Towards Smart Fraud Detection: Integrating Machine Learning and Anomaly Detection in Vehicle Insurance Claims

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The hybrid model significantly improves fraud detection accuracy in vehicle insurance claims, enhancing operational efficiency.
Using techniques like anomaly detection and machine learning, the framework achieves better decision-making transparency and reliability.
A robust preprocessing pipeline handles data cleaning and feature selection, helping retain the most predictive features for model training.
The system incorporates advanced models like Random Forest and XGBoost, ensuring effective identification and filtering of fraudulent claims.

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This research demonstrates improved fraud detection accuracy in vehicle insurance claims, integrating machine learning and anomaly detection methods.

Jashwanth et al. (2025) studied this question.

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This research demonstrates improved fraud detection accuracy in vehicle insurance claims, integrating machine learning and anomaly detection methods.