Fraud Detection in Auto Insurance Claims Using Machine Learning Algorithms

Abstract: Insurance fraud is a major problem that threatens both the stability and fairness of insurance systems. This study explores how machine learning techniques—such as Logistic Regression, Decision Trees, Random Forest, and XGBoost—can be applied to identify fraudulent auto insurance claims. The models obtain great accuracy, precision, recall, and F1-score, demonstrating their capacity to distinguish between false and legitimate claims. The performance of the models is further enhanced and improved prediction accuracy is ensured by the use of advanced approaches like feature selection and hyperparameter tuning. Overall, by offering a thorough review of machine learning algorithms and their use in identifying fraudulent claims, this project makes a contribution to the field of auto insurance fraud detection. Insurance businesses can use the created models and procedures to improve their fraud detection processes, reduce financial risks, and safeguard their operations from fraudulent activity Using a real-world dataset from Kaggle, we applied preprocessing techniques, feature selection via Recursive Feature Elimination, and data balancing through SMOTE. Out of all the models tested, XGBoost showed the highest performance, achieving an accuracy of 89% and an F1-score of 87%. The paper highlights the effectiveness of AI-driven detection systems in minimizing financial loss, improving risk management, and ensuring fairness in insurance systems.

Insurance fraud, Machine Learning, XGBoost, Auto claims, SMOTE

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