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The insurance industry represents a fundamental opportunity to demonstrate the potential of artificial intelligence (AI), with its vast stores of quotation, policy, claims and alternative data sources. However, the prerequisite for auditable systems which reduce bias and discrimination in the provision of insurance products has slowed widespread integration of AI. This research examines Explainable Artificial Intelligence (XAI) methods within the insurance industry to analyse the industry’s position in counteracting ‘black-box’ AI models. This research is the first to describe XAI’s current prominence and role within the  insurance industry, while simultaneously contributing to the interdisciplinary understanding of applied XAI.

In line with the call for greater transparency and understandability within industry practices and research, the thesis will also analyse the FAIR (Findable, Accessible, Interoperable, Reusable) Data Principles’ transposability to the insurance industry.