The Development of Forecasting Models for Life Insurance Data by Employing Time-series Analysis and Machine Learning Technique

Abstract

This article is conducted with the primary objective of investigating and comparing various forecasting models, aiming to identify the optimal model for life insurance data. For this investigation, we have employed a comprehensive dataset containing monthly direct premium data from the Thai life insurance sector, spanning from January 2003 to December 2022. Our approach involves the development of time-series models to forecast direct premiums, initially employing the SARIMAX framework. Subsequently, we have introduced an additional time-series forecasting model that incorporates SVR, collectively referred to as the SVR-SARIMAX model. The evaluation criteria used for model comparison encompass the Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and the Coefficient of Determination (R2). The results of our analysis demonstrate that the SARIMAX model outperforms both the SVR and SVR-SARIMAX models, primarily due to the linear pattern in the relationship between the independent and dependent variables. Nevertheless, it is noteworthy that the proposed SVR-SARIMAX model exhibits an improvement in prediction accuracy compared to the standalone non-linear model (SVR), even though the linear model (SARIMAX) still demonstrates superior accuracy.