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International Journal of Marketing & Financial Management

AI-POWERED TAX FRAUD DETECTION SYSTEM FOR INCOME TAX FILINGS

Dr R.Narmadha

Dr R.Narmadha, Associate Professor&Head of the Department, GFGC, HSR layout, Bangalore, Karnataka, India

DOI : https://doi.org/10.5281/zenodo.15344915 Page No : 40-48

Published Online : 2025-05-05

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The detection system uses AI and the Isolation Forest method implemented through Apache Spark to examine extensive financial data in order to detect tax fraud. The proposed detection system studies unusual tax filing behaviors because criminal tax payers behave differently from usual financial patterns. Isolation Forest acts as an unsupervised machine learning technique because it serves as an outlier detection method to identify questionable tax records while needing no dependent labeled data. The implementation of Apache Spark brings the ability to handle enormous financial transaction data alongside the provision of efficient scalable operations across numerous sources. This synthetic tax data methodology performed successful detection of suspicious cases without creating many wrong identifications. Small tax conduct nuances serve as the basis for the system to provide active oversight capabilities to tax authorities. Department of Revenue uses digital governance to achieve better compliance monitoring through data intelligence as well as enhanced system tracking capabilities.

Keywords: Tax Fraud Detection, Isolation Forest, Apache Spark, Anomaly Detection, Income Tax Filings, Machine Learning, Big Data Analytics

REFERENCES

L. Liu, T. Chen, and Z. Ma, "Isolation Forest for Anomaly Detection," *2019 IEEE Intelligent Transportation Systems Conference (ITSC)*, Auckland, New Zealand, 2019, pp. 2482–2487, doi: 10.1109/ITSC.2019.8917011.
A. Zaharia, M. Chowdhury, M. J. Franklin, S. Shenker and I. Stoica, "Spark: Cluster Computing with Working Sets," *2nd USENIX Conference on Hot Topics in Cloud Computing (HotCloud’10)*, Boston, MA, 2010.
R. Phua, V. Lee, K. Smith, and R. Gayler, "A Comprehensive Survey of Data Mining-based Fraud Detection Research," *arXiv preprint arXiv:1009.6119*, 2010.
L. Breiman, "Random Forests," *Machine Learning*, vol. 45, no. 1, pp. 5–32, 2001, doi: 10.1023/A:1010933404324.
A. Almalki and K. Aziz, "Hybrid Fraud Detection Framework Using Machine Learning and Rule-Based Techniques in Tax Data," *IEEE Access*, vol. 9, pp. 129861–129874, 2021, doi: 10.1109/ACCESS.2021.3113478.
F. T. Liu, K. M. Ting and Z. Zhou, "Isolation Forest," *2008 Eighth IEEE International Conference on Data Mining*, Pisa, Italy, 2008, pp. 413–422, doi: 10.1109/ICDM.2008.17.
M. Goldstein and S. Uchida, "A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data," *PLoS ONE*, vol. 11, no. 4, e0152173, 2016.
T. Chen and C. Guestrin, "XGBoost: A Scalable Tree Boosting System," *Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining*, San Francisco, CA, 2016, pp. 785–794.
C. Cortes and V. Vapnik, "Support-Vector Networks," *Machine Learning*, vol. 20, no. 3, pp. 273–297, 1995.
A. H. Lashkari, M. S. I. Mamun, and A. A. Ghorbani, "A Comprehensive Feature Selection Method for Intrusion Detection Systems," *2016 IEEE World Congress on Computational Intelligence (WCCI)*, Vancouver, BC, 2016, pp. 1–8.
S. Aggarwal and N. Kumar, "Big Data Analytics for Tax Fraud Detection Using MapReduce," *International Journal of Computer Applications*, vol. 104, no. 13, pp. 14–18, 2014.
T. Bifet, G. Holmes, B. Pfahringer and R. Kirkby, "MOA: Massive Online Analysis," *Journal of Machine Learning Research*, vol. 11, pp. 1601–1604, 2010.
K. Pearson, "On Lines and Planes of Closest Fit to Systems of Points in Space," *Philosophical Magazine*, vol. 2, no. 11, pp. 559–572, 1901.
B. Settles, "Active Learning Literature Survey," *University of Wisconsin-Madison, Computer Sciences Technical Report 1648*, 2009.
M. J. Zaki and W. Meira Jr., *Data Mining and Analysis: Fundamental Concepts and Algorithms*, Cambridge University Press, 2014.

Cite this Article as: Narmadha R. (2025), “AI-Powered Tax Fraud Detection System for Income Tax Filings”, International Journal of Marketing & Financial Management, Volume 13, Issue 1, 2025, pp 40-.44 DOI : https://doi.org/10.5281/zenodo.15344915

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REFERENCES

L. Liu, T. Chen, and Z. Ma, "Isolation Forest for Anomaly Detection," *2019 IEEE Intelligent Transportation Systems Conference (ITSC)*, Auckland, New Zealand, 2019, pp. 2482–2487, doi: 10.1109/ITSC.2019.8917011.
A. Zaharia, M. Chowdhury, M. J. Franklin, S. Shenker and I. Stoica, "Spark: Cluster Computing with Working Sets," *2nd USENIX Conference on Hot Topics in Cloud Computing (HotCloud’10)*, Boston, MA, 2010.
R. Phua, V. Lee, K. Smith, and R. Gayler, "A Comprehensive Survey of Data Mining-based Fraud Detection Research," *arXiv preprint arXiv:1009.6119*, 2010.
L. Breiman, "Random Forests," *Machine Learning*, vol. 45, no. 1, pp. 5–32, 2001, doi: 10.1023/A:1010933404324.
A. Almalki and K. Aziz, "Hybrid Fraud Detection Framework Using Machine Learning and Rule-Based Techniques in Tax Data," *IEEE Access*, vol. 9, pp. 129861–129874, 2021, doi: 10.1109/ACCESS.2021.3113478.
F. T. Liu, K. M. Ting and Z. Zhou, "Isolation Forest," *2008 Eighth IEEE International Conference on Data Mining*, Pisa, Italy, 2008, pp. 413–422, doi: 10.1109/ICDM.2008.17.
M. Goldstein and S. Uchida, "A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data," *PLoS ONE*, vol. 11, no. 4, e0152173, 2016.
T. Chen and C. Guestrin, "XGBoost: A Scalable Tree Boosting System," *Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining*, San Francisco, CA, 2016, pp. 785–794.
C. Cortes and V. Vapnik, "Support-Vector Networks," *Machine Learning*, vol. 20, no. 3, pp. 273–297, 1995.
A. H. Lashkari, M. S. I. Mamun, and A. A. Ghorbani, "A Comprehensive Feature Selection Method for Intrusion Detection Systems," *2016 IEEE World Congress on Computational Intelligence (WCCI)*, Vancouver, BC, 2016, pp. 1–8.
S. Aggarwal and N. Kumar, "Big Data Analytics for Tax Fraud Detection Using MapReduce," *International Journal of Computer Applications*, vol. 104, no. 13, pp. 14–18, 2014.
T. Bifet, G. Holmes, B. Pfahringer and R. Kirkby, "MOA: Massive Online Analysis," *Journal of Machine Learning Research*, vol. 11, pp. 1601–1604, 2010.
K. Pearson, "On Lines and Planes of Closest Fit to Systems of Points in Space," *Philosophical Magazine*, vol. 2, no. 11, pp. 559–572, 1901.
B. Settles, "Active Learning Literature Survey," *University of Wisconsin-Madison, Computer Sciences Technical Report 1648*, 2009.
M. J. Zaki and W. Meira Jr., *Data Mining and Analysis: Fundamental Concepts and Algorithms*, Cambridge University Press, 2014.

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