Artificial Intelligence Technologies in Predicting Life Insurance Premiums: A Case Study on the Iraqi General Insurance Company

Abstract

The insurance sector relies on considerable amounts of data and statistics where standard actuarial computations form the basis for determining risks, liabilities, and appropriate premiums. The traditional methods make predicting life insurance premiums complex task and less efficient. This affects the overall cost and development of proper insurance solutions. However, the rapid development of artificial intelligence technologies presents great opportunities to reshape traditional processes to improve efficiency and accuracy within the insurance industry. The presented work focuses on applying artificial intelligence in developing actuarial calculations, aiming to find a better estimate of the life insurance premium and providing better quality reports for the Iraqi General Insurance Company. In the current study, we used three machine learning algorithms, namely XGBoost, Random Forest (RF), and Decision Tree (DT), to analyze the collected data and estimate the life insurance premium. Different regression evaluation metrics, including MAE, RMSE, R², and Adjusted R², were applied in testing the performance of the algorithms. The results from the experiments showed that the XGBoost algorithm was the highest performing compared to other algorithms that recorded the lowest MAE and the highest R² score. The research also suggested that incorporating artificial intelligence techniques into actuarial analysis can enhance financial reporting efficiency, thus increasing insurance companies' competitiveness in the important sector. Consequently, Iraqi insurance companies need to adopt such technologies to face the changing insurance conditions by training employees and adopting digital transformation to stay competitive and benefit from innovation.