Predictive Accounting and Managerial Intelligence for Business Decisions Under Uncertainty
DOI:
https://doi.org/10.53485/rgn.v9i3.764Keywords:
predictive accounting, managerial intelligence, data analytics, decision-making, business management.Abstract
Objective: to analyze the contributions, applications, limitations, and perspectives of predictive accounting integrated into managerial intelligence systems to strengthen business decision-making under uncertainty. Methodology: a qualitative, documentary, and bibliographic study was conducted with a descriptive, analytical, and reflective scope, a non-experimental design, and an integrative review of scientific and institutional literature published between 2016 and 2026. The search considered academic databases and specialized organizations related to accounting, analytics, artificial intelligence, risk management, and business administration. Results: the reviewed evidence shows agreement regarding the capacity of advanced analytics to expand the informational value of historical records, anticipate liquidity pressures, estimate sales and costs, identify anomalies, and compare scenarios. Differences were also identified concerning the scope of automation, model explainability, data governance, and professional responsibility for biases, errors, or structural changes. Conclusions: the integration of predictive accounting and managerial intelligence improves organizational preparedness when data are consistent, assumptions remain visible, and results are subjected to validation, human oversight, and continuous feedback. This approach does not replace accounting professionals; instead, it broadens their participation in interpretation, risk communication, and strategic advice, while requiring technological competencies, ethical judgment, and control mechanisms capable of preserving traceability, transparency, and accountability in decision-making. The findings also indicate that predictive outputs should be communicated as revisable probability ranges rather than certainties, since their usefulness depends on organizational context, model maintenance, and the capacity to correct deviations before they affect material decisions. Accordingly, implementation should begin with limited projects, relevant indicators, and formal review protocols before wider organizational adoption.
References
Appelbaum, D., Kogan, A., Vasarhelyi, M. A., & Yan, Z. (2017). Impact of business analytics and enterprise systems on managerial accounting. International Journal of Accounting Information Systems, 25, 29-44. https://doi.org/10.1016/j.accinf.2017.03.003
Ballou, B., Heitger, D. L., & Stoel, D. (2018). Data-driven decision-making and its impact on accounting undergraduate curriculum. Journal of Accounting Education, 44, 14-24. https://doi.org/10.1016/j.jaccedu.2018.05.003
Bergmann, M., Brück, C., Knauer, T., & Schwering, A. (2020). Digitization of the budgeting process: Determinants of the use of business analytics and its effect on satisfaction with the budgeting process. Journal of Management Control, 31, 25-54. https://doi.org/10.1007/s00187-019-00291-y
IFAC. (2017). Blockchain, AI and accounting. International Federation of Accountants. https://www.ifac.org/knowledge-gateway/discussion/blockchain-ai-and-accounting
IFAC. (2023). AI and intelligent automation: Disrupting business, elevating the work of accounting and finance professionals. International Federation of Accountants. https://www.ifac.org/knowledge-gateway/discussion/ai-intelligent-automation-disrupting-business-elevating-work-accounting-finance-professionals
IFAC. (2024a). Exploring the responsible use of AI in finance and accounting. International Federation of Accountants. https://www.ifac.org/knowledge-gateway/discussion/harnessing-innovation-exploring-responsible-use-ai-finance-and-accounting
IFAC. (2024b). Navigating the Gen AI revolution: Implications for the accounting profession. International Federation of Accountants. https://www.ifac.org/knowledge-gateway/discussion/navigating-gen-ai-revolution-implications-accounting-profession
IFRS Foundation. (2023). IFRS S1 General requirements for disclosure of sustainability-related financial information. https://www.ifrs.org/issued-standards/ifrs-sustainability-standards-navigator/ifrs-s1-general-requirements/
IFRS Foundation. (2024a). Digital financial reporting: Facilitating digital comparability and analysis of financial reports. https://www.ifrs.org/content/dam/ifrs/standards/taxonomy/digital-financial-reporting/digitalreportingarticle-april2024.pdf
IFRS Foundation. (2024b). Using the IFRS digital taxonomies: A guide for regulators implementing the IFRS digital taxonomies in a digital filing system. https://www.ifrs.org/content/dam/ifrs/standards/taxonomy/general-resources/ifrs-taxonomy-regulators-guide.pdf
Issa, H., Sun, T., & Vasarhelyi, M. A. (2016). Research ideas for artificial intelligence in auditing: The formalization of audit and workforce supplementation. Journal of Emerging Technologies in Accounting, 13(2), 1-20. https://doi.org/10.2308/jeta-10511
Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61(4), 577-586. https://doi.org/10.1016/j.bushor.2018.03.007
Knudsen, D. R. (2020). Elusive boundaries, power relations, and knowledge production: A systematic review of the literature on digitalization in accounting. International Journal of Accounting Information Systems, 36, 100441. https://doi.org/10.1016/j.accinf.2019.100441
Kokina, J., & Davenport, T. H. (2017). The emergence of artificial intelligence: How automation is changing auditing. Journal of Emerging Technologies in Accounting, 14(1), 115-122. https://doi.org/10.2308/jeta-51730
Kurpiela, S., & Teuteberg, F. (2024). Linking business analytics affordances to corporate strategic planning and decision making outcomes. Information Systems and e-Business Management, 22(1), 33-60. https://doi.org/10.1007/s10257-023-00661-z
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, 22(2), 365-382. https://doi.org/10.1108/JAAR-10-2020-0207
Moll, J., & Yigitbasioglu, O. (2019). The role of internet-related technologies in shaping the work of accountants: New directions for accounting research. The British Accounting Review, 51(6), 100833. https://doi.org/10.1016/j.bar.2019.04.002
Munoko, I., Brown-Liburd, H. L., & Vasarhelyi, M. A. (2020). The ethical implications of using artificial intelligence in auditing. Journal of Business Ethics, 167(2), 209-234. https://doi.org/10.1007/s10551-019-04407-1
Nielsen, S. (2022). Management accounting and the concepts of exploratory data analysis and unsupervised machine learning: A literature study and future directions. Journal of Accounting & Organizational Change, 18(5), 811-853. https://doi.org/10.1108/JAOC-08-2020-0107
NIST. (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0). National Institute of Standards and Technology. https://doi.org/10.6028/NIST.AI.100-1
OECD. (2019a). Artificial intelligence in society. OECD Publishing. https://doi.org/10.1787/eedfee77-en
OECD. (2019b). Recommendation of the Council on Artificial Intelligence. https://legalinstruments.oecd.org/en/instruments/OECD-LEGAL-0449
Oesterreich, T. D., & Teuteberg, F. (2019). The role of business analytics in the controllers and management accountants' competence profiles: An exploratory study on individual-level data. Journal of Accounting & Organizational Change, 15(2), 330-356. https://doi.org/10.1108/JAOC-10-2018-0097
Oesterreich, T. D., Teuteberg, F., Bensberg, F., & Buscher, G. (2019). The controlling profession in the digital age: Understanding the impact of digitisation on the controller's job roles, skills and competences. International Journal of Accounting Information Systems, 35, 100432. https://doi.org/10.1016/j.accinf.2019.100432
Pisoni, G., Molnár, B., & Tarcsi, Á. (2024). Knowledge management and data analysis techniques for data-driven financial companies. Journal of the Knowledge Economy, 15(3), 13374-13393. https://doi.org/10.1007/s13132-023-01607-z
Quattrone, P. (2016). Management accounting goes digital: Will the move make it wiser? Management Accounting Research, 31, 118-122. https://doi.org/10.1016/j.mar.2016.01.003
Richins, G., Stapleton, A., Stratopoulos, T. C., & Wong, C. (2017). Big data analytics: Opportunity or threat for the accounting profession? Journal of Information Systems, 31(3), 63-79. https://doi.org/10.2308/isys-51805
Rikhardsson, P., & Yigitbasioglu, O. (2018). Business intelligence and analytics in management accounting research: Status and future focus. International Journal of Accounting Information Systems, 29, 37-58. https://doi.org/10.1016/j.accinf.2018.03.001
Schnegg, M., & Möller, K. (2022). Strategies for data analytics projects in business performance forecasting: A field study. Journal of Management Control, 33, 241-271. https://doi.org/10.1007/s00187-022-00338-7
Shrestha, Y. R., Ben-Menahem, S. M., & von Krogh, G. (2019). Organizational decision-making structures in the age of artificial intelligence. California Management Review, 61(4), 66-83. https://doi.org/10.1177/0008125619862257
Szukits, Á. (2022). The illusion of data-driven decision making: The mediating effect of digital orientation and controllers' added value in explaining organizational implications of advanced analytics. Journal of Management Control, 33, 403-446. https://doi.org/10.1007/s00187-022-00343-w
Wasserbacher, H., & Spindler, M. (2022). Machine learning for financial forecasting, planning and analysis: Recent developments and pitfalls. Digital Finance, 4(1), 63-88. https://doi.org/10.1007/s42521-021-00046-2
World Bank. (2021). World Development Report 2021: Data for better lives. World Bank. https://www.worldbank.org/en/publication/wdr2021
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