A Comprehensive Analysis on Smart Tax Management Systems Using Data Mining Algorithms and Applications

Nowadays, the rapid digitalization of financial transactions and tax administration system has resulted in huge volumes of heterogeneous tax data, facing issues for effective tax management. Moreover, conventional tax systems depend on rule based manual analysis and audits, which are inefficient in detecting complex fraud patterns ensuring consistent taxpayer compliance. Whereas the existing systems uses limited analytical methods like basic statistical models and isolated ML techniques which suffered from problems based on scalability, lack of interpretability, data imbalance and adaptability to evolving tax regulations. However, these drawbacks motivate the adoption of smart tax management systems presented by advanced data mining algorithms. In modern days, methods like decision trees, support vector regression, ensemble learning, deep neural networks, anomaly detection, clustering and explainable Artificial Intelligence (AI) are applied to overcome compliance prediction, tax fraud detection and revenue forecasting. Additionally, this survey provides a review of advance smart tax management methods using data mining algorithms and introduces a structured taxonomy that categorized techniques into supervised and semi-supervised, unsupervised and hybrid intelligent techniques. Hence, the comparative analysis of the existing research emphasizes its methodological strengths and limitations by relieving research gaps based to interpretability, real world deployment and computational efficiency. therefore, this paper aims to guide future research across developing scalable, transparent and robust intelligent tax management solutions.