How to cite this paper
Alotaibi, E., Khallaf, A & Gleason, K. (2024). The role of random forest in internal audit to enhance financial reporting accuracy.International Journal of Data and Network Science, 8(3), 1751-1764.
Refrences
Alles, M. G. (2015). Drivers of the use and facilitators and obstacles of the evolution of big data by the audit profession. Accounting horizons, 29(2), 439-449.
Alles, M. G., Dai, J., & Vasarhelyi, M. A. (2021). Reporting 4.0: Business reporting for the age of mass customization. Journal of Emerging Technologies in Accounting, 18(1), 1-15.
An, B., & Suh, Y. (2020). Identifying financial statement fraud with decision rules obtained from Modified Random For-est. Data Technologies and Applications, 54(2), 235-255.
Barr-Pulliam, D., Brown-Liburd, H. L., & Sanderson, K. A. (2022). The effects of the internal control opinion and use of audit data analytics on perceptions of audit quality, assurance, and auditor negligence. Auditing: A Journal of Practice & Theory, 41(1), 25-48.
Beven, K., & Binley, A. (1992). The future of distributed models: model calibration and uncertainty prediction. Hydrolog-ical processes, 6(3), 279-298.
Breiman, L. (2001). Random forests. Machine learning, 45, 5-32.
Brown-Liburd, H., Issa, H., & Lombardi, D. (2015). Behavioral implications of Big Data's impact on audit judgment and decision making and future research directions. Accounting horizons, 29(2), 451-468.
Brynjolfsson, E., & Mitchell, T. (2017). What can machine learning do? Workforce implications. Science, 358(6370), 1530-1534.
Byrnes, P. E., Al-Awadhi, A., Gullvist, B., Brown-Liburd, H., Teeter, R., Warren Jr, J. D., & Vasarhelyi, M. (2018). Evolu-tion of auditing: From the traditional approach to the future audit. In Continuous auditing: Theory and application (pp. 285-297). Emerald Publishing Limited.
Canhoto, A. I., & Clear, F. (2020). Artificial intelligence and machine learning as business tools: A framework for diag-nosing value destruction potential. Business Horizons, 63(2), 183-193.
Cho, S., Vasarhelyi, M. A., Sun, T., & Zhang, C. (2020). Learning from machine learning in accounting and assurance. Journal of Emerging Technologies in Accounting, 17(1), 1-10.
Cohen, J., Ding, Y., Lesage, C., & Stolowy, H. (2012). Corporate fraud and managers’ behavior: Evidence from the press (pp. 271-315). Springer Netherlands.
Costa, M., Lisboa, I., & Gameiro, A. (2022). Is the Financial Report Quality Important in the Default Prediction? SME Portuguese Construction Sector Evidence. Risks, 10(5), 98.
David, J. S., Gerard, G. J., & McCarthy, W. E. (2002). Design science: building the future of AIS. American Accounting Association, 69.
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., ... & Williams, M. D. (2021). Artificial Intel-ligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994.
Geerts, G. L. (2011). A design science research methodology and its application to accounting information systems re-search. International journal of accounting Information Systems, 12(2), 142-151.
Gregor, S., & Hevner, A. R. (2013). Positioning and presenting design science research for maximum impact. MIS quar-terly, 37(2), 337-355.
Groomer, S. M., & Murthy, U. S. (2018). Continuous Auditing of Database Applications: An Embedded Audit Module Approach1. In Continuous auditing (pp. 105-124). Emerald Publishing Limited.
Healy, P. M., & Palepu, K. G. (2003). The fall of Enron. Journal of economic perspectives, 17(2), 3-26.
Hevner, A. R., March, S. T., Park, J., Ram, S., & Ram, S. (2004). Research essay design science in information. MIS Q, 28(1), 75-105.
Holzinger, A., Kieseberg, P., Weippl, E., & Tjoa, A. M. (2018). Current advances, trends and challenges of machine learn-ing and knowledge extraction: from machine learning to explainable AI. In Machine Learning and Knowledge Extrac-tion: Second IFIP TC 5, TC 8/WG 8.4, 8.9, TC 12/WG 12.9 International Cross-Domain Conference, CD-MAKE 2018, Hamburg, Germany, August 27–30, 2018, Proceedings 2 (pp. 1-8). Springer International Publishing.
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.
Kou, G., Chao, X., Peng, Y., Alsaadi, F. E., & Herrera Viedma, E. (2019). Machine learning methods for systemic risk analysis in financial sectors.
Romney, M., Steinbart, P., Mula, J., McNamara, R., & Tonkin, T. (2012). Accounting Information Systems Australasian Edition. Pearson Higher Education AU.
Roussy, M., Barbe, O., & Raimbault, S. (2020). Internal audit: from effectiveness to organizational significance. Manage-rial Auditing Journal, 35(2), 322-342.
Stoel, D., Havelka, D., & Merhout, J. W. (2012). An analysis of attributes that impact information technology audit quali-ty: A study of IT and financial audit practitioners. International Journal of Accounting Information Systems, 13(1), 60-79.
Studer, S., Bui, T. B., Drescher, C., Hanuschkin, A., Winkler, L., Peters, S., & Müller, K. R. (2021). Towards CRISP-ML (Q): a machine learning process model with quality assurance methodology. Machine learning and knowledge extrac-tion, 3(2), 392-413.
Sun, T. (2019). Applying deep learning to audit procedures: An illustrative framework. Accounting Horizons, 33(3), 89-109.
Turkay, C., Laramee, R., & Holzinger, A. (2017). On the challenges and opportunities in visualization for machine learn-ing and knowledge extraction: A research agenda. In Machine Learning and Knowledge Extraction: First IFIP TC 5, WG 8.4, 8.9, 12.9 International Cross-Domain Conference, CD-MAKE 2017, Reggio, Italy, August 29–September 1, 2017, Proceedings 1 (pp. 191-198). Springer International Publishing.
Vasarhelyi, M. A., Halper, F. B., & Ezawa, K. J. (1991). The continuous process audit system: A UNIX-based auditing tool. The EDP Auditor Journal, 3(3), 85-91.
Vasarhelyi, M. A., Kogan, A., & Tuttle, B. M. (2015). Big data in accounting: An overview. Accounting Horizons, 29(2), 381-396.
Alles, M. G., Dai, J., & Vasarhelyi, M. A. (2021). Reporting 4.0: Business reporting for the age of mass customization. Journal of Emerging Technologies in Accounting, 18(1), 1-15.
An, B., & Suh, Y. (2020). Identifying financial statement fraud with decision rules obtained from Modified Random For-est. Data Technologies and Applications, 54(2), 235-255.
Barr-Pulliam, D., Brown-Liburd, H. L., & Sanderson, K. A. (2022). The effects of the internal control opinion and use of audit data analytics on perceptions of audit quality, assurance, and auditor negligence. Auditing: A Journal of Practice & Theory, 41(1), 25-48.
Beven, K., & Binley, A. (1992). The future of distributed models: model calibration and uncertainty prediction. Hydrolog-ical processes, 6(3), 279-298.
Breiman, L. (2001). Random forests. Machine learning, 45, 5-32.
Brown-Liburd, H., Issa, H., & Lombardi, D. (2015). Behavioral implications of Big Data's impact on audit judgment and decision making and future research directions. Accounting horizons, 29(2), 451-468.
Brynjolfsson, E., & Mitchell, T. (2017). What can machine learning do? Workforce implications. Science, 358(6370), 1530-1534.
Byrnes, P. E., Al-Awadhi, A., Gullvist, B., Brown-Liburd, H., Teeter, R., Warren Jr, J. D., & Vasarhelyi, M. (2018). Evolu-tion of auditing: From the traditional approach to the future audit. In Continuous auditing: Theory and application (pp. 285-297). Emerald Publishing Limited.
Canhoto, A. I., & Clear, F. (2020). Artificial intelligence and machine learning as business tools: A framework for diag-nosing value destruction potential. Business Horizons, 63(2), 183-193.
Cho, S., Vasarhelyi, M. A., Sun, T., & Zhang, C. (2020). Learning from machine learning in accounting and assurance. Journal of Emerging Technologies in Accounting, 17(1), 1-10.
Cohen, J., Ding, Y., Lesage, C., & Stolowy, H. (2012). Corporate fraud and managers’ behavior: Evidence from the press (pp. 271-315). Springer Netherlands.
Costa, M., Lisboa, I., & Gameiro, A. (2022). Is the Financial Report Quality Important in the Default Prediction? SME Portuguese Construction Sector Evidence. Risks, 10(5), 98.
David, J. S., Gerard, G. J., & McCarthy, W. E. (2002). Design science: building the future of AIS. American Accounting Association, 69.
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., ... & Williams, M. D. (2021). Artificial Intel-ligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994.
Geerts, G. L. (2011). A design science research methodology and its application to accounting information systems re-search. International journal of accounting Information Systems, 12(2), 142-151.
Gregor, S., & Hevner, A. R. (2013). Positioning and presenting design science research for maximum impact. MIS quar-terly, 37(2), 337-355.
Groomer, S. M., & Murthy, U. S. (2018). Continuous Auditing of Database Applications: An Embedded Audit Module Approach1. In Continuous auditing (pp. 105-124). Emerald Publishing Limited.
Healy, P. M., & Palepu, K. G. (2003). The fall of Enron. Journal of economic perspectives, 17(2), 3-26.
Hevner, A. R., March, S. T., Park, J., Ram, S., & Ram, S. (2004). Research essay design science in information. MIS Q, 28(1), 75-105.
Holzinger, A., Kieseberg, P., Weippl, E., & Tjoa, A. M. (2018). Current advances, trends and challenges of machine learn-ing and knowledge extraction: from machine learning to explainable AI. In Machine Learning and Knowledge Extrac-tion: Second IFIP TC 5, TC 8/WG 8.4, 8.9, TC 12/WG 12.9 International Cross-Domain Conference, CD-MAKE 2018, Hamburg, Germany, August 27–30, 2018, Proceedings 2 (pp. 1-8). Springer International Publishing.
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.
Kou, G., Chao, X., Peng, Y., Alsaadi, F. E., & Herrera Viedma, E. (2019). Machine learning methods for systemic risk analysis in financial sectors.
Romney, M., Steinbart, P., Mula, J., McNamara, R., & Tonkin, T. (2012). Accounting Information Systems Australasian Edition. Pearson Higher Education AU.
Roussy, M., Barbe, O., & Raimbault, S. (2020). Internal audit: from effectiveness to organizational significance. Manage-rial Auditing Journal, 35(2), 322-342.
Stoel, D., Havelka, D., & Merhout, J. W. (2012). An analysis of attributes that impact information technology audit quali-ty: A study of IT and financial audit practitioners. International Journal of Accounting Information Systems, 13(1), 60-79.
Studer, S., Bui, T. B., Drescher, C., Hanuschkin, A., Winkler, L., Peters, S., & Müller, K. R. (2021). Towards CRISP-ML (Q): a machine learning process model with quality assurance methodology. Machine learning and knowledge extrac-tion, 3(2), 392-413.
Sun, T. (2019). Applying deep learning to audit procedures: An illustrative framework. Accounting Horizons, 33(3), 89-109.
Turkay, C., Laramee, R., & Holzinger, A. (2017). On the challenges and opportunities in visualization for machine learn-ing and knowledge extraction: A research agenda. In Machine Learning and Knowledge Extraction: First IFIP TC 5, WG 8.4, 8.9, 12.9 International Cross-Domain Conference, CD-MAKE 2017, Reggio, Italy, August 29–September 1, 2017, Proceedings 1 (pp. 191-198). Springer International Publishing.
Vasarhelyi, M. A., Halper, F. B., & Ezawa, K. J. (1991). The continuous process audit system: A UNIX-based auditing tool. The EDP Auditor Journal, 3(3), 85-91.
Vasarhelyi, M. A., Kogan, A., & Tuttle, B. M. (2015). Big data in accounting: An overview. Accounting Horizons, 29(2), 381-396.