An Ensemble-Based Auto Insurance Fraud Detection Using BQANA Hyperparameter Tuning

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Abstract
The prevalence of insurance fraud in the auto industry poses significant financial challenges and undermines customer trust. Despite the application of machine learning methods to reduce these losses, current literature lacks effective tuned algorithms for detecting fraud in insurance claims. To address this gap, this study proposes an ensemble-based method with a weighted voting strategy for auto insurance fraud detection. The study uses the Binary Quantum-Based Avian Navigation Optimizer Algorithm (BQANA) to optimize the hyperparameters of Support Vector Machines (SVM), Random Forest (RF), and XGBoost classifiers, which are combined into an ensemble. To address the dataset’s imbalance, random undersampling was applied to create five legitimate-to-fraudulent claim ratios: A:A, 1:1, 2:1, 4:1, and 8:1. The performance of BQANA was compared with Genetic Algorithms and Simulated Annealing for hyperparameter tuning. The results indicate that the ensemble model with BQANA-optimized hyperparameters outperforms other methods, particularly at a 1:1 ratio, achieving 99.94% Accuracy, 98.93% Precision, 100% Recall, and a 99.46% F1-score. These metrics surpass those obtained without optimization or with traditional tuning methods. This research highlights the efficacy of the BQANA algorithm in optimizing hyperparameters for classification models. By combining these optimized classifiers into an ensemble, the study significantly enhances predictive accuracy in car insurance fraud detection, offering notable improvements over conventional methods.

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