Tax Audit in the Era of Big Data: The Case of Indonesia

Agung Darono, Aldi Pratama


This research uses an interpretive case study strategy to investigate how big data affects tax audits in Indonesia, both with regard to tax audit management and policy, and to tax auditors’ individual audit assignments. The study reveals that the impact of big data on tax audit exists in two aspects. First, at audit policy level, big data is used as part of risk analysis in order to determine which taxpayers should be audited. Second, at the individual tax audit assignment level, tax auditors must utilise big data in order to acquire and analyse data from taxpayers and other related parties. Big data has the following characteristics: it involves huge volumes of information, it is generated at a high velocity, it includes a wide array of data types, and it contains high uncertainty. Big data can be analysed in order to reinforce the results gained from risk engines as a part of a compliance risk management system at the audit policy level. Meanwhile, at the individual tax audit assignment level, empirical evidence shows that tax auditors may deal with: (1) large volumes of data (hundreds of millions of records) that originated from previous fiscal years (historical records); (2) variations in the format and sources of data acquired from taxpayers which, to some extent, may be giving an auditor the authority to request data in a format that suits their analytical tools—with an inherent risk that the data can only be acquired in its native format; (3) data veracity that requires the tax auditors to review data sources because the adopted data analysis techniques are determined by the validity of data under audit. The main benefit expected to be gained from the implementation of big data analytics in respect of tax audits is the provision of valid and reliable information that evidences that taxpayers are compliant with tax laws.

Keywords: Audit Policy, Audit Test, Big Data, Data Compatibility, Data Veracity.

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Alles, M., & Gray. G. L. (2016). Incorporating big data in audits: Identifying inhibitors and a research agenda to address those inhibitors. International Journal of Accounting Information Systems, 22, 44-59.

Arens, A. A., Elder, R. J., Beasley, M. S., & Hogan, C. E. (2017). Auditing and assurance services: An integrated approach (16th ed.). Harlow, England: Pearson Education Limited.

Bakker, J. I. (H.). (2010). Interpretivism. In A. J. Mills, G. Durepos, & E. Wiebe (Eds.) Encyclopedia of Case Study Research (Volume 1, pp. 486-492). Thousand Oaks, CA: SAGE Publications, Inc.

Booch, G., Rumbaugh, J., & Jacobson, I. (1998). Unified modeling language user guide. Reading, MA: Addison-Wesley.

Bowen, G. A. (2009). Document analysis as a qualitative research method. Qualitative Research Journal, 9(2), 27-40.

Brink, W., & Hansen, V. (2018, 1 May). Using big data to identify tax risk. The Tax Adviser.

Cascarino, R. E. (2017). Data analytics for internal auditors. Boca Raton, FL: CRC Press.

Chang, W. L., Grady, N., & the National Institute of Standards and Technology Big Data Public Working Group (2018). NIST big data interoperability framework: Volume 1, Big data definitions (Special Publication (NIST SP) - 1500-1 Version 2).

Chaudhari, P. A., & Paikrao. R. L. (2012). Web data extraction. IJCA Proceedings on Emerging Trends in Computer Science and Information Technology-2012(ETCSIT2012) ETCSIT (4), 13-17.

Chen, S.-C., Wu, C.-C., & Miau, S. (2015). Constructing an integrated e-invoice system: The Taiwan experience. Transforming Government: People, Process and Policy, 9(3), 370-383.

Cockfield, A. J. (2016). Big data and tax haven secrecy. Florida Tax Review, 18(8), 483-539.

Coffey, A. (2014). Analysing documents. In U. Flick (Ed.), The SAGE handbook of qualitative data analysis (pp. 367-379). London, England: Sage Publications Ltd.

Creswell, J. W. (2013). Research design: Qualitative, quantitative, and mixed methods approaches (4th ed.). London, England: SAGE Publications Ltd.

Darano, A., & Ardianto, D. (2016). The use of CAATTs in tax audits—lessons from some international practices. eJournal of Tax Research, 14(2), 506-526.

Darano, A., & Irawati, D. (2015). Service innovation in the complex environment of tax administration: The Indonesian public service perspective. International Journal of Innovation and Regional Development, 6(1), 102-123.

Davenport, T. H., & Harris, J. G. (2007). Competing on analytics: The new science of winning. Boston, MA: Harvard Business Review Press.

Deloitte. (2016). Tax data analytics: A new era for tax planning and compliance. n.p.: Deloitte.

Devlin, B. (2018). The EDW lives on: The beating heart of the data lake [White paper]. 9sight Consulting.

Diebold, F. X. (2012). On the origin(s) and development of the term “big data” (PIER Working Paper 12-037). Philadelphia, PA: Penn Institute for Economic Research, University of Pennsylvania.

Dimitropoulou, C., Govind, S., & Turcan, L. (2018). Applying modern, disruptive technologies to improve the effectiveness of tax treaty dispute resolution: Part 1. Intertax, 46(11), 856-970.

Diop, K. A. S., & Liu, E. (2020). Categorization of case in case study research method: New approach. Knowledge and Performance Management, 4(1), 1-14.

Direktorat Jenderal Pajak. (2015). Peraturan Direktur Jenderal Pajak Nomor PER-46/PJ/2015 tentang Cetak Biru Teknologi Informasi Dan Komunikasi Direktorat Jenderal Pajak Kementerian Keuangan Republik Indonesia Tahun 2015-2019. [Regulation of the Director General of Taxes, No. 46/PJ/2015: Information and Communications Blueprint of the Director General of Taxes 2015-2019].

Direktorat Jenderal Pajak. (2017). Annual report 2017. Jakarta, Indonesia: Direktorat Jenderal Pajak.

Direktorat Jenderal Pajak. (2022). CRM BI: Langkah awal menuju data driven organization. [CRM: BI: First steps towards a data driven organization]. Jakarta, Indonesia: Direktorat Jenderal Pajak.

Djuniardi, I. (2016, September). Next generation data analysis: The implementation of big data in Directorate General of Taxes Republic of Indonesia [Conference presentation]. 13th Association of Tax Authorities of Islamic Countries Annual Technical Conference, Melaka, Malaysia.

Djuniardi, I. (2018). Journey to big data. Jakarta, India: Direktorat Jenderal Pajak.

Drula, G. (2014). Media convergence and mobile technology. Journal of Media Research, 7(3), 47-71.

Edery, C. (2016). Big data serving tax compliance. In Intra-European Organisation of Tax Administrations, Data-driven tax administration (pp. 48-50). Budapest, Hungary: IOTA.

Edosomwan, S., Prakasan, S. K., Kouamé, D., Watson, J., & Seymour, T. (2011). The history of social media and its impact on business. Journal of Applied Management and Entrepreneurship, 16(3), 79-91.

EY Americas. (2019, June 27). How tax and finance departments can deliver value in the digital era. EY.

Fox, S. J. (2017). The rise of the drones: Framework and governance—Why risk it! Journal of Air Law and Commerce, 82(4), 683-715.

Gaillard, M. (2017, July 6). CERN data centre passes the 200-petabyte milestone. CERN.

Gantz, J., & Reinsel, D. (2011). Extracting value from chaos. Framingham, MA: IDC.

Goldstein, S. (2019, 24 July). Two key differences between digital forensic imaging and digital forensic clone and how they can affect your legal case. Capsicum Group.

G20 Sherpa Indonesia. (2019). History of the G20. G20 Indonesia Secretariat, Coordinating Ministry of Economic Affairs, Government of Indonesia.

Hartley, J. (2004). Case study research. In C. Cassell & G. Symon (Eds.), Essential guide to qualitative methods in organizational research (pp. 323-33). London, England: SAGE Publications Ltd.

Helskyaho, H. (2017). Big data and the multi-model database [Presentation]. The Nordic ACE Tour 2017.

Houser, K, A., & Sanders, D. (2017). The use of big data analytics by the IRS: Efficient solutions or the end of privacy as we know it? Vanderbilt Journal of Entertainment & Technology Law, 19(4), 817-872.

Howcroft, D., & Trauth, E. M. (2004). The choice of critical information systems research. In B. Kaplan, D. P. Truex III, D, Wastell, A. T. Wood-Harper, & J. I. DeGross (Eds.), Information systems research: Relevant theory and informed practice (IFIP International Federation for Information Processing, Vol. 143, pp. 195-211). Boston, MA: Springer.

Hu, H., Wen, Y., Chua, T.-S., & Li, X. (2014). Toward scalable systems for big data analytics: A technology tutorial. IEEE Access, 2, 652-87.

Hunton, J. E., Bryant, S. M., & Bragranoff, N. A. (2004). Core concepts of information technology auditing. Hoboken, NJ: John Wiley & Sons, Inc.

InfoDev & the Center for Democracy & Technology. (2002). The e-government handbook for developing countries. Washington, D.C.: The Center for Democracy and Technology.

Intra-European Organisation of Tax Administrations. (2016). IOTA good practice guide: Applying data and analytics in tax administrations. Budapest, Hungary: IOTA.

ISACA. (2011). Data analytics—A practical approach. Schaumburg, IL: ISACA.

Jain, S. (2008). Remote sensing application for property tax evaluation. International Journal of Applied Earth Observation and Geoinformation, 10(1), 109-121.

The Jakarta Post Editorial Board. (2019, June 27). We are G20. The Jakarta Post.,that%20the%20country%20stands%20out

Johannesson, P., & Perjons, E. (2014). An introduction to design science. Cham, Switzerland: Springer International Publishing.

Klein, H. K., & Myers, M. D. (1999). A set of principles for conducting and evaluating interpretive field studies in information systems. MIS Quarterly, 23(1), 67-93.

Krotov, V., & Silva, L. (2018). Legality and ethics of web scraping. Twenty-fourth Americas conference on information systems, New Orleans, 2018, 1-5.

Kundu, A., & Kundu, S. G. (2016). Big data analytics & its applications in the tax domain. BIMS International Journal of Social Science Research, 1(2), 117-133.

Laney, D. (2001). 3D data management: Controlling data volume, velocity, and variety (Application Delivery Strategies, File No. 949). Stamford, CT: META Group, Inc.

Laney, D. (2012). Deja vvvu: Others claiming Gartner’s construct for big data [Blog post].

Lewis, C. (2019). Raising more public revenue in Indonesia in a growth- and equity-friendly way (OECD Economics Department Working Papers No. 1534). Paris, France: Organisation for Economic Co-operation and Development.

Lu, J., & Holubová, I. (2019). Multi-model databases: A new journey to handle the variety of data. ACM Computing Surveys, 52(3), 1-38.

Luisi, J. V. (2014). Pragmatic enterprise architecture - Strategies to transform information systems in the era of big data. Waltham, MA: Morgan Kaufmann.

Manyika, J., Chui, M., Brown, B., Bughin J., Dobbs, R., Roxburgh, C., & Hung Byers, A. (2011). Big data: The next frontier for innovation, competition, and productivity. New York, NY: McKinsey Global Institute.

McAfee, A., Brynjolfsson, E., Davenport, T. H., Patil, D. J., & Barton, D. (2012). Big data: The management revolution. Harvard Business Review, 90(10), 60-68.

McKemmish, R. (2008). When is digital evidence forensically sound? In I. Ray & S. Shenoi (Eds.), Advances in Digital Forensics IV (pp. 3-15). New York, NY: Springer.

McKerchar, M. (2008). Philosophical paradigms, inquiry strategies and knowledge claims: Applying the principles of research design and conduct to taxation. eJournal of Tax Research, 6(1), 5-22.

Mehta, P., Mathews, J., Kumar, S., Suryamukhi, K., Sobhan Babu, Ch., Kasi Visweswara Rao, S. V., Shivapujimath, V., & Bisht, D. (2019). Big data analytics for tax administration. In A. Kö, E. Francesconi, G. Anderst-Kotsis, A. Tjoa, & I. Khalil (Eds.), Electronic Government and the Information Systems Perspective: 8th International Conference, EGOVIS 2019, Linz, Austria, August 26–29, 2019, Proceedings (pp. 47-57). Cham, Switzerland: Springer Nature Switzerland AG.

Menke, M., & Schwarzenegger, C. (2019). On the relativity of old and new media: A lifeworld perspective. Convergence: The International Journal of Research into New Media Technologies, 25(4), 657-672.

Microsoft & PricewaterhouseCoopers. (2018). The data intelligent tax administration: Meeting the challenges of big tax data and analytics [White paper]. Microsoft and PwC.

Ministry of Finance. (2015). Use of drone and aerial in tax potential search activities (in Bahasa Indonesia). In Notes on innovation and transformation (pp. 62-67). Jakarta, Indonesia: Balai Pustaka & Ministry of Finance (MoF).

Montasari, R. (2017). A standardised data acquisition process model for digital forensic investigations. International Journal of Information and Computer Security, 9(3), 229-249.

Mousa, R. (2011). E-government adoption process: XBRL adoption in HM revenue and customs and companies house. [Doctoral dissertation, University of Birmingham]. UBIRA E Theses.

Oates, B. J. (2006). Researching information system and computing. London, England: SAGE Publications.

Oats, L. (2012). On methods and methodology. In L. Oats (Ed.), Taxation: A fieldwork research handbook (pp. 9-18). Abingdon, England: Routledge.

Olson, M. (2010). Document analysis. In A. J. Mills, G. Durepos, & E. Wiebe (Eds.), Encyclopedia of Case Study Research (Volume 1, pp. 319-320). Thousand Oaks, CA: SAGE Publications, Inc.

Organisation for Economic Co-operation and Development. (2004). Forum on tax administration: Guidance note: Guidance for the standard audit file - Tax version 2.0. Paris, France: OECD Publishing.

Organisation for Economic Co-operation and Development. (2015). Addressing the tax challenges of the digital economy, Action 1: 2015 final report. Paris, France: OECD Publishing.

Organisation for Economic Co-operation and Development. (2016a). Advanced analytics for better tax administration: Putting data to work. Paris, France: OECD Publishing.

Organisation for Economic Co-operation and Development. (2016b). Technologies for better tax administration: A practical guide for revenue bodies. Paris, France: OECD Publishing.

Organisation for Economic Co-operation and Development. (2017). Technology tools to tackle tax evasion and tax fraud. Paris, France: OECD Publishing.

Pethe, H. B., & Pande, D. S. (2016). An overview of cryptographic hash functions MD-5 and SHA. IOSR Journal of Computer Engineering: National Conference on Recent Trends in Computer Science and Information Technology, 5, 37-42.

Pijnenburg, M., Kowalczyk, W., & van der Hel-van Dijk, L. (2017). A roadmap for analytics in taxpayer supervision. The Electronic Journal of E-Government, 15(1), 19-32.

Podesta, J., Pritzker, P., Moniz, E. J., Holdren, J., & Zients, J. (2014). Big data: Seizing opportunities, preserving values. Washington, D.C.: Executive Office of the President, The White House.

Pratomo, M. H. (2018). Investigating tax compliance risks of large businesses in Indonesia [Doctoral thesis, RMIT University]. RMIT University Research Repository.

PwC. (2015). Tax policy. PwC.

Richardson, V. J., Teeter, R. A., & Terrell, K. L. (2019). Data analytics for accounting. New York, NY: McGraw-Hill Education.

Rogers, H., & Oats, L. (2012). Case studies. In L. Oats (Ed.), Taxation: A fieldwork research handbook (pp. 26-33). Abingdon, England: Routledge.

Sakti, N. W. (2021, September 7-9). Implementation of data analytics in Directorate General of Taxes [Conference presentation]. The 2nd Conference of Belt and Road Initiative Tax Administration Cooperation Forum - Digitalization of Tax Administration, Online.

Salijeni, G., Samsonova-Taddei, A., & Turley, S. (2019). Big data and changes in audit technology: Contemplating a research agenda. Accounting and Business, 49(1), 95-119.

Santos, M. R. C., Laureano, R. M. S., & Albino, C. E. R. (2018). How tax audit and tax advisory can benefit from big data analytics tools data analysis and processing in relational databases using SQL Server and Power Pivot & Power View in Excel. 2018 13th Iberian conference on information systems and technologies (CISTI), 1-6.

Schroeck, M., Shockley, R., Smart, J., Romero-Morales, D., & Tufano (2012). Analytics: The real-world use of big data - How innovative enterprises extract value from uncertain data. Somers, NY: IBM Corporation.

Tian, F., Lan, T., Zheng, Q., Chao, K-.M., Godwin, N., Shah, N., & Zhang, F. (2017). Mining suspicious tax evasion groups in big data (Extended abstract). Proceedings of the 2017 IEEE 33rd International Conference on Data Engineering (ICDE), 25-26.

Vasarhelyi, M. A., Kogan, A., & Tuttle, B. M. (2015). Big data in accounting: An overview. Accounting Horizons, 29(2), 381-96.

Veit, A. (2019). Swimming upstream: Leveraging data and analytics for taxpayer engagement – An Australian and international perspective. eJournal of Tax Research, 16(3), 474-499.

Volvach, D., & Solovyev., M. (2018). Tax administration in the digital era: The FTS of Russia Approach. In Intra-European Organisation of Tax Administrations (Ed.), Impact of digitalisation on the transformation of tax administrations (pp.13-15). Budapest, Hungary: IOTA.

Walsham, G. (2006). Doing interpretive research. European Journal of Information Systems, 15(3), 320-330.

The World Bank. (2002). The e-government handbook for developing countries: A project of InfoDev and the Center for Democracy and Technology. Washington D.C.: The World Bank Group.

Yazan, B. (2015). Three approaches to case study methods in education: Yin, Merriam, and Stake. The Qualitative Report, 20(2), 134-152.

Yin, R. K. (2018). Case study research and applications: Design and methods (6th ed.). Los Angeles, CA: SAGE Publications, Inc.

Zuca, M., & Tinta, A. (2018). The contribution of computer assisted auditing techniques (CAAT) and of the business intelligence instruments in financial audit. Academic Journal of Economic Studies, 4(1), 183-191.


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