Artificial Intelligence, Auditing Industry, Bangladesh, Professional Accountants, Transparency, Big data, Technology.
Askary, S., Abu-Ghazaleh, N., & Tahat, Y. (2018). Artificial intelligence and reliability of accounting information. Paper presented at the 17th IFIP WG 6.11 Conference on e-Business, e-Services, and e-Society, I3E 2018, Kuwait City, Kuwait, October 30 – November 1, 2018, Proceedings. 10.1007/978-3-030-02131-3_28.
Ata, H. A., & Seyrek, I. H. (2009). The use of data mining techniques in detecting fraudulent financial statements: An application on manufacturing firms. Suleyman Demirel University Journal of Faculty of Economics & Administrative Sciences, 14(2), 157-170.
Baldwin, A. A., Brown, C. E., & Trinkle, B. S. (2006). Opportunities for artificial intelligence development in the accounting domain: The case for auditing. Intelligent Systems in Accounting, Finance & Management: International Journal, 14(3), 77-86.Available at: https://doi.org/10.1002/isaf.277.
Dungan, C. W., & Chandler, J. S. (1985). Auditor: A microcomputer-based expert system to support auditors in the field. Expert Systems, 2(4), 210-221.Available at: https://doi.org/10.1111/j.1468-0394.1985.tb00474.x.
Flowerday, S., Blundell, A., & Von Solms, R. (2006). Continuous auditing technologies and models: A discussion. Computers & Security, 25(5), 325-331.Available at: https://doi.org/10.1016/j.cose.2006.06.004.
Hunton, J. E., & Rose, J. M. (2010). 21st century auditing: Advancing decision support systems to achieve continuous auditing. Accounting Horizons, 24(2), 297–312.Available at: https://doi.org/10.2308/acch.2010.24.2.297.
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.Available at: https://doi.org/10.2308/jeta-10511.
Kanatov, M., Atymtayeva, L., & Yagaliyeva, B. (2014). Expert systems for information security management and audit. Implementation phase issues. Paper presented at the 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems (SCIS) and 15th International Symposium on Advanced Intelligent Systems (ISIS), Kitakyushu, 2014.
Lee, Y.-C., Hsiao, Y.-C., Peng, C.-F., Tsai, S.-B., Wu, C.-H., & Chen, Q. (2015). Using mahalanobis–taguchi system, logistic regression, and neural network method to evaluate purchasing audit quality. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 229(1_suppl), 3-12.
Malhotra, Y., & Galletta, D. F. (1999). Extending the technology acceptance model to account for social influence: Theoretical bases and empirical validation. Paper presented at the In Proceedings of the 32nd Annual Hawaii International Conference on Systems Sciences. 1999. HICSS-32. Abstracts and CD-ROM of Full Papers. IEEE.
Omoteso, K. (2012). The application of artificial intelligence in auditing: Looking back to the future. Expert Systems with Applications, 39(9), 8490-8495.Available at: https://doi.org/10.1016/j.eswa.2012.01.098.
Saleiro, P., Kuester, B., Hinkson, L., London, J., Stevens, A., Anisfeld, A., & Ghani, R. (2018). Aequitas: A bias and fairness audit toolkit (pp. 4-14). Chicago, IL 60637, USA: Center for Data Science and Public Policy University of Chicago.
Shi, G., Ma, Z., Feng, J., Zhu, F., Bai, X., & Gui, B. (2020). The impact of knowledge transfer performance on the artificial intelligence industry innovation network: An empirical study of Chinese firms. PLoS ONE, 15(5), e0232658.Available at: https://doi.org/10.1371/journal.pone.0232658.