A Real-Time Framework for Vehicle Insurance Fraud Detection Using Ensemble Learning and Anomaly Detection

Vehicle insurance fraud is a significant challenge for insurance companies, leading to substantial financial losses and resource wastage. This paper presents a real-time frame- work for detecting fraudulent vehicle insurance claims using a hybrid approach that combines ensemble learning and anomaly detection techniques. The proposed system ingests structured and unstructured claim data, including textual descriptions, numerical attributes, and image evidence, and processes them through feature engineering and preprocessing pipelines. Ensemble models such as XGBoost and LightGBM are employed for highaccuracy classification, while Isolation Forest is integrated for detecting anomalous claim patterns. The framework is designed for deployment in real-time environments, providing immediate fraud probability scores and risk assessments. Experimental evaluation on a publicly available vehicle insurance fraud dataset containing 9,154 claims (14.2% fraudulent) achieved an accuracy of 92.5%, precision of 93.0%, recall of 91.8%, F1-score of 92.4%, and ROC-AUC of 95.1%. These results demonstrate the effectiveness and robustness of the proposed approach in minimizing false positives while maintaining high fraud detection rates.