Hybrid Deep Learning and Behavioral Analytics for Predictive Tax Fraud Detection in Cloud-Enabled Government Systems

Abstract

Accurate prediction of tax fraud fosters favorable taxpayer-business relationships, streamlines tax authorities' operations, and optimally directs government investments to enhance public services. Reliable prediction of fraudulent taxpayers, however, requires careful selection of assessed features because of pronounced privacy, ethical, and security concerns associated with government-related data. Furthermore, limited historical records are available because fraud often remains undetected. Consequently, effective machine learning-based predictive models must deploy hybrid architectures capable of learning from different types of features, integrating feature engineering with representation learning, and extracting fraud behavioral patterns during training. Moreover, tax fraud detection is a pattern-discovery problem with critical imbalance between the positive and negative classes, necessitating the use of behaviorally informative indicators that optimize predictive performance, fairness, and robustness. Predictive models should therefore integrate hybrid deep learning representations with behavioral-based tax fraud indicators for risk detection. Cloud-enabled government data ecosystems provide abundant data sources for detecting and predicting all forms of tax fraud, and during operation can be simulated to include as many behaviors that are indicators of tax fraud as possible. Predictive models can thus be trained and tested using a behavioral pattern-discovery approach, forming the foundation for hybrid deep learning-based models that use a dual-tower architecture to employ both behavioral indicators and independently engineered features to optimize detection risk.