Explainable AI for Insurance: Enhancing Transparency, Accountability, and Client Trust
Wasswa Shafik · 2026
The insurance industry is deeply rooted in data-driven research, encompassing specialized knowledge in underwriting and dealing with the severe consequences of data errors. Given the abundance of data at hand, the implementation of machine learning techniques, especially supervised learning methods, naturally gained attention from risk modelers and in academia. In the last decade, a considerable amount of literature has arisen engaging in innovative methods, adaptive data structures, and plug-in software, allowing the application and implementation of different machine learning techniques in an insurance context. The accomplishment of supervised learning tasks in insurance relies on the estimation of predictive risk models, specifically done by risk professionals to develop risk premium models for pricing and reserving. Despite the pay-off improvements in terms of predictive performance achievable with machine learning methods, the insurance industry has been slower than other data-driven industries in adopting these advanced methods. This relatively slow transition towards machine learning might be attributable to the model risk and risk accountability of published machine learning models. That is, the predictive machine learning model is neither good nor does not exist; rather, it is interpreted given a certain part of the joint distribution of the underlying variables that makes the model useful for prediction at a certain time or over a certain period of time. But the adoption of machine learning comes with its own set of challenges and caveats. The most highlighted amongst them is probably the black-box argument: How can one hold gardens of millions of data points accountable when predicting over such models? For this reason, transparency and explanation of the prediction in the context of insurance are necessary to gain the trust of the public in order to safeguard the accountability of the prediction. The predicted outcome must be coherent with our understanding of the risk domain.