An Adaptive telematics framework for usage-based insurance in Kenya

Abstract

This study develops a telematics-based risk classification model for usage-based insurance (UBI) in Kenya’s fleet management sector, addressing critical limitations of traditional models through adaptive machine learning techniques. The research employs ADASYN (Adaptive Synthetic Sampling) to resolve class imbalance. This approach enables more effective learning of risky driving patterns compared to conventional sampling methods. The study processes telemetry data—including acceleration, deceleration, RPM, and speed—from Easy Coach Limited vehicles collected between January 2022 and February 2024. After minority class augmentation via ADASYN, several machine learning models were evaluated, including logistic regression, random forests, and gradient boosting. Among these, XGBoost demonstrated optimal performance, achieving a classification accuracy of 99% and an area under the ROC curve (AUC) of 0.98. Notably, the model achieved a precision of 93% and recall of 97% for the minority (risky) class. ADASYN yielded a 27% improvement in the minority class F1-score compared to SMOTE, highlighting its effectiveness in balancing highly skewed datasets. The model was deployed on an Amazon EC2 instance within a secured Virtual Private Cloud (VPC). The instance hosts a Gradio-based interface, which generates a URL endpoint that can be embedded in external web applications. This allows seamless, real-time interaction with the model. Operational monitoring was implemented using Amazon CloudWatch for system health tracking and failure alerts. Model explainability was supported through SHAP (Shapley Additive Explanations), providing transparency into feature importance. Key applications of the model include precision-targeted premium adjustments based on risk scores, temporal risk analysis and operational optimization based on RPM and braking behaviors. This work contributes empirical evidence supporting a deployable framework for insurance technology in Kenya. Furthermore, it proposes policy guidelines for the insurance industry to support UBI adoption. Future research directions include applying federated learning to enhance model generalizability across East African fleets while addressing data privacy concerns.