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

Abstract

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 high- accuracy 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.

Introduction

1. Background & Motivation

• Insurance fraud, especially in motor vehicle claims, is a major global issue, costing billions annually.

• Fraud includes fake accidents, inflated repair costs, and repeated claims, leading to higher premiums and reduced trust.

• Traditional manual, rule-based detection methods are slow, labor-intensive, and ineffective against evolving fraud tactics.

• Machine learning (ML) offers a faster, more accurate solution, able to process complex and unstructured data (text, images, etc.) in real time.

2. Proposed Solution

• A real-time, hybrid fraud detection system combining:

  • Supervised models: XGBoost, LightGBM

  • Unsupervised anomaly detection: Isolation Forest

• Inputs processed via APIs, with feature engineering and scoring done instantly.

• A fraud score determines whether claims are auto-approved or flagged for human review.

• The system achieves <200ms latency and supports horizontal scalability.

3. Key Features of the Architecture

• Multimodal data processing:

  • Structured data (e.g., policy, cost)

  • Unstructured text (e.g., accident descriptions, NLP-processed)

  • Image data (e.g., vehicle damage, CNN embeddings)

• Feature engineering includes:

  • Temporal and geospatial patterns

  • Repair cost deviations

  • Text sentiment and image similarity

• Explainable AI (XAI) using SHAP values ensures transparency and regulatory compliance.

4. Dataset

• Simulated realistic dataset of 50,000 claims (5% fraud).

• Features include:

  • 40+ structured features

  • NLP embeddings (TF-IDF, BERT)

  • Image features (CNN, perceptual hash)

• Addressed class imbalance with:

  • Class weighting

  • SMOTE oversampling

  • Precision–recall threshold tuning

5. Methodology

• Hybrid model combines:

  • Supervised learning (XGBoost, LightGBM, Random Forest, Logistic Regression)

  • Anomaly detection (Isolation Forest, Autoencoders)

• Stacked ensemble with meta-learner optimizes fraud score.

• Trained using time-based splits and evaluated with standard metrics: precision, recall, F1, ROC-AUC, PR-AUC.

6. Experimental Results

• Hybrid model performance (vs others):

• Confusion Matrix for Hybrid Model:

  • True Positives: 1125

  • False Positives: 150 (~0.8%)

  • False Negatives: 125

  • True Negatives: 18,700

• ROC-AUC: 0.98 → Excellent class separation

• PR-AUC: 0.92 → Effective under class imbalance

7. Contribution & Novelty

The proposed system fills key research and operational gaps by:

• Delivering real-time detection with sub-second latency

• Combining supervised and unsupervised methods for better fraud detection

• Using explainable ML (SHAP) for transparency

• Integrating multimodal data (structured, text, image)

Conclusion

This paper presented a real-time, hybrid vehicle insurance fraud detection framework that integrates gradient-boosted decision trees with anomaly detection algorithms to identify suspicious claims before payout. By combining structured, textual, and visual claim data, the system captures complex fraud patterns that traditional rule-based methods often miss. Evaluation on a simulated but realistic dataset of 50,000 claims with a 5% fraud rate demonstrated that the proposed hybrid model achieved an ROC-AUC of 0.98, precision of 88%, and recall of 90%, while maintaining sub-200 ms latency per prediction. These results confirm the system’s ability to operate effectively in high-volume, real-time insurance envi- ronments. The inclusion of SHAP-based explainability also ensures regulatory compliance and enhances investigator trust. The architecture’s modular design allows seamless integra- tion into existing claim management systems, with horizontal scalability for increasing workloads. Its feedback loop en- ables continuous model improvement, ensuring adaptability to evolving fraud strategies.

References

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Copyright

Copyright © 2025 Prof. Ekta Meshram , Dr. Shivajirao M. Jadhav, Ms. Manali Mahendra Jadhav. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.