Desarrollo e implementación de un modelo estadístico para la predicción de las ventas mensuales de las primas de seguros del estado

Abstract

This study presents the implementation of a statistical model to forecast the monthly sales of insurance premiums at the Antiguo Country branch of Seguros del Estado. Using historical data from 2014 onward, the Box-Jenkins methodology was applied, employing the ARIMA model to identify temporal patterns and dependencies. A rigorous statistical analysis was conducted, including stationarity tests such as the Dickey-Fuller test, as well as autocorrelation and partial autocorrelation functions (ACF and PACF) to determine the model’s optimal parameters. Additionally, diagnostic tests such as Box-Ljung and Jarque-Bera were used to validate the model’s accuracy and reliability. The results confirm the effectiveness of the proposed model in anticipating future demand, enabling the company to optimize resource allocation and enhance strategic decision-making. Given its accuracy and robustness, this model can be replicated in other branches.