Policyholder cluster divergence based differential premium in diabetes insurance

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.

An outlook on the development of renewable energy, policy measures to reshape the current energy mix, and how to achieve sustainable economic growth in the post COVID-19 era

“…In recent months, the global crude oil prices for WTI fell by a record 22.30 USD/BBL (WTI), which accounts for 67% reduction; and global crude oil for Brent fell by 22.36 USD/BBL, which is a 65% decline compared to the reference year 2019. The most significant shock came from a drastic drop in transportation, residential, industrial, and commercial usage (Ma et al 2020a , b ). This has contributed to additional pressure on balancing demand and supply to regulate oil prices more efficiently.…”

Oil price shocks, stock market returns, and volatility spillovers: a bibliometric analysis and its implications

“…Huang et al ( 2005 ) researched Japan, Canada, and the USA to examine the impact of oil price volatility and oil price changes over real stock returns and industrial production levels to report nonlinear threshold effects; change in oil volatility or price has a significant impact on macroeconomic variables but only if the change is above a certain threshold. Asteriou and Bashmakova ( 2013 ) used a multi-factor modeling approach in Central and Eastern European countries to indicate that stock returns have a statistically significant and negative association with oil price shocks (Ma et al 2021b , c ). Asteriou et al ( 2013 ), by taking into account that whether countries are oil exporters or oil importers, analyzed the association between stock markets and sudden shifts in oil prices to summarize that changes in oil prices have significantly higher impact on stock markets in oil-importing countries than oil-exporting economies.…”