Business and Social Sciences
Artificial Intelligence Enabled Auditing for Real Time Financial Reporting Enhancing Precision and Regulatory Compliance
Muslima Jahan Diba1*, Md. Rezaul Haque2, Mitu Akter3
Business and Social Sciences 2 (1) 1-8 https://doi.org/10.25163/business.2110492
Submitted: 07 July 2024 Revised: 07 September 2024 Accepted: 13 September 2024 Published: 15 September 2024
Abstract
Background: Artificial intelligence (AI) has taken over auditing operations through its ability to perform instant financial reporting and its capacity to produce exact results while maintaining compliance with U.S. financial institution regulations. Traditional auditing systems face problems with slow error detection and complex regulatory compliance requirements and manual limitations.
Methods: The study involved 240 finance professionals and auditors who worked at different financial institutions throughout the United States to analyze how AI auditing tools affect their performance. The survey examined three main areas which include reporting accuracy improvements and operational efficiency and regulatory compliance. The analytical methods used for fraud detection included anomaly detection and predictive analytics and regression-based risk modeling and clustering techniques.
Results: The data shows that 78% of participants experienced better reporting precision and 82% of them saw improved compliance monitoring effectiveness after they started using AI systems. The processing time for audits dropped by 27% because real-time anomaly detection enabled organizations to speed up their response to unexpected events. The findings from the statistical assessments indicated Precision=0.85 and Recall=0.87 and F1-score=0.86 and Accuracy=89% and p=0.05 which proves that AI-enabled auditing provides better performance measures and operational outcomes.
Conclusion: AI-enabled auditing provides a new age approach to financial reporting in providing accurate data delivery, as well as improving regulatory compliance and speed in conducting audit activities.
Keywords: Financial Reporting, Artificial Intelligence, Real-Time Auditing, Regulatory Compliance
References
Alghanmi, N., Alotaibi, R., & Buhari, S. M. (2021). Machine Learning Approaches for Anomaly Detection in IoT: An Overview and future research directions. Wireless Personal Communications, 122(3), 2309–2324. https://doi.org/10.1007/s11277-021-08994-z
Alotaibi, G., Awawdeh, M., Farook, F. F., Aljohani, M., Aldhafiri, R. M., & Aldhoayan, M. (2022). Artificial intelligence (AI) diagnostic tools: utilizing a convolutional neural network (CNN) to assess periodontal bone level radiographically—a retrospective study. BMC Oral Health, 22(1). https://doi.org/10.1186/s12903-022-02436-3
Ambika, P. (2019). Machine learning and deep learning algorithms on the Industrial Internet of Things (IIoT). In Advances in computers (pp. 321–338). https://doi.org/10.1016/bs.adcom.2019.10.007
Ashta, A., & Herrmann, H. (2021). Artificial intelligence and fintech: An overview of opportunities and risks for banking, investments, and microfinance. Strategic Change, 30(3), 211–222. https://doi.org/10.1002/jsc.2404
Bibi, R., Saeed, Y., Zeb, A., Ghazal, T. M., Rahman, T., Said, R. A., Abbas, S., Ahmad, M., & Khan, M. A. (2021). EdgE AI-Based automated detection and classification of road anomalies in VANET using deep Learning. Computational Intelligence and Neuroscience, 2021(1). https://doi.org/10.1155/2021/6262194
Dauvergne, P. (2020). Is artificial intelligence greening global supply chains? Exposing the political economy of environmental costs. Review of International Political Economy, 29(3), 696–718. https://doi.org/10.1080/09692290.2020.1814381
De Gheselle, S., Jacques, C., Chambost, J., Blank, C., Declerck, K., De Croo, I., Hickman, C., & Tilleman, K. (2022). Machine learning for prediction of euploidy in human embryos: in search of the best-performing model and predictive features. Fertility and Sterility, 117(4), 738–746. https://doi.org/10.1016/j.fertnstert.2021.11.029
Dogo, E. M., Nwulu, N. I., Twala, B., & Aigbavboa, C. (2019). A survey of machine learning methods applied to anomaly detection on drinking-water quality data. Urban Water Journal, 16(3), 235–248. https://doi.org/10.1080/1573062x.2019.1637002
Falco, G., Shneiderman, B., Badger, J., Carrier, R., Dahbura, A., Danks, D., Eling, M., Goodloe, A., Gupta, J., Hart, C., Jirotka, M., Johnson, H., LaPointe, C., Llorens, A. J., Mackworth, A. K., Maple, C., Pálsson, S. E., Pasquale, F., Winfield, A., & Yeong, Z. K. (2021). Governing AI safety through independent audits. Nature Machine Intelligence, 3(7), 566–571. https://doi.org/10.1038/s42256-021-00370-7
Fedyk, A., Hodson, J., Khimich, N., & Fedyk, T. (2022). Is artificial intelligence improving the audit process? Review of Accounting Studies, 27(3), 938–985. https://doi.org/10.1007/s11142-022-09697-x
Guo, H., & Polak, P. (2021). Artificial Intelligence and Financial Technology FinTech: How AI is being used under the pandemic in 2020. In Studies in computational intelligence (pp. 169–186). https://doi.org/10.1007/978-3-030-62796-6_9
Hasan, M., Islam, M. M., Zarif, M. I. I., & Hashem, M. (2019). Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches. Internet of Things, 7, 100059. https://doi.org/10.1016/j.iot.2019.100059
Hicks, S. A., Strümke, I., Thambawita, V., Hammou, M., Riegler, M. A., Halvorsen, P., & Parasa, S. (2022). On evaluation metrics for medical applications of artificial intelligence. Scientific Reports, 12(1). https://doi.org/10.1038/s41598-022-09954-8
Killawi, A., Khidir, A., Elnashar, M., Abdelrahim, H., Hammoud, M., Elliott, H., Thurston, M., Asad, H., Al-Khal, A. L., & Fetters, M. D. (2014). Procedures of recruiting, obtaining informed consent, and compensating research participants in Qatar: findings from a qualitative investigation. BMC Medical Ethics, 15(1). https://doi.org/10.1186/1472-6939-15-9
Lee, J. (2020). Access to finance for artificial intelligence regulation in the financial services industry. European Business Organization Law Review, 21(4), 731–757. https://doi.org/10.1007/s40804-020-00200-0
Liu, J., Chang, H., Forrest, J. Y., & Yang, B. (2020). Influence of artificial intelligence on technological innovation: Evidence from the panel data of china’s manufacturing sectors. Technological Forecasting and Social Change, 158, 120142. https://doi.org/10.1016/j.techfore.2020.120142
Lui, A., & Lamb, G. W. (2018). Artificial intelligence and augmented intelligence collaboration: regaining trust and confidence in the financial sector. Information & Communications Technology Law, 27(3), 267–283. https://doi.org/10.1080/13600834.2018.1488659
Mahalakshmi, V., Kulkarni, N., Kumar, K. P., Kumar, K. S., Sree, D. N., & Durga, S. (2021). The Role of implementing Artificial Intelligence and Machine Learning Technologies in the financial services Industry for creating Competitive Intelligence. Materials Today Proceedings, 56, 2252–2255. https://doi.org/10.1016/j.matpr.2021.11.577
McEnroe, J. E., & Sullivan, M. (2013). An examination of the perceptions of auditors and chief financial officers regarding principles versus rules based accounting standards. Research in Accounting Regulation, 25(2), 196–207. https://doi.org/10.1016/j.racreg.2013.08.008
Molyneux, S., Mulupi, S., Mbaabu, L., & Marsh, V. (2012). Benefits and payments for research participants: Experiences and views from a research centre on the Kenyan coast. BMC Medical Ethics, 13(1). https://doi.org/10.1186/1472-6939-13-13
Owoc, M. L., Sawicka, A., & Weichbroth, P. (2021). Artificial intelligence Technologies in Education: Benefits, challenges and Strategies of implementation. In IFIP advances in information and communication technology (pp. 37–58). https://doi.org/10.1007/978-3-030-85001-2_4
Pachauri, G., & Sharma, S. (2015). Anomaly Detection in Medical Wireless Sensor Networks using Machine Learning Algorithms. Procedia Computer Science, 70, 325–333. https://doi.org/10.1016/j.procs.2015.10.026
Pathan, M. S., Nag, A., Pathan, M. M., & Dev, S. (2022). Analyzing the impact of feature selection on the accuracy of heart disease prediction. Healthcare Analytics, 2, 100060. https://doi.org/10.1016/j.health.2022.100060
Poornima, I. G. A., & Paramasivan, B. (2020). Anomaly detection in wireless sensor network using machine learning algorithm. Computer Communications, 151, 331–337. https://doi.org/10.1016/j.comcom.2020.01.005
Qin, S. J., & Chiang, L. H. (2019). Advances and opportunities in machine learning for process data analytics. Computers & Chemical Engineering, 126, 465–473. https://doi.org/10.1016/j.compchemeng.2019.04.003
Rodríguez-Espíndola, O., Chowdhury, S., Beltagui, A., & Albores, P. (2020). The potential of emergent disruptive technologies for humanitarian supply chains: the integration of blockchain, Artificial Intelligence and 3D printing. International Journal of Production Research, 58(15), 4610–4630. https://doi.org/10.1080/00207543.2020.1761565
Rousopoulou, V., Vafeiadis, T., Nizamis, A., Iakovidis, I., Samaras, L., Kirtsoglou, A., Georgiadis, K., Ioannidis, D., & Tzovaras, D. (2021). Cognitive analytics platform with AI solutions for anomaly detection. Computers in Industry, 134, 103555. https://doi.org/10.1016/j.compind.2021.103555
Schepman, A., & Rodway, P. (2020). Initial validation of the general attitudes towards Artificial Intelligence Scale. Computers in Human Behavior Reports, 1, 100014. https://doi.org/10.1016/j.chbr.2020.100014
Shanmuganathan, M. (2020). Behavioural finance in an era of artificial intelligence: Longitudinal case study of robo-advisors in investment decisions. Journal of Behavioral and Experimental Finance, 27, 100297. https://doi.org/10.1016/j.jbef.2020.100297
Umer, M., & Razi, S. (2018). Analyzing research methodologies and publication trends in service marketing literature. Cogent Business & Management, 5(1), 1446265. https://doi.org/10.1080/23311975.2018.1446265