Explainable Artificial Intelligence (XAI) in Insurance

Abstract

Explainable Artificial Intelligence (XAI) models allow for a more transparent and understandable relationship between humans and machines. The insurance industry represents a fundamental opportunity to demonstrate the potential of XAI, with the industry’s vast stores of sensitive data on policyholders and centrality in societal progress and innovation. This paper analyses current Artificial Intelligence (AI) applications in insurance industry practices and insurance research to assess their degree of explainability. Using search terms representative of (X)AI applications in insurance, 419 original research articles were screened from IEEE Xplore, ACM Digital Library, Scopus, Web of Science and Business Source Complete and EconLit. The resulting 103 articles (between the years 2000–2021) representing the current state-of-the-art of XAI in insurance literature are analysed and classified, highlighting the prevalence of XAI methods at the various stages of the insurance value chain. The study finds that XAI methods are particularly prevalent in claims management, underwriting and actuarial pricing practices. Simplification methods, called knowledge distillation and rule extraction, are identified as the primary XAI technique used within the insurance value chain. This is important as the combination of large models to create a smaller, more manageable model with distinct association rules aids in building XAI models which are regularly understandable. XAI is an important evolution of AI to ensure trust, transparency and moral values are embedded within the system’s ecosystem. The assessment of these XAI foci in the context of the insurance industry proves a worthwhile exploration into the unique advantages of XAI, highlighting to industry professionals, regulators and XAI developers where particular focus should be directed in the further development of XAI. This is the first study to analyse XAI’s current applications within the insurance industry, while simultaneously contributing to the interdisciplinary understanding of applied XAI. Advancing the literature on adequate XAI definitions, the authors propose an adapted definition of XAI informed by the systematic review of XAI literature in insurance.

Weather Conditions and Telematics Panel Data in Monthly Motor Insurance Claim Frequency Models

“…Another issue that has recently been investigated in the literature is the existence of a cut-off value of distance driven, below which drivers who have a traditional policy should switch to a PAYD policy (Cheng et al 2022). The impact of non-linearities and interactions may be better captured with, for example, neural nets or the combined GLM/NN methods that have been proposed in the literature (see Blier-Wong et al 2020;Owens et al 2022). However, they use cross-sectional information on insurance claims, and no studies have used panel data.…”

Potential Applications of Explainable Artificial Intelligence to Actuarial Problems

“…On the other hand, although they do not entirely cover the focus of an actuary's work, some reviews develop reference approaches such as a SLR in a specific area of actuarial work. For example, the work presented in [10] analyzes the different applications of AI in the insurance industry, identifying and classifying them according to their levels of explainability and contributions at the different stages of the insurance cycle. In this sense, it identifies how XAI methods predominate in the claim, underwriting, and pricing stages, contributing to the simplification of models and extracting relationships or rules to understand the established relationships.…”

Explainable Image Classification: The Journey So Far and the Road Ahead

“…They call for applying techniques to extract causal explanations in a privacy-preserving manner, a crucial requirement that must be satisfied in finance. On the other hand, Owens et al [41] focus exclusively on the sub-domain of finance dealing with insurance and advocate the need for attention of XAI researchers to tailor XAI techniques to cater to the needs of the insurance sub-domain. In addition to fulfilling the critical desiderata of financial systems, the approaches have to be vigilant about the imbalanced data [42] concerning fraudulent practices, which need to be addressed promptly when encountered.…”

“…Furthermore, Clement et al [8] present a comprehensive survey that positions various XAI methods with respect to software development principles. Researchers interested in applying XAI techniques to these application domains are encouraged to refer to these surveys [6,7,39,41,45], which provide detailed reviews of methods tailored to specific applications.…”