Advanced Tax Fraud Detection: A Soft-Voting Ensemble Based on GAN and Encoder Architecture

The world prevalence of the two types of authorized and fraudulent transactions makes it difficult to distinguish between the two operations. The small percentage of fraudulent transactions, in turn, gives rise to the class imbalance problem. Hence, an adequately robust fraud detection mechanism must exist for tax systems to avoid their collapse. It has become significantly difficult to obtain any dataset, specifically a tax return dataset, because of the rising importance of privacy in a society where people generally feel squeamish about sharing personal information. Because of this, we arrive at the decision to synthesize our dataset by employing publicly available data, as well as enhance them through Correlational Generative Adversarial Networks (CGANs) and the Synthetic Minority Oversampling Technique (SMOTE). The proposed method includes a preprocessing stage to denoise the data and identify anomalies, outliers, and dimensionality reduction. Then the data have undergone enhancement using the SMOTE and the proposed CGAN techniques. A unique encoder design has been proposed, which serves the purpose of exposing the hidden patterns among legitimate and fraudulent records. This research found anomalous deductions, income inconsistencies, recurrent transaction manipulations, and irregular filing practices that distinguish fraudulent from valid tax records. These patterns are identified by encoder-based feature extraction and synthetic data augmentation. Several machine learning classifiers, along with a voting ensemble technique, have been used both with and without data augmentation. Experimental results have shown that the proposed Soft-Voting technique outperformed the original without an ensemble method.