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A network and machine learning approach to detect Value Added Tax fraud

Angelos N. Alexopoulos,P. Dellaportas,S. Gyoshev,Christos Kotsogiannis,S. Olhede,Trifon Pavkov

Published 2021 in Unknown venue

ABSTRACT

Value Added Tax (VAT) fraud erodes public revenue and puts legitimate businesses at a disadvantaged position thereby impacting inequality. Identifying and combating VAT fraud before it occurs is therefore important for welfare. This paper proposes flexible machine learning algorithms which detect fraudulent transactions, utilising the information provided by the complex VAT network structure of a large dimension. VAT fraud detection is implemented through a combination of a suitably constructed Laplacian matrix with classification algorithms that rely on scalable machine learning techniques. The method is implemented on the universe of Bulgarian VAT data and detects around 50 percent of the VAT fraud, outperforming well-known techniques that ignore the information provided by the network of VAT transactions. Importantly, the proposed methods are automated, and can be implemented following the taxpayers submission of their VAT returns. This allows tax revenue authorities to prevent large losses of tax revenues through performing early identification of fraud between business-to-business transactions within the VAT system.

PUBLICATION RECORD

  • Publication year

    2021

  • Venue

    Unknown venue

  • Publication date

    2021-06-26

  • Fields of study

    Physics, Computer Science, Economics, Mathematics, Business

  • Identifiers
    arXiv 2106.14005
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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