A Systematic Literature Review of Forecasting Models for Life Insurance Sales: From ARIMA to Deep Learning

Abstract

Traditionally, the techniques used in forecasting life insurance sales have been based on traditional statistical models, including ARIMA and VAR, but now they have moved to data-driven artificial intelligence methods. This paper is based on a systematic and bibliometric review aimed at tracking this methodological shift, combining results from literature spanning 2000 to 2025 indexed in Scopus. The review combines quantitative bibliometric mapping with VOSviewer and qualitative synthesis based on the PRISMA approach. Findings indicate that the trend is inclined toward machine learning, deep learning, and hybrid ensemble models, which are more accurate in prediction, flexible, and have risk assessment potential. Major contributors, thematic groups, and relation networks were determined with a primary focus on hybrid AI structures and explainable modeling techniques. The paper ends by providing a roadmap that highlights domain-specific data integration, multimodal AI systems, and ethical explainability as the key to the next generation of forecasting in the insurance sector.