Investigating the Pricing on Individual Health Insurance Schemes Based on National Health Insurance Fund (NHIF) Using Credibility Theory

Abstract

This study used credibility theory to investigate the pricing of individual health insurance schemes based on the National Health Insurance Fund (NHIF). The specific objectives were to analyze NHIF data using credibility theory to understand better the risk factors associated with providing health insurance coverage, estimate and price health insurance schemes using B¨uhlmann credibility and B¨uhlmann-Straub credibility models, and investigate the Bayesian credibility approach to determine the price of premiums payable by NHIF scheme holders. The simulated data for four counties under Universal Health Care of Region1, Region2, Region3, and Region4 were used to determine these models’ impact on the premiums payable by the policyholders under the Covers. The data were analyzed using Excel, where the Buhlmann credibility and Buhlmann-Straub analysis were performed. The study found that all four counties analyzed experienced an increase in aggregate claim amounts over five years, with Region2 and Region3 having significantly higher total claim amounts than Region4 and Region1. The premiums calculated through the process have shown reduced rates, thus enabling people to purchase these products to help them, especially whenever they need medical help. The Buhlmann-Straub method was used to calculate the final premium for each county, taking into historical account data and actual claim experience to determine more accurate and reflective premiums. The results will help the Ministry of Health formulate policies on improving the National Health Insurance Fund (NHIF) benefits, thus enabling many people to get covered. Ultimately, the research proposes using the Bayesian Credibility approach. The prior information about the policyholder is essential in determining the price of premiums they will pay whenever they acquire these National Health Insurance Fund (NHIF) schemes available in the Kenyan market for sale. The policyholders can use the research findings to enhance the policies regarding pricing sold to the Kenyans living in these areas. Recommendations include improving the availability and quality of data, exploring alternative statistical models and methods for computing credibility premiums, and addressing outliers in data to improve the accuracy of premium calculations.