Mitigating Ethical Risks of Using Artificial Intelligence in the Insurance Industry

Abstract

The insurance industry, like other financial services sectors, has been disrupted by artificial intelligence (AI), transforming decision-making processes such as underwriting, pricing, and claim management. While AI can improve productivity, accuracy, and efficiency, it can also raise ethical concerns such as bias, discrimination, data quality, and data privacy concerns. These ethical risks can potentially have a negative impact on fairness, transparency, and trust in AI-driven decision-making. This study explored the ethical implications of AI-driven decision-making in the insurance industry through the ethical lens of utilitarianism (consequentialist) and deontology (rule-based). A qualitative research approach was employed within an interpretivist paradigm, with data collected through semi-structured interviews. A purposive and snowball sampling strategy was used to identify professionals within the insurance industry with knowledge and/or experience in AI. Thematic analysis, guided by Braun and Clarke’s six-phase framework, was conducted to identify recurring patterns and insights within the data. The study’s findings highlight the ethical risks associated with the integration of AI in insurance processes. Current strategies were found to be somewhat effective however there remains a need for continuous improvement and adaptability to advances in AI. It also emerged that balancing innovation with ethical considerations was important to ensure that AI-driven insurance systems achieve optimal efficiency while maintaining ethical integrity and consumer trust. A conceptual framework that incorporates utilitarian and deontological ethical principles, was therefore proposed as a guide to balancing accuracy, efficiency, continuous improvement and maximising overall good while ensuring adherence to moral rules, transparency and explainability, responsibility and accountability. The proposed conceptual framework serves as a guide for insurance industry stakeholders to infuse both utilitarian and deontological ethical principles into AI-driven decision- making.