Abstract
Health insurance premiums are on the rise due to increasing medical costs, inflation, and the lingering effects of the COVID-19 pandemic. Accurate premium pricing is crucial for insurance companies to maintain financial stability and offer fair rates to policyholders. Generalized Linear Models (GLM) have been widely used in actuarial science for modeling insurance premiums. This study proposes the use of GLM with a Gamma distribution to model health insurance premiums. The Gamma distribution is suitable for non-negative and positively skewed data, which is characteristic of insurance claim amounts. By analyzing historical data from a Southeast United State insurance company, we aim to identify key factors influencing premium pricing and develop a robust premium model. The model will consider factors such as age, gender, BMI, number of children, and smoking status to predict individual risk profiles and determine appropriate premiums. Our findings indicate that age and smoking status are the most significant factors affecting premium rates. Older individuals and smokers tend to have higher premiums due to their increased risk of health issues. Gender and BMI, however, were found to have no significant impact on premium pricing in this specific dataset. Insurance companies can use the identified factors (age, smoking status, etc.) to create more precise risk profiles for their policyholders.
Published Version
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