Fair Pricing of Insurance for Women Drivers in Saudi Arabia

Abstract

This thesis examines fair automobile insurance pricing in Saudi Arabia, with a focus on the Bonus-Malus System (BMS) and its adaptation for newly licensed women drivers following the 2017 driving policy reform. The research identifies inefficiencies in the current system and proposes methodological improvements to enhance pricing fairness and adaptability. The first part evaluates Saudi Arabia’s existing no-claim discount system by modelling it as a six-class Bonus-Malus structure. Comparative analysis with different international systems using actuarial tools and a principal component analysis-derived toughness index reveals key weaknesses: slow convergence, financial imbalance, and excessive penalties for new drivers. Real-world data from Allied Cooperative Insurance Group confirms these limitations, particularly for novice women drivers. On the other hand, the second part introduces a goal programming (GP) optimisation approach to design optimal premium scales. Implemented in R and Excel, the GP model balances competing objectives, including fairness, financial equilibrium, and premium stability, while improving actuarial performance measures. This methodology provides a flexible framework for adapting BMS structures to changing market and regulatory conditions. The final part investigates modifications to key BMS components using Saudi market data. Analysis of transition rules based on claim frequency and severity shows that severity-based penalties enhance risk classification, especially when modelling dependencies between claim frequency and severity. The study demonstrates that increasing BMS levels improves risk segmentation but requires balancing practical implementation constraints. In general, this research contributes practical and generalisable methods for modernising BMS design in evolving insurance markets, benefiting insurers, regulators, and policyholders through improved risk assessment and fairer premium structures.