Financial accounting and auditing procedures have undergone a revolutionary transformation due to the rapid advancement of technology and innovation, presenting significant opportunities to enhance efficiency, accuracy, and transparency. In this dynamic landscape, machine learning (ML), data analytics, and artificial intelligence (AI) have emerged as indispensable tools. These technological advancements empower financial professionals to swiftly deliver precise financial information, enhance decision-making, and expedite operations. Utilizing AI and ML techniques is paramount in improving accounting and auditing procedures. By automating laborious processes such as data input and reconciliation, errors are reduced, and productivity is heightened. AI and ML systems possess the capability to swiftly and reliably identify trends, anomalies, and potential fraud by analyzing vast volumes of financial data. By harnessing real-time monitoring and predictive analytics capabilities, financial professionals can effectively manage risks and make well-informed decisions. Furthermore, incorporating AI and ML in auditing elevates the quality of audits by providing profound insights, detecting non-compliance, and strengthening fraud detection mechanisms. This engenders trust in the accounting profession and fosters more accurate financial reporting. The present study offers a comprehensive review of the literature on the utilization of AI and ML in accounting and auditing. The findings encompass the cost-reducing effect of AI-based supplier audits, the superior predictive capacity of nonlinear models in forecasting stock volatility compared to linear models, and the enhanced efficiency of accounting and auditing through the integration of AI and ML. Moreover, AI augments the auditing process and automates workflows, while ML facilitates projections of stock performance and facilitates fraud identification.
Kroon, N., M. d. C. Alves, and I. Martins. (2021). The Impacts of Emerging Technologies on Accountants' Role and Skills: Connecting to Open Innovation-A Systematic Literature Review. Journal of Open Innovation: Technology, Market, and Complexity, 7(3), 163. https://doi.org/10.3390/joitmc7030163
Imoniana, J. O., et al. (2023). Impact of technological advancements on auditing of financial statements.
Ahmad, A., et al. (2024). Assessment of effects in advances of accounting technologies on quality financial reports in Jordanian public sector. Uncertain Supply Chain Management, 12(1), 133-142. https://doi.org/10.5267/j.uscm.2023.10.011
Schmitt, M. (2020). Artificial intelligence in business analytics, capturing value with machine learning applications in financial services.
Nguyen, D. K., G. Sermpinis, and C. Stasinakis. (2023). Big data, artificial intelligence and machine learning: A transformative symbiosis in favour of financial technology. European Financial Management, 29(2), 517-548. https://doi.org/10.1111/eufm.12365
Al-amri, R., et al. (2021). A Review of Machine Learning and Deep Learning Techniques for Anomaly Detection in IoT Data. Applied Sciences, 11(12), 5320. https://doi.org/10.3390/app11125320
Nassif, A. B., et al. (2021). Machine Learning for Anomaly Detection: A Systematic Review. IEEE Access, 9, 78658-78700. https://doi.org/10.1109/ACCESS.2021.3083060
Milojević, N. and S. Redzepagic. (2021). Prospects of artificial intelligence and machine learning application in banking risk management. Journal of Central Banking Theory and Practice, 10(3), 41-57. https://doi.org/10.2478/jcbtp-2021-0023
Mhlanga, D. (2021). Financial Inclusion in Emerging Economies: The Application of Machine Learning and Artificial Intelligence in Credit Risk Assessment. International Journal of Financial Studies, 9(3), 39. https://doi.org/10.3390/ijfs9030039
Dzuranin, A. C. and I. Mălăescu. (2016). The current state and future direction of IT audit: Challenges and opportunities. Journal of Information Systems, 30(1), 7-20. https://doi.org/10.2308/isys-51315
Thakker, P. and G. Japee. (2023). Emerging technologies in accountancy and finance: A comprehensive review. European Economic Letters (EEL), 13(3), 993-1011.
Druzhynina, V., et al. (2020). Innovative technology in terms of socio-economic value diffusion: Accounting and analytical support. European Journal of Sustainable Development, 9(3), 476-476. https://doi.org/10.14207/ejsd.2020.v9n3p476
Mancini, D., R. Lombardi, and M. Tavana. (2021). Four research pathways for understanding the role of smart technologies in accounting. Meditari Accountancy Research, 29(5), 1041-1062. https://doi.org/10.1108/MEDAR-03-2021-1258
Ibrahim, A. E. A., A. A. Elamer, and A. N. Ezat. (2021). The convergence of big data and accounting: innovative research opportunities. Technological Forecasting and Social Change, 173, 121171. https://doi.org/10.1016/j.techfore.2021.121171
Taipaleenmäki, J. and S. Ikäheimo. (2013). On the convergence of management accounting and financial accounting - the role of information technology in accounting change. International Journal of Accounting Information Systems, 14(4), 321-348. https://doi.org/10.1016/j.accinf.2013.09.003
Munoko, I., H. L. Brown-Liburd, and M. Vasarhelyi. (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
Hasan, A. R. (2021). Artificial Intelligence (AI) in accounting & auditing: A Literature review. Open Journal of Business and Management, 10(1), 440-465. https://doi.org/10.4236/ojbm.2022.101026
Maleki, S. and Y. Z. Mehrjerdi. (2022). Diagnosis of coronary artery disease by bat and Harris hawk meta-heuristic optimization algorithms and machine learning methods. https://doi.org/10.52547/jha.25.1.57
Sahbafar, H., et al. (2023). Prepared Plasmonic Glass Substrates via Electrodeposition for Detecting Trace Glucose: SERS, DFT, and FDTD Investigations. Plasmonics. https://doi.org/10.1007/s11468-023-02126-y
Basiri, R., M. R. Popovic, and S. S. Khan. (2022). Domain-Specific Deep Learning Feature Extractor for Diabetic Foot Ulcer Detection. In 2022 IEEE International Conference on Data Mining Workshops (ICDMW). https://doi.org/10.1109/ICDMW58026.2022.00041
Maleki, S., et al. (2022). Predicting coronary artery diseases using effective features selected by Harris Hawks optimization algorithm and support vector machine. Journal of Industrial and Systems Engineering, 14(Special issue: 18th International Industrial Engineering Conference), 40-47.
Sahbafar, H., et al. (2023). Surface-Enhanced Raman Scattering (SERS) and Finite Difference Time Domain (FDTD) Investigations of Plasmonic and Flexible Filter Papers for the Detection of the Molecular Vibrations of Amoxicillin. Plasmonics. https://doi.org/10.1007/s11468-023-02106-2
Basiri, R., et al. (2024). Protocol for metadata and image collection at diabetic foot ulcer clinics: enabling research in wound analytics and deep learning. BioMedical Engineering OnLine, 23(1), 12. https://doi.org/10.1186/s12938-024-01210-6
Noroozi, R., et al. (2023). 3D and 4D Bioprinting Technologies: A Game Changer for the Biomedical Sector? Annals of Biomedical Engineering, 51(8), 1683-1712. https://doi.org/10.1007/s10439-023-03243-9
Dehghan Rouzi, M., et al. (2023). Breast Cancer Detection with an Ensemble of Deep Learning Networks Using a Consensus-Adaptive Weighting Method. Journal of Imaging, 9(11), 247. https://doi.org/10.3390/jimaging9110247
Bagheri, A.B., et al. (2023). Potential applications of artificial intelligence and machine learning on diagnosis, treatment, and outcome prediction to address health care disparities of chronic limb-threatening ischemia. Seminars in Vascular Surgery, 36(3), 454-459. https://doi.org/10.1053/j.semvascsurg.2023.06.003
Park, C., et al. (2023). Machine Learning-Based Aggression Detection in Children with ADHD Using Sensor-Based Physical Activity Monitoring. Sensors, 23(10), 4949. https://doi.org/10.3390/s23104949
Mohsennezhad, A., et al. (2024). Simply Developed Surface-Enhanced Raman Scattering (SERS) Sensors for Ultra-Sensitive Detection of Lindane Pesticide. Plasmonics. https://doi.org/10.1007/s11468-024-02254-z
Hariri, R.H., Fredericks, E.M., & Bowers, K.M. (2019). Uncertainty in big data analytics: survey, opportunities, and challenges. Journal of Big Data, 6(1), 44. https://doi.org/10.1186/s40537-019-0206-3
Sohangir, S., et al. (2018). Big Data: Deep Learning for financial sentiment analysis. Journal of Big Data, 5(1), 3. https://doi.org/10.1186/s40537-017-0111-6
Goodell, J.W., et al. (2021). Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis. Journal of Behavioral and Experimental Finance, 32, 100577. https://doi.org/10.1016/j.jbef.2021.100577
Ahmed, S., et al. (2022). Artificial intelligence and machine learning in finance: A bibliometric review. Research in International Business and Finance, 61, 101646. https://doi.org/10.1016/j.ribaf.2022.101646
Kaul, V., Enslin, S., & Gross, S.A. (2020). History of artificial intelligence in medicine. Gastrointestinal Endoscopy, 92(4), 807-812. https://doi.org/10.1016/j.gie.2020.06.040
Chowdhary, K. (2020). Fundamentals of artificial intelligence. Springer. https://doi.org/10.1007/978-81-322-3972-7
Ahmed, I., Jeon, G., & Piccialli, F. (2022). From artificial intelligence to explainable artificial intelligence in industry 4.0: a survey on what, how, and where. IEEE Transactions on Industrial Informatics, 18(8), 5031-5042. https://doi.org/10.1109/TII.2022.3146552
Hassan, M., Aziz, L.A.-R., & Andriansyah, Y. (2023). The role artificial intelligence in modern banking: an exploration of AI-driven approaches for enhanced fraud prevention, risk management, and regulatory compliance. Reviews of Contemporary Business Analytics, 6(1), 110-132.
Ahmadi, S. (2023). Open AI and its Impact on Fraud Detection in Financial Industry. Journal of Knowledge Learning and Science Technology ISSN, 2959-6386. https://doi.org/10.31219/osf.io/myg6r
Cho, S., et al. (2020). Learning from machine learning in accounting and assurance (pp. 1-10). American Accounting Association. https://doi.org/10.2308/jeta-10718
Calderon, T.G., & Cheh, J.J. (2002). A roadmap for future neural networks research in auditing and risk assessment. International Journal of Accounting Information Systems, 3(4), 203-236. https://doi.org/10.1016/S1467-0895(02)00068-4
Soori, M., Arezoo, B., & Dastres, R. (2023). Artificial intelligence, machine learning and deep learning in advanced robotics, a review. Cognitive Robotics, 3, 54-70. https://doi.org/10.1016/j.cogr.2023.04.001
Aziz, S., & Dowling, M. (2019). Machine learning and AI for risk management. Springer International Publishing. https://doi.org/10.1007/978-3-030-02330-0_3
Aljohani, A. (2023). Predictive Analytics and Machine Learning for Real-Time Supply Chain Risk Mitigation and Agility. Sustainability, 15(20), 15088. https://doi.org/10.3390/su152015088
Tien, J.M. (2017). Internet of Things, Real-Time Decision Making, and Artificial Intelligence. Annals of Data Science, 4(2), 149-178. https://doi.org/10.1007/s40745-017-0112-5
Albawwat, I., & Frijat, Y. (2021). An analysis of auditors' perceptions towards artificial intelligence and its contribution to audit quality. Accounting, 7(4), 755-762. https://doi.org/10.5267/j.ac.2021.2.009
Al-Shabandar, R., et al. (2019). The application of artificial intelligence in financial compliance management. In Proceedings of the 2019 International Conference on Artificial Intelligence and Advanced Manufacturing. https://doi.org/10.1145/3358331.3358339
Baghdasaryan, V., et al. (2022). Improving Tax Audit Efficiency Using Machine Learning: The Role of Taxpayer's Network Data in Fraud Detection. Applied Artificial Intelligence, 36(1), 2012002. https://doi.org/10.1080/08839514.2021.2012002
Wang, J., et al. (2024). Supply chain diffusion mechanisms for AI applications: A perspective on audit pricing. International Review of Financial Analysis, 93, 103113. https://doi.org/10.1016/j.irfa.2024.103113
Thottoli, M.M. (2024). Leveraging information communication technology (ICT) and artificial intelligence (AI) to enhance auditing practices. Accounting Research Journal, ahead-of-print(ahead-of-print). https://doi.org/10.1108/ARJ-09-2023-0269
Zhen, X., & Zhen, L. (2023). Accounting Information Systems and Strategic Performance: The Interplay of Digital Technology and Edge Computing Devices. Journal of Grid Computing, 22(1), 5. https://doi.org/10.1007/s10723-023-09720-8
Zhang, C., et al. (2023). Ethical impact of artificial intelligence in managerial accounting. International Journal of Accounting Information Systems, 49, 100619. https://doi.org/10.1016/j.accinf.2023.100619
Fülöp, M., et al. (2023). Ethical concerns associated with artificial intelligence in the accounting profession: a curse or a blessing? Journal of Business Economics and Management, 24, 387-404. https://doi.org/10.3846/jbem.2023.19251
Shiyyab, F. S., et al. (2023). The Impact of Artificial Intelligence Disclosure on Financial Performance. International Journal of Financial Studies, 11, https://doi.org/10.3390/ijfs11030115
Shankar, A., et al. (2024). Exploring enablers and inhibitors of AI-enabled drones for manufacturing process audits: A mixed-method approach. Business Strategy and the Environment, n/a(n/a). https://doi.org/10.1002/bse.3679
Rodgers, W., Al-Shaikh, S., & Khalil, M. (2024). Protocol Analysis Data Collection Technique Implemented for Artificial Intelligence Design. IEEE Transactions on Engineering Management, 71, 6842-6853. https://doi.org/10.1109/TEM.2023.3269291
Qasim, A., El Refae, G. A., & Eletter, S. (2023). A Proposed Model to Integrate Drone Technology in Accounting for Long Term Contracts: A Cash Flow Management Perspictive. https://doi.org/10.34028/iajit/20/3A/5
Marques, S., et al. (2023). The Impact of Intelligent Systems on Management Accounting. International Journal of Intelligent Information Technologies (IJIIT), 19(1), 1-32. https://doi.org/10.4018/IJIIT.324601
Lambert, K. J., & Fegley, T. (2023). Economic Calculation in Light of Advances in Big Data and Artificial Intelligence. Journal of Economic Behavior & Organization, 206, 243-250. https://doi.org/10.1016/j.jebo.2022.12.009
Hu, K.-H., et al. (2023). Governance of artificial intelligence applications in a business audit via a fusion fuzzy multiple rule-based decision-making model. Financial Innovation, 9(1), 117. https://doi.org/10.1186/s40854-022-00436-4
Goto, M. (2023). Anticipatory innovation of professional services: The case of auditing and artificial intelligence. Research Policy, 52(8), 104828. https://doi.org/10.1016/j.respol.2023.104828
Ramzan, S., & Lokanan, M. (2024). The application of machine learning to study fraud in the accounting literature. Journal of Accounting Literature, ahead-of-print(ahead-of-print). https://doi.org/10.1108/JAL-11-2022-0112
Liao, C., & Ma, T. (2024). From fundamental signals to stock volatility: A machine learning approach. Pacific-Basin Finance Journal, 84, 102283. https://doi.org/10.1016/j.pacfin.2024.102283
Yang, X. (2024). Optimizing Accounting Informatization through Simultaneous Multi-Tasking across Edge and Cloud Devices using Hybrid Machine Learning Models. Journal of Grid Computing, 22(1), 12. https://doi.org/10.1007/s10723-023-09735-1
Subedi, M. (2024). Principles based accounting standards, audit fees and going concern: evidence using advanced machine learning. International Journal of Accounting & Information Management, 32(2), 308-344. https://doi.org/10.1108/IJAIM-02-2023-0026
Rahman, M. J., & Zhu, H. (2024). Detecting accounting fraud in family firms: Evidence from machine learning approaches. Advances in Accounting, 64, 100722. https://doi.org/10.1016/j.adiac.2023.100722
Bochkay, K., et al. (2023). Textual Analysis in Accounting: What's Next?*. Contemporary Accounting Research, 40(2), 765-805. https://doi.org/10.1111/1911-3846.12825
Zhao, J., & Wang, X. (2023). Unleashing efficiency and insights: Exploring the potential applications and challenges of ChatGPT in accounting. Journal of Corporate Accounting & Finance, 35. https://doi.org/10.1002/jcaf.22663
Wood, D., et al. (2023). The ChatGPT Artificial Intelligence Chatbot: How Well Does It Answer Accounting Assessment Questions? Issues in Accounting Education, 38, 1-28.
Bavaresco, R. S., et al. (2023). Machine learning-based automation of accounting services: An exploratory case study. International Journal of Accounting Information Systems, 49, 100618. https://doi.org/10.1016/j.accinf.2023.100618
Shapovalova, A., et al. (2023). MODERNIZATION OF THE NATIONAL ACCOUNTING AND AUDITING SYSTEM USING DIGITAL TRANSFORMATION TOOLS. Financial & Credit Activity: Problems of Theory & Practice, 4(51).
Zhu, D. (2022). A Fuzzy Comprehensive Evaluation and Random Forest Model for Financial Account Audit Early Warning. Mobile Information Systems, 2022, 5425618. https://doi.org/10.1155/2022/5425618
Zheng, M. (2022). Advanced Artificial Intelligence Model for Financial Accounting Transformation Based on Machine Learning and Enterprise Unstructured Text Data. Mobile Information Systems, 2022, 5708652. https://doi.org/10.1155/2022/5708652
Vărzaru, A. A. (2022). Assessing Artificial Intelligence Technology Acceptance in Managerial Accounting. Electronics, 11. https://doi.org/10.3390/electronics11142256
Vărzaru, A. A. (2022). Assessing the Impact of AI Solutions' Ethical Issues on Performance in Managerial Accounting. Electronics, 11. https://doi.org/10.3390/electronics11142221
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
Mousa, G. A., Elamir, E. A. H., & Hussainey, K. (2022). The effect of annual report narratives on the cost of capital in the Middle East and North Africa: A machine learning approach. Research in International Business and Finance, 62, 101675. https://doi.org/10.1016/j.ribaf.2022.101675
Liu, X. (2022). Accounting and Financial Management Cost Accounting Integrating Rough Set Knowledge Recognition Algorithm. Discrete Dynamics in Nature and Society, 2022, 9286252. https://doi.org/10.1155/2022/9286252
Lehner, O. M., et al. (2022). Artificial intelligence based decision-making in accounting and auditing: ethical challenges and normative thinking. Accounting, Auditing & Accountability Journal, 35(9), 109-135. https://doi.org/10.1108/AAAJ-09-2020-4934
Krupa, J., & Minutti-Meza, M. (2022). Regression and Machine Learning Methods to Predict Discrete Outcomes in Accounting Research. Journal of Financial Reporting, 7. https://doi.org/10.2308/JFR-2021-010
Hanauer, M. X., Kononova, M., & Rapp, M. S. (2022). Boosting agnostic fundamental analysis: Using machine learning to identify mispricing in European stock markets. Finance Research Letters, 48, 102856. https://doi.org/10.1016/j.frl.2022.102856
Chen, X. I., et al. (2022). Predicting Future Earnings Changes Using Machine Learning and Detailed Financial Data. Journal of Accounting Research, 60(2), 467-515. https://doi.org/10.1111/1475-679X.12429
Agustí, M. A., & Orta-Pérez, M. (2023). Big data and artificial intelligence in the fields of accounting and auditing: a bibliometric analysis. Spanish Journal of Finance and Accounting / Revista Española de Financiación y Contabilidad, 52(3), 412-438. https://doi.org/10.1080/02102412.2022.2099675
Shahbazbeigi,A. (2024). Application of Artificial Intelligence and Machine Learning in Accounting and Auditing. Transactions on Data Analysis in Social Science, 6(2), 92-108. doi: 10.47176/TDASS.2024.92
MLA
Shahbazbeigi,A. . "Application of Artificial Intelligence and Machine Learning in Accounting and Auditing", Transactions on Data Analysis in Social Science, 6, 2, 2024, 92-108. doi: 10.47176/TDASS.2024.92
HARVARD
Shahbazbeigi A. (2024). 'Application of Artificial Intelligence and Machine Learning in Accounting and Auditing', Transactions on Data Analysis in Social Science, 6(2), pp. 92-108. doi: 10.47176/TDASS.2024.92
CHICAGO
A. Shahbazbeigi, "Application of Artificial Intelligence and Machine Learning in Accounting and Auditing," Transactions on Data Analysis in Social Science, 6 2 (2024): 92-108, doi: 10.47176/TDASS.2024.92
VANCOUVER
Shahbazbeigi A. Application of Artificial Intelligence and Machine Learning in Accounting and Auditing. TDASS, 2024; 6(2): 92-108. doi: 10.47176/TDASS.2024.92
