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ABSTRACT. This article reviews and advances existing literature concerning artificial intelligence applications optimizing quality, efficiency, transparency, and interpretability in the accounting and auditing tasks. In this research, previous findings were cumulated showing that cognitive modeling technology and deep learning of neural networks are decisive in improving accounting statements and combating corporate tax evasion by collecting and processing data, and I contribute to the literature by indicating that artificial intelligence applications further financial and managerial accounting and accounting information systems in relation to automation-based repetitive tasks and updated skills. Throughout July 2022, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “accounting and auditing” + “machine learning-based decision-making systems,” “cloud computing and blockchain technologies,” and “big data analytics algorithms.” As research published between 2020 and 2022 was inspected, only 193 articles satisfied the eligibility criteria. By taking out controversial or ambiguous findings (insufficient/irrelevant data), outcomes unsubstantiated by replication, too general material, or studies with nearly identical titles, I selected 54 mainly empirical sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AXIS, MMAT, ROBIS, and SRDR.
JEL codes: E24; J21; J54; J64

How to cite: Ionescu, L. (2022). “Machine Learning-based Decision-Making Systems, Cloud Computing and Blockchain Technologies, and Big Data Analytics Algorithms in Accounting and Auditing,” Economics, Management, and Financial Markets 17(4): 9–26. doi: 10.22381/emfm17420221.

Received 19 August 2022 • Received in revised form 21 December 2022
Accepted 28 December 2022 • Available online 30 December 2022

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