ΑΙ@AUEB talk: "A machine learning and network approach to Value Added Tax fraud detection", Angelos Alexopoulos,Tuesday 23 May, 16:15-17:30
ΑΙ@AUEB talk, Tuesday 23 May, 16:15-17:30 (hybrid presentation, Greek time)
Speaker: Angelos Alexopoulos (Dept. of Economics, AUEB)
Title: A machine learning and network approach to Value Added Tax fraud detection
Abstract:
Value Added Tax (VAT) fraud erodes tax revenues 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 suitably flexible machine learning algorithms for detection of VAT fraudulent transactions. The innovation of the algorithms is in utilising the information provided by their VAT structure. Making use of the universe of Bulgarian VAT data the fraud detection algorithms detect around 50 percent of the VAT fraud and outperform well-known techniques that ignore the network of VAT transactions. Importantly, the proposed methods are automated, and can be implemented following the taxpayers' submission of their VAT returns, enabling tax revenue authorities, in the EU and elsewhere, to prevent large losses of tax revenues through performing early identification of fraud between business-to-business transactions within the VAT system.
The approach developed can be applied to similar problems where network interactions exist and can be quantified, such as fraud in online transactions, in insurance and social security systems as well as in telecommunication systems.
Room: T105, Troias building
and virtually via MS Teams:
https://teams.microsoft.com/l/meetup-join/19%3aOiYUJgd5vTDTv9p0FnXvTdZ9TTZxIBRHwZzEpD02P-Y1%40thread.tacv2/1683621333082?context=%7b%22Tid%22%3a%22ad5ba4a2-7857-4ea1-895e-b3d5207a174f%22%2c%22Oid%22%3a%225b49c8b5-6801-409c-a8f8-6e18215b3a08%22%7d
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