Policyholder cluster divergence based differential premium in diabetes insurance. (16th April 2021) - Ma, Benjiang; Tang, Qing; Qin, Yifang; Bashir, Muhammad Farhan
Abstract : Traditional health insurance pricing, which is based on experience rates, cannot correctly estimate the risk types of policyholders, can lead to serious adverse selection. Due to massive data volumes and developments in data analysis technology, the underwriting process can more accurately reflect the insured's risk type. Therefore, this paper based on policyholder cluster divergence proposes a differential premium approach by employing fuzzy c‐means algorithm (FCM) with an extended initial multistate Markov model to formulate the differential premium that matches the policyholder's risk category. Our results confirm that the proposed differential premium approach better reveals the policyholder's risk type as compared with unified pricing and effectively counteracts adverse selection.