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Tax Defense Analytics: A Data-Driven Application for Identifying Cross-Border VAT Fraud and Tax Avoidance Schemes

In: Recent Advances in Information Systems

Author

Listed:
  • Kristof Mose

    (Carl von Ossietzky Universität Oldenburg)

  • Keyvan Narimani

    (Lower Saxony State Tax Office)

  • Maged Ahmad

    (Lower Saxony State Tax Office)

  • André Klümpen

    (Lower Saxony State Tax Office)

  • Jorge Marx Gómez

    (Carl von Ossietzky Universität Oldenburg)

Abstract

In this work, we present the current state of a work in progress multifunctional application for detecting cross-border value added tax (VAT) fraud and identifying tax avoidance schemes of multinational corporations. We outline the design and architecture of this data science and artificial intelligence (AI) driven application, which incorporates not only data analytics but additional functionalities, which in combination support and elevate the workflow of the auditors. These additional functionalities include a sophisticated data integration pipeline, a multipurpose AI tool suite, as well as a tax expert knowledge management system. Key challenges in data governance, infrastructure, and organizational readiness are discussed. Despite not being finished, this approach already shows the potential to significantly enhance the efficiency and effectiveness of tax fraud detection and tax avoidance identification. This work aims to share insights from operationalizing AI in tax administration to facilitate broader digital transformation efforts.

Suggested Citation

  • Kristof Mose & Keyvan Narimani & Maged Ahmad & André Klümpen & Jorge Marx Gómez, 2026. "Tax Defense Analytics: A Data-Driven Application for Identifying Cross-Border VAT Fraud and Tax Avoidance Schemes," Progress in IS, in: Jorge Marx Gómez & Roopesh Kevin Sungkur & Sameerchand Pudaruth & Jan-Hendrik Witte & Gerrit Schuman (ed.), Recent Advances in Information Systems, pages 61-70, Springer.
    • Handle: RePEc:spr:prochp:978-3-032-14099-9_5
    DOI: 10.1007/978-3-032-14099-9_5
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