A novel decision-making agent-based multi-objective automobile insurance pricing algorithm with insurers and customers satisfaction

Abstract

Automobile insurance is essential for customers to obtain an appropriate automobile insurance policy and plays a significant role in the insurance industry. However, pricing a reasonable automobile insurance policy for different customers requires considering several indicators, such as the number of historical accidents, the frequency of long-distance driving, and the mileage driven per month. Therefore, how to make a reasonable automobile insurance pricing policy according to the needs of different customers is an urgent issue. Existing pricing methods often face the challenges of inefficiencies and mispricing, making it difficult to satisfy both insurers and customers. To better address this problem, this paper proposes a decision-making agent-based multi-objective automobile insurance pricing (DMA-MoAIP) algorithm. The DMA-MoAIP algorithm provides a diverse set of pricing strategies that meet various requirements, and considers the satisfaction of both insurers and customers. Firstly, a decision-making agent (DMA) framework is proposed for insurers, which provides an accurate assessment of the risk level for each customer. Secondly, a data-driven scoring (DDS) mechanism is established to better measure the satisfaction of insurers and customers and form a score that corresponds to their satisfaction. Thirdly, a novel multi-objective particle swarm optimization (MoPSO) algorithm is designed to search for effective and diverse solutions in dealing with automobile insurance pricing problem. Experimental results show that DMA-MoAIP outperforms five state-of-the-art multi-objective algorithms (including NSGA-II and so on) in terms of convergence and solution diversity. Specifically, the solution diversity of DMA- MoAIP in automotive pricing has increased by an average of 17%, which can provide more pricing options to meet different customer needs. In practical applications, DMA-MoAIP provides three distinct pricing strategies: insurer-oriented, customer-oriented, and compromise pricing. These strategies underscore the importance of considering relevant driving behavior metrics, rendering them valuable for real-life applications.