Using a Data-Driven Model to Predict Taxpayers Filing False Returns: A Case of Zambia Revenue Authority
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-3556-3_34
Reference16 articles.
1. de Roux D, Perez B, Moreno A, del Pilar Villami M (2018) Tax fraud detection for under-reporting declaring using unsupervised machine learning approach. Applied data science track paper, pp 19–23
2. Mwanza M, Phiri J (2016) Fraud detection on Bulk tax data using business intelligence data mining tool: a case of Zambia Revenue Authority. IJARCCE J 5
3. ZRA website, VAT GUIDE. https://www.zra.org.zm/wp-content/uploads/2020/07/VAT-Guide.pdf. Accessed 27 Aug 2023
4. Vanhoeyveld J, Martens D (2020) Value-added tax fraud detection with scalable anomaly techniques. Appl Soft Comput 86
5. ZRA website, Annual report 2021. https://www.zra.org.zm/wp-content/uploads/2022/05/Annual-Report-2021.pdf. Accessed 27 Aug 2023
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