Machine Learning on Insurance Premium Prediction

Abstract

The insurance field is going through a phase of great transformation due to the growth of new technologies and techniques that are causing a change in the way data is handled and analyzed. The main perpetrator of this phenomenon is the introduction of Machine Learning (ML) in financial decision-making due to their efficiency and productivity. However, there is a new intervenient in the room, which will automate and support all steps of ML system development. Machine Learning Operations (MLOPs) will reduce technical friction, so that the model may move from an idea into production, in the shortest amount of time, and subsequently to market with the least possible risk. In this paper, a detailed review of the impacts of ML on insurance premium forecasting and the influence that MLOPs can have on forecasting outcomes is provided. Furthermore, a comprehensive summary is presented of crucial principles in the insurance industry, which are essential for comprehending the role that MLOPs will play in tailoring and individualizing insurance policies and premiums.