The paper aimed to demonstrate higher profitability from the use of loss modeling on the estimation of health insurance product pricing utilized by Health Maintenance Organizations using the Frequency-Severity Method. This is opposed to the traditional Loss-Cost approach, which only estimates claims severity. Our results show that by classifying data according to their similar characteristics, the risk of wrongly specifying a best-fit probability distribution is minimized. Percentiles can also be determined through Maximum Likelihood Estimation, thus avoiding the use of the central limit theorem that assumes normality in the data even when they lead to wrong model specifications and erroneous results. The modeled premiums were approximately 20-percent higher than the empirical premiums. It showed a 50-percent increase in underwriting profit at the onset. The 20-percent gap was also addressed through the adjustment of the frequency component, which proved the versatility of the pricing model
