Český finanční a účetní časopis 2021(4):81-99 | DOI: 10.18267/j.cfuc.569

Artificial intelligence in management accounting

Aneta Zemánková
Vysoká škola ekonomická v Praze, Fakulta financí a účetnictví, katedra finančního účetnictví a auditingu

The paper deals with the possibilities of using artificial intelligence in tasks and activities of management accounting. Its aim is to create a comprehensive overview of the current state and possibilities of utilization of artificial intelligence technologies in management accounting, based on a systematic literature review of empirical literature. Academic articles were complemented by current surveys of research consulting companies and professional organizations and bodies. Literature review results are grouped by specific key areas of management accounting, analyzing the tools for facilitating the budgeting and calculation processes or performance measurement. Artificial intelligence is also presented as a customization process or strategic decisions support. Resulting from the executed analysis, a few fundamental aspects affecting the success of artificial intelligence technologies implementation were determined, primarily the quality of the primary data and reasonable choice of activities suitable for automation.

Keywords: Artificial intelligence; ERP system; Management accounting.
JEL classification: M15, M41, O14

Received: July 3, 2021; Revised: December 5, 2021; Accepted: January 8, 2022; Prepublished online: February 7, 2022; Published: February 8, 2022  Show citation

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Zemánková, A. (2021). Artificial intelligence in management accounting. Czech Financial and Accounting Journal, 2021(4), 81-99. doi: 10.18267/j.cfuc.569
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    References

    1. ABUSUKHON, A., HAWASHIN, B., LAFI, M., 2021. An Efficient System for Reducing the Power Consumption in Offices Using the Internet of Things. International Journal of Advances in Soft Computing and its Applications. Sv. 13, č. 1, s. 1-26.
    2. CALVARD, T. S., 2016. Big data, organizational learning, and sensemaking: Theorizing interpretive challenges under conditions of dynamic complexity. Management Learning. Sv. 47, č. 1, s. 65-82. doi: 10.1177/1350507615592113. Go to original source...
    3. CORBAN, T., 2021. Data as a Strategic Asset [online]. [vid. 6. 6. 2021]. Dostupné z: https://sfmagazine.com/post-entry/april-2021-data-as-a-strategic-asset/.
    4. DE RIJCK, P., JORISSEN, A., 2021. The impact of a firm's big data analytics capability on managerial decision-making and control: a case study on the intervening mechanisms.
    5. DELOITTE, 2020. Data Valuation: Understanding the value of your data assets [online]. [vid. 4. 6. 2020]. Dostupné z: https://www2.deloitte.com/content/dam/ Deloitte/global/Documents/Finance/Valuation-Data-Digital.pdf.
    6. DUTTA, S., 2015. Forensic Analytics and Management Accountants [online]. [vid. 4. 6. 2020]. Dostupné z: https://www.imanet.org/insights-and-trends/risk-management/forensic-analytics-and-management-accountants?ssopc=1.
    7. ELBELTAGI, E., WEFKI, H., 2021. Predicting energy consumption for residential buildings using ANN through parametric modeling. Energy Reports. Sv. 7, s. 2534-2545. doi: 10.1016/j.egyr.2021.04.053. Go to original source...
    8. FSN PUBLISHING LIMITED, 2017. The Future of Planning Budgeting and Forecasting - Global Survey 2017 [online]. [vid. 4. 6. 2020]. Dostupné z: https://fsn.co.uk/research-papers/the-future-of-planning-budgeting-and-forecasting-global-survey-2017/.
    9. GARTNER, 2019. Gartner Survey Shows 37 Percent of Organizations Have Implemented AI in Some Form [online] [vid. 3. 11. 2019]. Dostupné z: https://www.gartner.com/en/newsroom/press-releases/2019-01-21-gartner-survey-shows-37-percent-of-organizations-have.
    10. GARTNER, 2020. Gartner Survey of Nearly 2,000 CIOs Reveals Top Performing Enterprises are Prioritizing Digital Innovation During the Pandemic [online]. [vid. 24. 5. 2021]. Dostupné z: https://www.gartner.com/en/newsroom/press-releases/2020-10-20-gartner-survey-of-nearly-2000-cios-reveals-top-performing-enterprises-are-prioritizing-digital-innovation-during-the-pandemic.
    11. GARTNER, 2021. Gartner Survey Reveals Over Half of CIOs Plan to Increase Full-Time Employees in IT to Accelerate Digital Initiatives in 2021 [online]. [vid. 24. 5. 2021]. Dostupné z: https://www.gartner.com/en/newsroom/press-releases/2021-03-31-gartner-survey-reveals-over-half-of-cios-plan-to-increase-full-time-employees-in-it-to-accelerate-digital-business-initiatives-in-2021.
    12. GÄRTNER, B., HIEBL, M. R. W., 2017. Issues with Big Data. In QUINN, M., STRAUSS, E. The Routledge Companion to Accounting Information Systems. London: Routledge. doi: 10.4324/9781315647210-13. Go to original source...
    13. GRANLUND, M., MALMI, T., 2002. Moderate impact of ERPS on management accounting: a lag or permanent outcome? Management Accounting Research. Sv. 13, č. 3, s. 299-321. doi: 10.1006/mare.2002.0189. Go to original source...
    14. GRIGORESCU, A., BAIASU, D., CHITESCU, R. I., 2020. Business Intelligence, the New Managerial Tool: Opportunities and Limits. Ovidius University Annals, Series Economic Sciences. Sv. 20, č. 1, s. 651-657.
    15. HAENLEIN, M., KAPLAN, A., 2019. A Brief History of Artificial Intelligence: On the Past, Present, and Future of Artificial Intelligence. California Management Review. Sv. 61, č. 4, s. 5-14. doi: 10.1177/0008125619864925. Go to original source...
    16. HART, A., 2014. Automated Budgeting, Forecasting and Business Intelligence in a Manufacturing Environment [online]. [vid. 10. 6. 2021]. Dostupné z: https://grjenkin.com/articles/category/business-intelligence/4235/automated-budgeting-forecasting-and-business-intelligence-in-a-manufacturing-environment.
    17. CHENG, M.-Y., TSAI, H.-C., SUDJONO, E., 2010. Conceptual cost estimates using evolutionary fuzzy hybrid neural network for projects in construction industry. Expert Systems with Applications. Sv. 37, č. 6, s. 4224-4231. doi: 10.1016/j.eswa.2009.11.080. Go to original source...
    18. CHOU, D. C., TRIPURAMALLU, H. B., CHOU, A. Y., 2005. BI and ERP integration. Information Management & Computer Security. Sv. 13, č. 5, s. 340-349. doi: 10.1108/09685220510627241. Go to original source...
    19. IBM, 2021. AI vs. Machine Learning vs. Deep Learning vs. Neural Networks: What's the Difference? [online]. [vid. 7. 6. 2021]. Dostupné z: https://www.ibm.com/cloud/blog/ai-vs-machine-learning-vs-deep-learning-vs-neural-networks.
    20. KAPLAN, A., HAENLEIN, M., 2019. Siri, Siri, in my hand: Who's the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons. Sv. 62, č. 1, s. 15-25. doi: 10.1016/j.bushor.2018. 08.004. Go to original source...
    21. KAPLAN, R. S., NORTON, D. P., 1992. The balanced scorecard-measures that drive performance. Harvard Business Review. Sv. 70, č. 1, s. 71-79.
    22. KARNA, H. A., 2018. Data Is a "Tangible" Asset [online]. [vid. 6. 7. 2021]. Dostupné z: https://www.cfo.com/analytics/2018/05/data-assets-tangible/.
    23. KHATAIE, A. H., BULGAK, A. A., SEGOVIA, J. J., 2011. Activity-Based Costing and Management applied in a hybrid Decision Support System for order management. Decision Support Systems [online]. Sv. 52, č. 1, s. 142-156. doi: 10.1016/j.dss.2011.06.003. Go to original source...
    24. KORHONEN, T., SELOS, E., LAINE, T., SUOMALA, P., 2020. Exploring the programmability of management accounting work for increasing automation: an interventionist case study. Accounting, Auditing & Accountability Journal. Sv. 34, č. 2, s. 253-280. doi: 10.1108/AAAJ-12-2016-2809. Go to original source...
    25. KRÁL, B., 2018. Manažerské účetnictví. Praha: Management Press.
    26. LAURAS, M., MARQUES, G., GOURC, D., 2010. Towards a multi-dimensional project Performance Measurement System. Decision Support Systems. Sv. 48, č. 2, s. 342-353. doi: 10.1016/j.dss.2009.09.002. Go to original source...
    27. LOSBICHLER, H., LEHNER, O. M., 2021. Limits of artificial intelligence in controlling and the ways forward: a call for future accounting research. Journal of Applied Accounting Research. Sv. 22, č. 2, s. 365-382. doi: 10.1108/JAAR-10-2020-0207. Go to original source...
    28. MAROTTA, G., 2021. Opportunities of AI in Budgeting Approaches | DIGITALE WELT | Das Wirtschaftsmagazin zur Digitalisierung [online]. [vid. 10. 6. 2021]. Dostupné z: https://digitaleweltmagazin.de/fachbeitrag/opportunities-of-ai-in-budgeting-approaches/.
    29. MCAFEE, A., BRYNJOLFSSON, E., 2012. Big data: The management revolution. Harvard Business Review. Sv. 90, č. 10, s. 61-68.
    30. PING, W., 2021. Data mining and XBRL integration in management accounting information based on artificial intelligence. Journal of Intelligent and Fuzzy Systems. Sv. 40, č. 4, s. 6755-6766. doi: 10.3233/JIFS-189509. Go to original source...
    31. PRICEWATERHOUSECOOPERS, 2019. 2019 AI Predictions: Six priorities you can't afford to ignore [online]. [vid. 26. 5. 2021]. Dostupné z: https://www.pwc.com/us/en/services/consulting/library/artificial-intelligence-predictions-2019.html.
    32. QIN, J., QIN, Q., 2021. Cloud Platform for Enterprise Financial Budget Management Based on Artificial Intelligence. Wireless Communications and Mobile Computing. Sv. 2021, s. 1-10. doi: 10.1155/2021/8038433. Go to original source...
    33. QUATTRONE, P., 2016. Management accounting goes digital: Will the move make it wiser? Management Accounting Research. Sv. 31, s. 118-122. doi: 10.1016/j.mar.2016.01.003. Go to original source...
    34. SABHERWAL, R., BECERRA-FERNANDEZ, I., 2013. Business intelligence: practices, technologies, and management. Hoboken: John Wiley & Sons.
    35. SALMERON, J. L., FROELICH, W., 2016. Dynamic optimization of fuzzy cognitive maps for time series forecasting. Knowledge-Based Systems. Sv. 105, s. 29-37. doi: 10.1016/j.knosys.2016.04.023. Go to original source...
    36. SUNEJA, N., SHAH, J. P., SHAH, Z. H., HOLIA, M. S., 2021. A neural network approach to design reality oriented cost estimate model for infrastructure projects. Reliability: Theory and Applications. Sv. 16, č. 1(60), s. 254-263.
    37. THOMASNET NEWS, 2019. Big Data, Better Budgeting: Machine Learning for Facilities Management [online]. [vid. 10. 6. 2021]. Dostupné z: https://www.thomas net.com/insights/big-data-better-budgeting-machine-learning-for-facilities-management/.
    38. TSENG, M. M., JIAO, J., MERCHANT, M. E., 1996. Design for Mass Customization. CIRP Annals. Sv. 45, č. 1, s. 153-156. doi: 10.1016/S0007-8506(07)63036-4. Go to original source...
    39. TUNCKAYA, Y., 2017. Performance assessment of permeability index prediction in an ironmaking process via soft computing techniques. Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering. Sv. 231, č. 6, s. 1101-1113. doi: 10.1177/0954408916654199. Go to original source...
    40. WAGNER, J., 2009. Měření výkonnosti: Jak měřit, vyhodnocovat a využívat informace o podnikové výkonnosti. Praha: Grada.
    41. WAGNER, J., FIBÍROVÁ, J., KŘEHNÁČOVÁ, A., 2020. Vývoj manažerského účetnictví v České republice: 1989-2019. Český finanční a účetní časopis. Sv. 2019, č. 4, s. 57-80. doi: 10.18267/j.cfuc.539. Go to original source...
    42. WARREN, J. J. D., MOFFITT, K. C., BYRNES, P., 2015. How big data will change accounting. Accounting Horizons. Sv. 29, č. 2, s. 397-407. doi: 10.2308/acch-51069. Go to original source...
    43. WIXOM, B. H., WATSON, H. J., WERNER, T., 2011. Developing an Enterprise Business Intelligence Capability: The Norfolk Southern Journey. MIS Quarterly Executive. Sv. 10, č. 2, s. 61-71.
    44. WYATT PARTNERS, 2020. How to Value Data as an Asset [online]. [vid. 7. 6. 2021]. Dostupné z: https://wyatt.partners/2020/how-to-value-data/.
    45. XIA, F., YANG, L. T., WANG, L., VINEL, A., 2012. Internet of Things. International Journal of Communication Systems. Sv. 25, č. 9, s. 1101-1102. doi: 10.1002/dac.2417. Go to original source...
    46. XU, Y., LANDON, Y., SEGONDS, S., ZHANG, Y., 2017. A decision support model in mass customization. Computers & Industrial Engineering. Sv. 114, s. 11-21. doi: 10.1016/j.cie.2017.09.046. Go to original source...
    47. ZHANG, X., 2021. Application of data mining and machine learning in management accounting information system. Journal of Applied Science and Engineering. Sv. 24, č. 5, s. 813-820. doi: 10.6180/jase.202110_24(5).0018. Go to original source...

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