given its access to vast amounts of private policyholder data and its critical role in promoting innovation and societal growth, the insurance sector has a great opportunity to demonstrate the capabilities of XAI. The present state of artificial intelligence (AI) applications in insurance practices and research is examined in this article, with an emphasis on their explainability. Through a thorough analysis of 419 primary research papers from 2000 to 2021 from a variety of scholarly sources, the study finds 103 publications that provide an overview of the state of XAI in insurance literature. Using an analysis and classification of these papers, it highlights the use of XAI approaches in critical insurance operations such as claims management, underwriting, and actuarial pricing, among others. In the insurance industry, the study highlights information distillation and rule extraction as the main XAI methodologies. It also promotes the development of interpretable XAI models. By breaking down complex structures into simpler, rule-based ones, this method makes it easier to design models that are easier to understand. By incorporating moral values, openness, and confidence into system design, XAI is a substantial breakthrough in AI. Stakeholders including industry practitioners, regulators, and XAI developers can gain important insights by evaluating these emphasis points within the insurance sector and determining whether regions are ready for more development. By analyzing its applications in the insurance industry, this study provides a fresh viewpoint in addition to enhancing the multidisciplinary understanding of applied XAI. Furthermore, by offering a more nuanced definition based on a thorough analysis of XAI literature within the insurance sector, it adds to the continuing conversations about a precise definition of XAI.