aceInsurance claim level estimation and influencing factor analysis for electric vehicles: A case study in Changsha city
Objectives Claim amount levels reflect severity risk and facilitates risk estimation in insurance practice. This study supports risk management in electric-vehicle traffic insurance by establishing an effective classification framework for claim levels. Methods The Synthetic Minority Over-Sampling Technique (SMOTE) is applied to balance the distribution of claim amount levels in traffic insurance. This study applies six distinct models-Decision Tree, Random Forest, CatBoost, Support Vector Machine (SVM), Multila…
Machine learning classification improves risk differentiation for electric-vehicle traffic insurance by predicting claim amount levels from policy and behavioral features.
Evidence
- Peer-reviewedTraffic Injury Prevention2026-09-15
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Truvace Impact Record TRV-2026-1106, v1: “Insurance claim level estimation and influencing factor analysis for electric vehicles: A case study in Changsha city.” Truvace, 2026-09-16. /record/TRV-2026-1106 (accessed at citation time). sha256 5e16458731f0e5b8…
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