Motor Insurance Policy Selection: A Joint Spherical Fuzzy Analytic Hierarchy Process (SF-AHP) and Combined Compromise Solution (CoCoSo) Approach

MOTOR INSURANCE POLICY SELECTION WITH SF-AHP & COCOSO

Abstract

Motor insurance policies play a crucial role in protecting vehicle owners against financial losses due to accidents, theft, or other unforeseen events. The selection of an appropriate motor insurance policy is a complex decision-making process that requires considering multiple criteria and their interrelationships. The motivation behind this study is to offer an advanced decision-making framework that addresses the complexities of motor insurance policy selection, improves risk management, fosters innovation in decision-making methodologies, enhances customer satisfaction, and increases the competitiveness of insurance providers. This research presents a joint approach, combining the SF-AHP (Spherical Fuzzy Analytic Hierarchy Process) and the CoCoSo (Combined Compromise Solution) method, to facilitate the selection of the most suitable motor insurance policy. The weights of the factors are estimated by SF-AHP method with experts’ advice. The rankings of the alternatives are calculated using CoCoSo method. The sensitivity analysis is also carried out to check the stability of results over different Eigen values (l). Premium amount is identified as the most influencing factor with factors weight as 0.178 and reputation of the insurance company is identified as least dominating out of other selected factors with factor weight as 0.10. The results are significantly stable over different l values ranging from zero to one. The research paper addresses a novel problem of motor insurance policy selection that has not been explored by any previous researchers in the existing literature.