Legislative and Regulatory Frameworks: Regulatory Issue of Black Box Algorithms in Motor Insurance Pricing

Abstract

This study investigates the regulatory challenges posed by the integration of AI-driven black box algorithms in motor insurance pricing. The research employs archival techniques to select scholarly articles and thesis records from the period 2017 to 2025, focusing on topics such as unfair discrimination, transparency, fairness, and regulatory compliance requirements. The methodology encompasses a comprehensive literature review to identify existing regulatory gaps and challenges, and case studies to examine real-world applications and implications of black box algorithms. The study provides an in-depth examination of the current regulatory landscape, identifying specific gaps and challenges in overseeing AI-driven decision-making in motor insurance. The results highlight the inadequacies of current regulations in ensuring transparency, fairness, and accountability, and underscore the necessity for regulatory refinement. A comparative regulatory analysis across various jurisdictions reveals the effectiveness of different frameworks, while ethical considerations are integrated to address the broader implications of AI deployment in insurance. The study concludes that there is an urgent need for robust regulatory frameworks that mandate the use of explainable AI techniques to enhance model interpretability and mitigate biases. Recommendations include fostering public debate and interdisciplinary research, updating actuarial education to include AI and data science skills, and refining regulatory standards to protect consumer rights and promote ethical AI deployment. These measures are essential to ensure that AI-driven motor insurance pricing operates in an equitable and transparent manner, fostering trust and fairness in insurance practices.

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