Abstract
This paper describes the use Self-Organizing Maps (or Kohonen Networks) in helping to detect Value Added Tax (VAT) fraud. The sub-Saharan African Country presented as a case study has had its largest share in tax revenues since the introduction of VAT, nearly two decades ago, but still has a relatively modest tax efficiency when compared to European or worldwide standards. This trend can be reversed by strengthening the audit and inspection processes of its Revenue Authority (RA) with Data Mining, taking advantage of historical data stored by different information systems. A Case Study in the southern region of this country is presented, where historical data available from tax audits are compared with the VAT returns using Kohonen Maps. Comparing the experimental results with other anomaly detection algorithms, Kohonen maps prove to be of great value in predicting and characterizing VAT fraud in this case study.
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Notes
- 1.
Approximately 0.44% of the taxpayers, among which 91.6% are positive cases of fraud, typically unbalanced and biased dataset [29].
- 2.
Samples from other VAT regimes or with invalid/non-existent TIN are excluded.
- 3.
States that once registered as a positive case of fraud, the Taxpayer tends to relapse into fraudulent practices in future fiscal years.
- 4.
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Appendix A - Component Maps
Appendix A - Component Maps
Case study components maps.
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Santos, R., Moura, R., Lobo, V. (2022). Application of Kohonen Maps in Predicting and Characterizing VAT Fraud in a Sub-Saharan African Country. In: Faigl, J., Olteanu, M., Drchal, J. (eds) Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization. WSOM+ 2022. Lecture Notes in Networks and Systems, vol 533. Springer, Cham. https://doi.org/10.1007/978-3-031-15444-7_8
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