The life insurance industry plays a strategic role in the national financial system, not only as a provider of protection against life risks such as premature death or critical illness, but also as an instrument of long-term fund accumulation. Increased public awareness of the importance of risk protection has driven significant growth in the number of active policies. This condition has a direct impact on the risk exposure of claims that must be carefully managed by insurance companies. One of the main challenges in risk management is to accurately estimate the number of claims in a certain period, to support premium setting, technical reserve planning, and maintain the company's financial stability. This study aims to examine the use of Poisson regression model in estimating the frequency of life insurance claims based on the number of active policies in life insurance company. The data used is simulative and represents an exponential relationship between the number of policies and claims. The model is analyzed using the Maximum Likelihood Estimation (MLE) approach and evaluated through goodness-of-fit indicators such as deviance, Pearson chi-square, log-likelihood, and Mean Squared Error (MSE). The results of the analysis show that the Poisson regression model can capture the significant relationship pattern between the number of active policies and claims, and provide accurate prediction results. Thus, Poisson regression is proven to be a relevant and applicable statistical method in supporting strategic decision-making in insurance companies, especially in the context of data-driven risk management.
