2025/02/12 by Arthur Charpentier, Xavier Vamparys · 1 voice
Economics, Econometrics and Finance · #Insurance and Financial Risk Management
paper · doi:10.1177/20539517241291817
openalex publication_date 2025/02/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/02
In insurance, there is still a significant gap between the anticipated disruption, due to big data and machine learning algorithms, and the actual implementation of behaviour-based personalization, as described by Meyers (2018). Here, we identify eight key factors that serve as fundamental obstacles to the radical transformation of insurance guarantees, aiming to closely align them with the risk profile of each policyholder. These obstacles include the collective nature of insurance, the entrenched beliefs of some insurance companies, challenges related to data collection and use for personalized pricing, limited interest from insurers in adopting new models as well as policyholders’ reluctance towards embracing connected devices. Additionally, the hurdles of explainability, insurer inertia and ethical or societal considerations further complicate the path toward achieving highly individualized insurance pricing.