• Anju Sharma
• Dr. Kapil Kumar

The Goods and Services Tax (GST) is a significant change in tax policy that is designed to improve transparency and efficiency. Nevertheless, GST fraud continues to be a significant concern, obstructing tax compliance and revenue collection. The objective of this paper is to improve the integrity of the tax system by introducing a machine learning-based approach to the detection of GST fraud. A survey of 50 participants, including tax professionals and software engineers, is included in the methodology to collect insights on prevalent fraud tactics and challenges. Data on typical fraudulent behaviours and extant detection methods was collected through the use of a questionnaire. For the purpose to analyse transaction data and identify anomalies that suggest fraud, machine learning techniques were implemented. In order to detect anomalies, a variety of algorithms were implemented, including supervised methods such as decision trees and random forests, as well as unsupervised methods like clustering. The models were trained and evaluated using historical transaction records in conjunction with survey data. The results indicate that traditional methods are considerably outperformed by machine learning models in terms of the detection of deceptive activities. High accuracy was demonstrated in the identification of patterns associated with tax evasion, including fraudulent invoices and unreported transactions, by specific algorithms, such as random forests. The significance of the integration of advanced detection systems and continuous model updates into tax administration was also underscored by the results. In summary, machine learning is a potent instrument for the detection of GST fraud, providing improved efficiency and accuracy. The integration of these technologies into tax compliance frameworks can result in more effective fraud prevention and revenue assurance, which is advantageous for both tax authorities and businesses.

GST Fraud, Machine Learning, Fraud Detection, Tax Compliance.

"DETECTING GST FRAUD THROUGH MACHINE LEARNING TECHNIQUES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 3, page no.320-337, March-2021, Available :http://www.jetir.org/papers/JETIR2103433.pdf

"DETECTING GST FRAUD THROUGH MACHINE LEARNING TECHNIQUES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 3, page no. pp320-337, March-2021, Available at : http://www.jetir.org/papers/JETIR2103433.pdf