Paper Title
FRAUD DETECTION IN INSURANCE USING MACHINE LEARNING

Abstract
Fraudulent claims in the insurance industry result in tremendous financial losses and have detrimental impacts on policyholders and insurance firms. Machine learning offers the possibility of changing fraud detection through automation, enhancement, and optimization from rule-based systems. The novel hybrid approach makes use of supervised and unsupervised machine learning techniques to detect insurance fraud with high accuracy and robustness. Three primary models are proposed for the system: Decision Tree, Random Forest, and Voting Classifier, which will be used in the system to enhance the performance of detection on a real-world dataset. In addition, we present an embedding-based model to interpret sequential claims data and apply a statistically validated network to find patterns of collusive fraud involving related entities. We extensively experiment on this system with a large scale health, motor, as well as general insurance dataset. Our proposed hybrid method was evaluated against metrics in the form of accuracy, precision, recall, and finally, F1-score - overall achieving 98.2% accuracy. Hyperparameter tuning and data preprocessing techniques further optimize the performance of the model, addressing the key challenges of data imbalance, complexity, and variance in fraud types. The proposed methodology outperforms conventional models, especially in rare or sophisticated fraud cases. We discuss the implications of integrating ML-driven models in the insurance sector and outline best practices for deployment, data governance, and model interpretability to foster stakeholder trust. Future work includes extending the model to include real-time analytics for faster fraud detection, deepening interpretability features, and extending the framework to adapt dynamically to emerging fraud patterns in evolving data landscapes. Keywords - Insurance Fraud Detection, Machine Learning, Supervised Learning, Unsupervised Learning, Hybrid Models, Sequence Embeddings, Network Analysis, Anomaly Detection, Healthcare Fraud, Auto Insurance, Gradient Boosting, Data Imbalance, Interpretability, Privacy-Preserving ML, Real-Time Processing