Risk Assessment Using Predictive Analytics
DOI:
https://doi.org/10.26668/businessreview/2023.v8i5.1723Keywords:
Information Technology, Auditing, Risk Assessment, Predictive Analytics, Design ScienceAbstract
Purpose: This research paper uses design science methodology to develop and evaluate a predictive analytics model for audit risk assessment. This research therefore contributes to improving the accuracy and efficiency of audit risk assessment through predictive analytics.
Theoretical framework: This study involved developing and evaluating a predictive analytics model for audit risk assessment, with it being tested during the audit of a publicly listed Saudi company.
Design/methodology/approach: This study adopted the design science research methodology, which is a problem-solving approach that involves the creation of innovative solutions to practical problems. This methodology is particularly relevant for developing and evaluating predictive analytics models for audit risk assessment, because it provides a structured, systematic approach to the problem-solving process. In the context of this research paper, the design science research methodology was used to develop and evaluate a predictive analytics model for audit risk assessment.
Findings: The proposed predictive analytics model for audit risk assessment was found to be an effective tool for helping auditors to make informed decisions based on data analysis. The model accurately identifies high-risk factors associated with an organization, provides valuable insights for decision-making, and highlights areas of potential risk that may require further investigation.
Research, practical & social implications: Future research could explore several areas related to predictive analytics in audit risk assessment. One important area to investigate would be the impact of using predictive analytics on audit quality. The ethical implications of using predictive analytics in audit risk assessment and the potential biases that could affect a model’s accuracy are also important areas to explore.
Originality/value: This paper helps improve our understanding of how predictive analytics can be effectively applied to audit risk assessment and how design science methodology can be used to develop and evaluate predictive analytics models. Furthermore, this study provides insights about the effectiveness of predictive analytics for improving audit risk assessment, thus contributing to the existing literature on the topic.
Downloads
References
Abass, Z. K., Flayyih, H. H., & Hasan, S. I. (2022). The Relationship Between Audit Services and Non-Audit Actuarial Services in the Auditor’s Report. International Journal of Professional Business Review, 7(2), e0455-e0455.
AICPA. (2017). Guide to Audit Data Analytics an Overview. American Institute of Certified Public Accountants. https://www.aicpa.org/resources/article/guide-to-audit-data-analytics-an-overview
Al-Refiay, H. A. N., Abdulhussein, A. S., & Al-Shaikh, S. S. K. (2022). The Impact of Financial Accounting in Decision Making Processes in Business. International Journal of Professional Business Review, 7(4), e0627-e0627.
Alotaibi, E. M. (2023). A Conceptual Model of Continuous Government Auditing Using Blockchain-Based Smart Contracts. International Journal of Business and Management, 17(11), 1-1.
Araz, O. M., Choi, T. M., Olson, D. L., & Salman, F. S. (2020). Role of analytics for operational risk management in the era of big data. Decision Sciences, 51(6), 1320-1346.
Brandtner, P. (2022). Predictive Analytics and Intelligent Decision Support Systems in Supply Chain Risk Management—Research Directions for Future Studies. Proceedings of Seventh International Congress on Information and Communication Technology: ICICT 2022, London, Volume 3,
David, J. S., Gerard, G. J., & McCarthy, W. E. (2002). Design science: building the future of AIS. American Accounting Association, 69.
de Langhe, B., & Puntoni, S. (2021). Leading with decision-driven data analytics. MIT Sloan Management Review, 62(3), 1-4.
Dusenbury, R. B., Reimers, J. L., & Wheeler, S. W. (2000). The audit risk model: An empirical test for conditional dependencies among assessed component risks. Auditing: A Journal of Practice & Theory, 19(2), 105-117.
Geerts, G. L. (2011). A design science research methodology and its application to accounting information systems research. International Journal of Accounting Information Systems, 12(2), 142-151.
Grover, V., Chiang, R. H., Liang, T.-P., & Zhang, D. (2018). Creating strategic business value from big data analytics: A research framework. Journal of management information systems, 35(2), 388-423.
Hevner, A. R., March, S. T., Park, J., & Ram, S. (2008). Design science in information systems research. Management Information Systems Quarterly, 28(1), 6.
Hirt, R., Kühl, N., & Satzger, G. (2019). Cognitive computing for customer profiling: meta classification for gender prediction. Electronic Markets, 29(1), 93-106.
Hogan, C. E., & Wilkins, M. S. (2008). Evidence on the audit risk model: Do auditors increase audit fees in the presence of internal control deficiencies? Contemporary Accounting Research, 25(1), 219-242.
Huang, F., No, W. G., Vasarhelyi, M. A., & Yan, Z. (2022). Audit data analytics, machine learning, and full population testing. The Journal of Finance and Data Science, 8, 138-144.
Kogan, A., Mayhew, B. W., & Vasarhelyi, M. A. (2019). Audit data analytics research—An application of design science methodology. Accounting Horizons, 33(3), 69-73.
McKinney, W. (2011). pandas: a foundational Python library for data analysis and statistics. Python for high performance and scientific computing, 14(9), 1-9.
Messier, W. F., Glover, S. M., & Prawitt, D. F. (2008). Auditing & assurance services: A systematic approach. McGraw-Hill Irwin Boston, MA.
Nasir, M., Simsek, S., Cornelsen, E., Ragothaman, S., & Dag, A. (2021). Developing a decision support system to detect material weaknesses in internal control. Decision Support Systems, 151, 113631.
Paltrinieri, N., Comfort, L., & Reniers, G. (2019). Learning about risk: Machine learning for risk assessment. Safety science, 118, 475-486.
Qi, Q., & Tao, F. (2018). Digital twin and big data towards smart manufacturing and industry 4.0: 360 degree comparison. Ieee Access, 6, 3585-3593.
Samuel, O. W., Asogbon, G. M., Sangaiah, A. K., Fang, P., & Li, G. (2017). An integrated decision support system based on ANN and Fuzzy_AHP for heart failure risk prediction. Expert Systems with Applications, 68, 163-172.
Schlegel, G. L. (2014). Utilizing big data and predictive analytics to manage supply chain risk. The Journal of Business Forecasting, 33(4), 11.
Shmueli, G., & Koppius, O. R. (2011). Predictive analytics in information systems research. MIS quarterly, 553-572.
Søgaard, J. S. (2021). A blockchain-enabled platform for VAT settlement. International Journal of Accounting Information Systems, 40, 100502.
Sun, T. (2018). Deep learning applications in audit decision making Rutgers University-Graduate School-Newark].
Susanto, A., & Meiryani, M. (2019). The impact of environmental accounting information system alignment on firm performance and environmental performance: A case of small and medium enterprises s of Indonesia. International Journal of energy economics and policy, 9(2), 229.
Vasarhelyi, M. A., Kogan, A., & Tuttle, B. M. (2015). Big data in accounting: An overview. Accounting Horizons, 29(2), 381-396.
Wieringa, R. J. (2014). Design science methodology for information systems and software engineering. Springer.
Zhou, J., San, O. T., & Liu, Y. (2023). Design and Implementation of Enterprise Financial Decision Support System Based on Business Intelligence. International Journal of Professional Business Review, 8(4), e0873-e0873.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Eid Mohammed Alotaibi
This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.
Authors who publish in this journal agree to the following terms: the author(s) authorize(s) the publication of the text in the journal;
The author(s) ensure(s) that the contribution is original and unpublished and that it is not in the process of evaluation by another journal;
The journal is not responsible for the views, ideas and concepts presented in articles, and these are the sole responsibility of the author(s);
The publishers reserve the right to make textual adjustments and adapt texts to meet with publication standards.
Authors retain copyright and grant the journal the right to first publication, with the work simultaneously licensed under the Creative Commons Atribuição NãoComercial 4.0 (http://creativecommons.org/licenses/by-nc/4.0/), which allows the work to be shared with recognized authorship and initial publication in this journal.
Authors are allowed to assume additional contracts separately, for non-exclusive distribution of the version of the work published in this journal (e.g. publish in institutional repository or as a book chapter), with recognition of authorship and initial publication in this journal.
Authors are allowed and are encouraged to publish and distribute their work online (e.g. in institutional repositories or on a personal web page) at any point before or during the editorial process, as this can generate positive effects, as well as increase the impact and citations of the published work (see the effect of Free Access) at http://opcit.eprints.org/oacitation-biblio.html
