AI-Based Tax Compliance Monitoring and Fraud Detection for Taxpaying Citize

Abstract

The increasing complexity of financial transactions and tax evasion strategies has necessitated the development of intelligent, data-driven systems to detect fraudulent activity and ensure compliance. This study introduces a robust hybrid framework that leverages Sparse Autoencoders for feature extraction, Neural Decision Forests for high-accuracy classification, and statistical feature engineering for behavioral analysis. The system is trained on a curated dataset of approximately 10,000 financial transactions, encompassing features such as transaction type, account balances, timing, and recipient risk scores, with derived metrics like transaction velocity, deviation, and balance changes. The architecture is designed to identify anomalous patterns, assess evasion probabilities, and flag high-risk accounts. Advanced machine learning models are employed to address challenges such as class imbalance, dynamic user behavior, and hidden fraud patterns. Compared to traditional rule-based methods, the proposed framework enhances fraud detection accuracy while minimizing false positives, offering a scalable solution for regulatory bodies to audit financial flows and improve transparency in tax collection systems.

Introduction

Tax fraud, involving deliberate financial misrepresentation to evade taxes, causes significant global revenue loss and challenges traditional detection methods like manual audits and rule-based systems, which are resource-heavy and often inflexible. The rise of digital finance and large transactional datasets has made Machine Learning (ML) a powerful alternative, capable of detecting complex fraud patterns. Artificial Neural Networks (ANNs) show high accuracy in tax fraud detection, but their reliance on labeled data limits their effectiveness. Hybrid models combining supervised and unsupervised learning better handle large, diverse datasets.

This study proposes a hybrid framework integrating Sparse Autoencoders (for unsupervised feature extraction), Time Series Forests (for temporal pattern analysis), and Neural Decision Forests (for interpretable classification). This system leverages both transactional and contextual features (like account behavior and risk scores) to improve fraud detection accuracy and scalability, helping tax agencies reduce costs and recover lost revenue.

The literature review highlights advances in ML applications for fraud detection across domains, noting that combining multiple techniques and data mining methods generally enhances detection performance.

The proposed methodology uses Principal Component Analysis (PCA), Autoencoders, and Isolation Forests to preprocess data and detect anomalies efficiently. The system architecture covers comprehensive data collection, rigorous preprocessing, and advanced feature extraction to handle imbalanced datasets and large volumes of tax-related financial data.

The dataset used contains about 10,000 financial transactions with multiple behavioral and monetary features, labeled to indicate fraud presence.

Conclusion

This paper presents a financial fraud detection and tax evasion risk assessment system that utilizes Sparse Autoencoders and Neural Decision Forests for deep feature extraction and interpretable classification. The model achieved an accuracy of 96%, effectively identifying high-risk financial transactions and potential tax evaders based on behavioral features such as transaction type, amount deviation, transaction velocity, and income-tax ratio. The use of Sparse Autoencoders helps in learning compact, meaningful representations from complex transaction data, while Neural Decision Forests enhance classification accuracy through structured decision-making. The system also pinpoints individuals with large balance changes and high tax gaps, indicating possible tax evasion. Future work will focus on expanding the dataset, incorporating real-time analytics, and integrating hybrid AI models for improved performance. Additionally, the deployment of this system in real financial infrastructures can aid in proactive fraud prevention and regulatory compliance.

References

[1] E. W. T. Ngai, Y. Hu, Y. H. Wong, Y. Chen and X. Sun, “The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature,” Decision Support Systems, vol. 50, no. 3, pp. 559–569, Feb. 2011. [2] C. Phua, V. Lee, K. Smith and R. Gayler, “A comprehensive survey of data mining-based fraud detection research,” arXiv preprint arXiv:1009.6119, 2010. [3] V. Murorunkwere, M. S. Elaraby and L. Feng, “A Comparative Study on Tax Fraud Detection Using Supervised Machine Learning Algorithms,” International Journal of Computer Applications, vol. 178, no. 16, pp. 21–27, May 2019. [4] J. Perols, “Financial statement fraud detection: An analysis of statistical and machine learning algorithms,” Auditing: A Journal of Practice & Theory, vol. 30, no. 2, pp. 19–50, May 2011. [5] Manjunath Narayana Mavalangi, “Hybrid Deep Learning Framework for Financial Tax Fraud Detection using Sparse Autoencoder, Time Series Forest, and Neural Decision Forest,” Unpublished MTech Thesis, 2025. [6] A. Alexopoulos, I. T. Christou and G. C. Polyzos, “A network-based approach for VAT fraud detection,” 2018 IEEE International Conference on Big Data (Big Data), Seattle, WA, USA, 2018, pp. 1868–1877. [7] V. Murorunkwere, M. S. Elaraby and L. Feng, “A Comparative Study on Tax Fraud Detection Using Supervised Machine Learning Algorithms,” International Journal of Computer Applications, vol. 178, no. 16, pp. 21–27, May 2019. [8] M. Tax, M. van der Vecht, E. E. V. Vlasselaer, G. G. Jans and W. Verbeke, “Designing a research agenda for fraud detection in e-commerce using machine learning,” IEEE Intelligent Systems, vol. 33, no. 2, pp. 63–71, Mar. 2018. [9] E. W. T. Ngai, Y. Hu, Y. H. Wong, Y. Chen and X. Sun, “The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature,” Decision Support Systems, vol. 50, no. 3, pp. 559–569, Feb. 2011. [10] C. Phua, V. Lee, K. Smith and R. Gayler, “A comprehensive survey of data mining-based fraud detection research,” arXiv preprint arXiv:1009.6119, 2010. [11] E. Kirkos, C. Spathis and Y. Manolopoulos, “Data mining techniques for the detection of fraudulent financial statements,” Expert Systems with Applications, vol. 32, no. 4, pp. 995–1003, May 2007. [12] J. Perols, “Financial statement fraud detection: An analysis of statistical and machine learning algorithms,” Auditing: A Journal of Practice & Theory, vol. 30, no. 2, pp. 19–50, May 2011. [13] K. Fanning and K. Cogger, “Neural network detection of management fraud using published financial data,” International Journal of Intelligent Systems in Accounting, Finance and Management, vol. 7, no. 1, pp. 21–41, 1998. [14] Y. Kou, C.-T. Lu, S. Sirwongwattana and Y.-P. Huang, “Survey of fraud detection techniques,” 2004 IEEE International Conference on Networking, Sensing and Control, Taipei, Taiwan, 2004, pp. 749–754. [15] S. Bhattacharyya, S. Jha, K. Tharakunnel and J. C. Westland, “Data mining for credit card fraud: A comparative study,” Decision Support Systems, vol. 50, no. 3, pp. 602–613, Feb. 2011. [16] R. J. Bolton and D. J. Hand, “Statistical fraud detection: A review,” Statistical Science, vol. 17, no. 3, pp. 235–255, 2002. [17] M. D. Beneish, “The detection of earnings manipulation,” Financial Analysts Journal, vol. 55, no. 5, pp. 24–36, Sep.–Oct. 1999. [18] Y. Chen, X. Han and Y. Zhang, “Financial statement fraud detection: An application of support vector machine,” 2011 International Conference on Management and Service Science, Wuhan, China, 2011, pp. 1–4. [19] C. Lin, Y. Hwang and J. Becker, “A framework for detecting financial statement fraud using data mining and forensic accounting techniques,” International Journal of Digital Accounting Research, vol. 10, pp. 1–27, 2010. [20] Y. Yue, H. Wang and J. Li, “A hybrid model for fraud detection in telecom using clustering and classification,” Procedia Computer Science, vol. 122, pp. 601–607, 2017. [21] D. Sánchez, M. Vila, L. Cerda and J. Serrano, “Association rules applied to credit card fraud detection,” Expert Systems with Applications, vol. 36, no. 2, pp. 3630–3640, Mar. 2009. [22] V. Van Vlasselaer, M. E. Bravo, A. Eliassi-Rad, L. Akoglu, L. Snoeck and B. Baesens, [23] “APATE: A novel approach for automated credit card transaction fraud detection using network-based features,” Decision Support Systems, vol. 75, pp. 38–48, Jun. 2015. [24] M. Jans, N. Lybaert and K. Vanhoof, “Internal fraud risk reduction: Results of a data mining case study,” International Journal of Accounting Information Systems, vol. 11, no. 1, pp. 17–41, Mar. 2010. [25] B. Hoogs, A. Kiehl, A. Lacomb and K. Senturk, “A genetic algorithm approach to detecting temporal patterns indicative of fraud,” Journal of Artificial Intelligence Research, vol. 30, pp. 389–415, 2007. [26] M. Vatsa, R. Singh and A. Noore, “A game-theoretic approach to credit card fraud detection,” Expert Systems with Applications, vol. 38, no. 4, pp. 3728–3735, Apr. 2011.

Copyright

Copyright © 2025 Manjunath Narayana Mavalangi, Madhusudhan M V. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.