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Vergi Denetim Sürecinde Büyük Veri Analitiği

Yıl 2020, Cilt: 8 Sayı: 1, 1 - 24, 29.06.2020

Öz

Büyük veri analitiği geleneksel veritabanı teknolojilerinin sınırını aşan, birçok kaynaktan sağlanan farklı formatlardaki verilerin bir araya getirilmesi, standartlaştırılması ve işlenmesi yoluyla bilgi üretme sürecini ifade etmektedir. Söz konusu teknolojinin ekonomide uygulama alanının genişlemesi vergi idareleri açısından yeni fırsat ve tehditler ortaya çıkarmaktadır. Büyük veri ve bağlantılı teknolojiler vergi idaresine gerçek zamanlı denetim, mükellef beyanına bağımlılığın azalması, risk analizine dayalı mükellef seçimi, illegal faaliyetler öncesi önleyici prosedürler, iktisadi kararlarla ilgili eğilim, trend ve kalıpların belirlenmesi başta olmak üzere yeni imkan ve araçlar sunmaktadır. Bu çalışmanın amacı büyük veri analitiğinin vergi denetim sürecinde ortaya çıkaracağı yapısal dönüşümü, olası faydalar, dezavantajlar ve zorluklar çerçevesinde değerlendirmektir. Yeni teknolojilere uyum sağlanabilmesi için vergi idarelerinin fiziki ve beşeri altyapısının yeniden yapılandırılması; vergi denetim sürecinde etkinliğin artırılması, vergi kaybının önlenmesi, dönüşümün iktisadi hayattaki etkilerinin yönetilmesi ve yönlendirilmesi açısından bir tercihten ziyade bir zorunluluktur.

Kaynakça

  • AICPA (2015), Audit Analytics and Continuous Audit: Looking Toward the Future, American Institute of Certified Public Accountants Report, https://www.aicpa.org/interestareas/frc/assuranceadvi soryservices/downloadabledocuments/auditanalytics_lookingtowardfuture.pdf, 20.12.2019.
  • Baisalbayeva, K., E. Enden, R. Tenan, ve R. Flores (2018), The Data Intelligent Tax Administration, Microsoft and PricewaterhouseCoopers Publication, p.34. Dai, J. (2017), Three Essays On Audit Technology: Audit 4.0, Blockchain, and Audit APP, Doctoral dissertation, The State University of New Jersey, p.173
  • Dai, J. ve M.A. Vasarhelyi (2016), “Imagineering Audit 4.0”, Journal of Emerging Technologies in Accounting, Volume: 13, Issue:1, p.1-15.
  • Diebold, F. X. (2003), “Big Data Dynamic Factor Models for Macroeconomic Measurement and Forecasting”, Advances in Economics and Econometrics: Theory and Applications, Eighth World Congress of the Econometric Society, edt. M. Dewatripont, LP Hansen and S. Turnovsky, p. 115-122.
  • Drath, R., and A. Horch. (2014), “Industrie 4.0: Hit or Hype?”, IEEE Industrial Electronics Magazine, Volume: 8, Issue: 2, p.56–58.
  • Earley, C.E. (2015), “Data Analytics in Auditing: Opportunities and Challenges”, Business Horizons, Volume 58, Issue 5, p.493-500.
  • European Commission (2018), “Stronger Protection, New Opportunities”, Commission Guidance on the Direct Application of the GDPR Regulation as of 25 May 2018.
  • Frey, C. B. and M.A. Osborne (2017), “The Future of Employment: How Susceptible are Jobs to Computerisation?”, Technological forecasting and social change, Volume: 114, p.254-280.
  • Grant Thornton-GTIL (2017), Seizing Opportunities: Meeting the Challenge of Building a Tax Function of the Future, https://www.grantthornton.com.au/insights/publications/seizing-opportunities-with-tax-automation, 12.08.2019.
  • Gray, G. L., V. Chiu, Q. Liu, and P. Li. (2014), “The Expert Systems Life Cycle in AIS Research: What Does It Mean for Future AIS Research?”, International Journal of Accounting Information Systems, Volume: 15, Issue: 4, p.423-451.
  • Groves, P., B. Kayyali, D. Knott, and S. Van Kuiken (2013), “The Big Data Revolution in Healthcare”, McKinsey Quarterly, Volume: 2, Issue: 3, p.1-22.
  • Hamari, J., M. Sjöklint and A. Ukkonen (2015), “The Sharing Economy: Why People Participate in Colloborative Consumption”, Journal of The Association for Information Science and Technology, Volume: 67, Issue: 9, p.2047-2059.
  • Hemberg, E., J. Rosen, G. Warner and S. Wijesinghe (2015), Tax Non-Compliance Detection Using Co-Evolution of Tax Evasion Risk and Audit Likelihood, The 15th International Conference on Artificial Intelligence and Law, San Diego.
  • Hsu, K. W., N. Pathak, J. Srivastava, G. Tschida, and E. Bjorklund (2015), “Data Mining Based Tax Audit Selection: a Case Study of a Pilot Project at the Minnesota Department of Revenue”, Real world data mining applications, Springer Cham., p. 221-245.
  • IMRECZE (2016), Data-Driven Tax Administration, Intra-European Organisation of Tax Administrations Report, pp.12-18
  • IOTA (2018), Good Practice Guide Applying Data and Analytics in Tax Administrations, Intra-European Organisation of Tax Administrations Report, p.1-18.
  • Ipsos Mori (2014), Ipsos Global Trends, https://www.ipsos.com/ipsos-mori/en-uk/three-four-britons-are-worried-about-companies-collecting-information-about-them, 01.09.2019.
  • Jacobs, B. (2017), “Digitalization and Taxation”, Digital Revolutions in Public Finance, (ed.) S. Gupta, M. Keen, A. Shah, G. Verdier, Washington: IMF, p.25-53.
  • Krishna, A., M. Fleming and S. Assefa (2017), “Instilling Digital Trust: Blockchain and Cognitive Computing for Government”, Digital Revolutions in Public Finance, (ed.) S. Gupta, M. Keen, A. Shah, G. Verdier, Washington: IMF, p.173-197.
  • Linke, D., M. Herring and G. Wardell-Johnson (2017), Technology in Tax, KPGM International Tax Catalyst Report, p.1-20.
  • Maciejewski, M. (2017), “To do More, Better, Faster and More Cheaply: Using Big Data in Public Administration”, International Review of Administrative Sciences, Volume: 83, Issue: 1, p.120-135.
  • Manyika, J. (2011), Big Data: the Next Frontier for innovation, Competition, and Productivity. http://www.mckinsey.com/Insights/MGI/ Research/Technology_and_Innovation/Big_data_The_next_frontier_for_innovation, 21.06.2019.
  • Marnin, M. (2016), Using Artificial Intelligence to Defeat Tax Evasion, and the Nonparticipation of the US, The Swiss-American Chamber of Commerce Emerging Issues in CRS, https://www.amcham.ch 20.10.2019.
  • Martin-Sanchez, F.J., V. Aguiar-Pulido, G.H. Lopez-Campos, N. Peek and L. Sacchi (2017), “Secondary Use and Analysis of Big Data Collected for Patient Care”, IMIA Yearbook, Volume: 26, p.1–10.
  • Mahzan, N., and A. Lymer. (2014), “Examining the Adoption of Computer-Assisted Audit Tools and Techniques: Cases of Generalized Audit Software Use by Internal Auditors”, Managerial Auditing Journal, Volume:29, Issue: 4, p.327-349.
  • Milner, C. and B. Berg (2016), Tax Analytics Artificial Intelligence and Machine Learning–Level 5, PwC Advanced Tax Analytics & Innovation, https://www.pwc.no/no/publikasjoner/Digitalisering/ artificial-intelligence-and-machine-learning-final1.pdf 04.11.2019.
  • Mills, L. (2016), Inland Revenue’s Multinational Enterprises Compliance Focus, Transparency Times, https://www.transparency.org.nz/ newsletter/transparency-times-november-2016, 08.08.2019.
  • OECD (2016), Advanced Analytics for Better Tax Administration: Putting Data to Work, OECD Publishing, Paris, https://doi.org/10.1787/9789264256453-en, 27.09.2019.
  • OECD (2017a), The Changing Tax Compliance Environment and the Role of Audit, OECD Publishing, Paris, https://doi.org/10.1787/9789264282186-en, 18.09.2019.
  • OECD (2017b), Technology Tools to Tackle Tax Evasion and Tax Fraud. www.oecd.org/tax/crime, 13.07. 2019.
  • Pande, G. and R. Patni (2016), Tax Technology and Transformation, Ernst & Young TaxTech India Survey.
  • PricewaterhouseCoopers (2015), The Sharing Economy, PwC Consumer Intelligence Series, http://www.pwc.com/cis, 26.10.2019.
  • Tinholt, D., S. Enzerink, W. Sträter, P. Hautvast and W. Carrara (2017), Unleashing the Potential of Artificial Intelligence in the Public Sector, Capgemini Consulting Report, www.capgemini-consulting.com, 23.10.2019.
  • Vergi Denetim Kurulu, (2019), Faaliyet Raporu 2018, Vergi Denetim Kurulu Başkanlığı, Strateji, İş Geliştirme ve Kontrol Şube Müdürlüğü, s.121. Viglione, J. and D. Deputy (2017), Your Tax Data Is Ripe for Artificial Intelligence. Are You Prepared, Corptax Tax Executive, p.25-30.

Big Data Analysis in Tax Audit

Yıl 2020, Cilt: 8 Sayı: 1, 1 - 24, 29.06.2020

Öz

Big data analytics refers to the process of generating information by combining, standardizing and processing data in different formats from multiple sources that exceed the limit of traditional database technologies. The expansion of the application area of this technology in the economy creates new opportunities and threats for tax administrations. Big data and related technologies offer new opportunities and tools to the tax administration, including real-time auditing, reduction of dependence to taxpayer declaration, taxpayer selection based on risk analysis, preventive procedures before illegal activities, determination of trends and patterns related to economic decisions. The aim of this study is to evaluate the structural transformation of big data analytics during tax audit within the framework of possible benefits, disadvantages and difficulties. Restructuring the physical and human infrastructure of the tax administrations in order to adapt to new technologies is an obligation rather than an option in terms of increasing the efficiency in the tax audit process, preventing tax loss, managing and directing the economic effects of the transformation.

Kaynakça

  • AICPA (2015), Audit Analytics and Continuous Audit: Looking Toward the Future, American Institute of Certified Public Accountants Report, https://www.aicpa.org/interestareas/frc/assuranceadvi soryservices/downloadabledocuments/auditanalytics_lookingtowardfuture.pdf, 20.12.2019.
  • Baisalbayeva, K., E. Enden, R. Tenan, ve R. Flores (2018), The Data Intelligent Tax Administration, Microsoft and PricewaterhouseCoopers Publication, p.34. Dai, J. (2017), Three Essays On Audit Technology: Audit 4.0, Blockchain, and Audit APP, Doctoral dissertation, The State University of New Jersey, p.173
  • Dai, J. ve M.A. Vasarhelyi (2016), “Imagineering Audit 4.0”, Journal of Emerging Technologies in Accounting, Volume: 13, Issue:1, p.1-15.
  • Diebold, F. X. (2003), “Big Data Dynamic Factor Models for Macroeconomic Measurement and Forecasting”, Advances in Economics and Econometrics: Theory and Applications, Eighth World Congress of the Econometric Society, edt. M. Dewatripont, LP Hansen and S. Turnovsky, p. 115-122.
  • Drath, R., and A. Horch. (2014), “Industrie 4.0: Hit or Hype?”, IEEE Industrial Electronics Magazine, Volume: 8, Issue: 2, p.56–58.
  • Earley, C.E. (2015), “Data Analytics in Auditing: Opportunities and Challenges”, Business Horizons, Volume 58, Issue 5, p.493-500.
  • European Commission (2018), “Stronger Protection, New Opportunities”, Commission Guidance on the Direct Application of the GDPR Regulation as of 25 May 2018.
  • Frey, C. B. and M.A. Osborne (2017), “The Future of Employment: How Susceptible are Jobs to Computerisation?”, Technological forecasting and social change, Volume: 114, p.254-280.
  • Grant Thornton-GTIL (2017), Seizing Opportunities: Meeting the Challenge of Building a Tax Function of the Future, https://www.grantthornton.com.au/insights/publications/seizing-opportunities-with-tax-automation, 12.08.2019.
  • Gray, G. L., V. Chiu, Q. Liu, and P. Li. (2014), “The Expert Systems Life Cycle in AIS Research: What Does It Mean for Future AIS Research?”, International Journal of Accounting Information Systems, Volume: 15, Issue: 4, p.423-451.
  • Groves, P., B. Kayyali, D. Knott, and S. Van Kuiken (2013), “The Big Data Revolution in Healthcare”, McKinsey Quarterly, Volume: 2, Issue: 3, p.1-22.
  • Hamari, J., M. Sjöklint and A. Ukkonen (2015), “The Sharing Economy: Why People Participate in Colloborative Consumption”, Journal of The Association for Information Science and Technology, Volume: 67, Issue: 9, p.2047-2059.
  • Hemberg, E., J. Rosen, G. Warner and S. Wijesinghe (2015), Tax Non-Compliance Detection Using Co-Evolution of Tax Evasion Risk and Audit Likelihood, The 15th International Conference on Artificial Intelligence and Law, San Diego.
  • Hsu, K. W., N. Pathak, J. Srivastava, G. Tschida, and E. Bjorklund (2015), “Data Mining Based Tax Audit Selection: a Case Study of a Pilot Project at the Minnesota Department of Revenue”, Real world data mining applications, Springer Cham., p. 221-245.
  • IMRECZE (2016), Data-Driven Tax Administration, Intra-European Organisation of Tax Administrations Report, pp.12-18
  • IOTA (2018), Good Practice Guide Applying Data and Analytics in Tax Administrations, Intra-European Organisation of Tax Administrations Report, p.1-18.
  • Ipsos Mori (2014), Ipsos Global Trends, https://www.ipsos.com/ipsos-mori/en-uk/three-four-britons-are-worried-about-companies-collecting-information-about-them, 01.09.2019.
  • Jacobs, B. (2017), “Digitalization and Taxation”, Digital Revolutions in Public Finance, (ed.) S. Gupta, M. Keen, A. Shah, G. Verdier, Washington: IMF, p.25-53.
  • Krishna, A., M. Fleming and S. Assefa (2017), “Instilling Digital Trust: Blockchain and Cognitive Computing for Government”, Digital Revolutions in Public Finance, (ed.) S. Gupta, M. Keen, A. Shah, G. Verdier, Washington: IMF, p.173-197.
  • Linke, D., M. Herring and G. Wardell-Johnson (2017), Technology in Tax, KPGM International Tax Catalyst Report, p.1-20.
  • Maciejewski, M. (2017), “To do More, Better, Faster and More Cheaply: Using Big Data in Public Administration”, International Review of Administrative Sciences, Volume: 83, Issue: 1, p.120-135.
  • Manyika, J. (2011), Big Data: the Next Frontier for innovation, Competition, and Productivity. http://www.mckinsey.com/Insights/MGI/ Research/Technology_and_Innovation/Big_data_The_next_frontier_for_innovation, 21.06.2019.
  • Marnin, M. (2016), Using Artificial Intelligence to Defeat Tax Evasion, and the Nonparticipation of the US, The Swiss-American Chamber of Commerce Emerging Issues in CRS, https://www.amcham.ch 20.10.2019.
  • Martin-Sanchez, F.J., V. Aguiar-Pulido, G.H. Lopez-Campos, N. Peek and L. Sacchi (2017), “Secondary Use and Analysis of Big Data Collected for Patient Care”, IMIA Yearbook, Volume: 26, p.1–10.
  • Mahzan, N., and A. Lymer. (2014), “Examining the Adoption of Computer-Assisted Audit Tools and Techniques: Cases of Generalized Audit Software Use by Internal Auditors”, Managerial Auditing Journal, Volume:29, Issue: 4, p.327-349.
  • Milner, C. and B. Berg (2016), Tax Analytics Artificial Intelligence and Machine Learning–Level 5, PwC Advanced Tax Analytics & Innovation, https://www.pwc.no/no/publikasjoner/Digitalisering/ artificial-intelligence-and-machine-learning-final1.pdf 04.11.2019.
  • Mills, L. (2016), Inland Revenue’s Multinational Enterprises Compliance Focus, Transparency Times, https://www.transparency.org.nz/ newsletter/transparency-times-november-2016, 08.08.2019.
  • OECD (2016), Advanced Analytics for Better Tax Administration: Putting Data to Work, OECD Publishing, Paris, https://doi.org/10.1787/9789264256453-en, 27.09.2019.
  • OECD (2017a), The Changing Tax Compliance Environment and the Role of Audit, OECD Publishing, Paris, https://doi.org/10.1787/9789264282186-en, 18.09.2019.
  • OECD (2017b), Technology Tools to Tackle Tax Evasion and Tax Fraud. www.oecd.org/tax/crime, 13.07. 2019.
  • Pande, G. and R. Patni (2016), Tax Technology and Transformation, Ernst & Young TaxTech India Survey.
  • PricewaterhouseCoopers (2015), The Sharing Economy, PwC Consumer Intelligence Series, http://www.pwc.com/cis, 26.10.2019.
  • Tinholt, D., S. Enzerink, W. Sträter, P. Hautvast and W. Carrara (2017), Unleashing the Potential of Artificial Intelligence in the Public Sector, Capgemini Consulting Report, www.capgemini-consulting.com, 23.10.2019.
  • Vergi Denetim Kurulu, (2019), Faaliyet Raporu 2018, Vergi Denetim Kurulu Başkanlığı, Strateji, İş Geliştirme ve Kontrol Şube Müdürlüğü, s.121. Viglione, J. and D. Deputy (2017), Your Tax Data Is Ripe for Artificial Intelligence. Are You Prepared, Corptax Tax Executive, p.25-30.
Toplam 34 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Araştırma Makaleleri
Yazarlar

Miraç Fatih İlgün 0000-0002-1305-2067

Yayımlanma Tarihi 29 Haziran 2020
Yayımlandığı Sayı Yıl 2020 Cilt: 8 Sayı: 1

Kaynak Göster

ISNAD İlgün, Miraç Fatih. “Vergi Denetim Sürecinde Büyük Veri Analitiği”. Siyaset, Ekonomi ve Yönetim Araştırmaları Dergisi 8/1 (Haziran 2020), 1-24.