Gamma Regression Model for Insurance Claims Analysis: A Comparative Study on Link Functions and Their Influence on Cost Predictions

The Gamma Regression Model is useful when the dependent variable is continuous and positive, and the data follows a Gamma distribution. It is a Generalized Linear Model of the type GLM, which is useful for fitting models when assumptions of normal error distribution are not reasonable. Unlike classical or normal regression models, which assume a normal distribution of errors, the Gamma Regression Model fits distributions that are more appropriate for skewed data and can be used in fields such as insurance, economics, and environmental studies, for example. The analysis of claim costs in this study illustrates how the Gamma Regression Model can be applied through SPSS software in the context of insurance claims data. The work highlights the need to choose an appropriate link function in order to achieve an optimal fit to a model, contrasting the logarithmic and the inverse link functions. The outcome indicates that the models with the log link function have higher compatibilities since the deviance statistic is smaller and the Pearson Chi-Square is smaller. The independent variables of vehicle age and vehicle type are found to have a significant effect on the cost of claims. These variables’ predictive power is important to help insurance companies when calculating premiums and estimating risks. To wrap up, the Gamma Regression Model is an efficient and effective model that can be used to influence decision-making wherever there are values that are continuous and skewed.