Mitigating Class Imbalance with Ensemble SMOTEfied-GAN: Advancing Detection Strategies for Automobile Insurance Fraud
Prisha Patel, Sakshi Chauhan, Shaurya Gupta, Tawishi Gupta, Renuka Agrawal · International Journal of Fuzzy Logic and Intelligent Systems · 2024
The automobile insurance industry faces significant challenges in detecting fraudulent activities because of the imbalanced nature of fraud data, which traditional machine learning algorithms struggle to address effectively.In this study, to improve the efficiency of fraud detection, we investigated three approaches: the Synthetic Minority Oversampling TEchnique (SMOTE), generative adversarial networks (GANs), and a hybrid approach combining SMOTE with GANs (SMOTEfied-GAN).SMOTE addresses the class imbalance by oversampling the minority class, whereas GANs generate synthetic data that resemble the training data distribution.The SMOTEfied-GAN combines the strengths of both methods by oversampling the minority class using SMOTE before training the GAN to enhance the quality of the synthetic samples.A comparative analysis was conducted on these approaches using a dataset from the automobile insurance industry.Our evaluation included metrics, such as precision, recall and F1-score.These findings suggest that each approach offers unique advantages in improving fraud-detection efficiency.