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

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The framework achieved 92.5% accuracy in detecting vehicle insurance fraud, minimizing false positives effectively.
Key performance metrics included a precision of 93.0% and a recall of 91.8% on 9,154 claims, indicating high reliability.
The model integrates ensemble learning techniques like XGBoost and LightGBM to classify claims and utilize Isolation Forest for anomaly detection.
This real-time system offers instant fraud probability scores, marking a significant advancement in fraud detection technologies.

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Framework detects vehicle insurance fraud in real-time, combining ensemble learning and anomaly detection techniques.

Prof. Ekta Meshram (2025) studied this question.

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