Predictive Analytics in Automotive Insurance for Financial Risk Mitigation
Abstract
The paper investigates how predictive analytics affects automotive insurance when combined with Driving Behavior Scoring which uses XGBoost machine learning techniques. Through the analysis of telematics data features including acceleration rates and braking patterns as well as speed pattern changes and driving frequency the model develops dynamic risk scores. The model uses XGBoost algorithm to divide drivers into low-risk versus high-risk categories through predictive behavioral assessment. A real dataset consisting of more than 100,000 driving records was used to train and validate the model which reached a 89% accuracy level. The scoring system allows insurance providers to design individual premium costs while also helping them prevent financial losses and conduct more exact underwriting procedures. The methodology increases claim predictions while creating behavioral feedback systems to promote driving safety. XGBoost integration with behavior-based analytics proves successful for improving risk management together with financial performance in automotive insurance operations.