The insurance industry is undergoing a fundamental transformation driven by artificial intelligence (AI) and telematics technologies, enabling the shift from traditional risk pooling models to usage-based insurance (UBI) and personalized coverage frameworks. This paper examines how AI algorithms, including machine learning (ML) and deep learning (DL), combined with telematics data collection systems, are revolutionizing insurance pricing, risk assessment, and customer engagement. The integration of Internet of Things (IoT) devices, connected vehicles, and wearable sensors provides insurers with granular, real-time behavioral data that enables dynamic premium adjustment and individualized policy customization. Through comprehensive analysis of current implementations and emerging applications, this study explores the technical architecture of AI-enabled UBI systems, examines the transformation of actuarial practices through predictive analytics, and evaluates the implications for stakeholders across the insurance ecosystem. The findings reveal that AI-driven telematics solutions significantly enhance pricing accuracy, reduce adverse selection, improve customer satisfaction through fairness perceptions, and create new opportunities for preventive risk management. However, challenges persist regarding data privacy, algorithmic transparency, regulatory compliance, and equitable access to technology-enabled insurance products. This paper provides insights into how insurers can leverage AI and telematics to create sustainable competitive advantages while addressing ethical considerations and ensuring consumer protection in the evolving landscape of personalized insurance.
Sun et al. (Sun,) studied this question.