Decentralized Predictive Maintenance Framework for Dynamic Vehicle Insurance Premiums

This paper introduces a framework for dynamic vehicle insurance premium calculation based on real-time vehicle maintenance data. Traditional vehicle insurance systems predominantly rely on static factors like driving history and vehicle age, overlooking the potential of real-time vehicle health data in risk assessment. The proposed solution combines advanced data analysis of vehicle diagnostic information with secure decentralized technology, offering enhanced data integrity and transparency. The framework enables insurance companies to access verified vehicle health records and adjust premiums dynamically based on current vehicle conditions. Implementation results demonstrate the framework’s effectiveness in providing transparent, data-driven premium calculations while maintaining data security through distributed storage. The proposed system represents a significant advancement in automotive insurance by enabling fair premium adjustments based on actual vehicle condition rather than static historical data, ultimately benefiting both insurers and vehicle owners through more equitable pricing models.