AI-Driven Continuous Audit for Smart Manufacturing Finance
Keywords:
Compliance, smart manufacturing, governance, explainability, data privacy, data lineage, risk mitigation, model risk, continuous controls monitoring, anomaly detection, event correlation, supervision, testing, validation, assurance, mapping, pattern design, and validation cycle.Abstract
Smart manufacturing offers an increasingly integrated and data-driven approach to production, benefitting from the latest technologies like Industry 4.0, Internet of Things (IoT), cloud computing, and Big Data. Real-time artificial intelligence (AI)-enabled auditing and compliance can exploit these technologies to manage compliance in real-time. The advantages of continuous auditing compared with batch auditing are obvious. AI supports the automation of many audit and compliance functions. Although companies have adopted artificial intelligence for various operations, the AI model development process requires considerable resources, time, and expertise. Therefore, the AI-supported real-time audit and compliance framework is within smart manufacturing finance. Key definitions are clarified: Real-Time Audit (RTA), Continuous Controls Monitoring (CCM), AI components (models, data preparation), data lineage, and governance terms. A Continuous Auditing and Continuous Controls Monitoring (CACM) model supports more efficient compliance management and verification. Information Technology Controls and General Application Controls aligned with common regulations can now be automated.
Real-time AI-driven event detection, triggered event correlation, preventive controls, and data-driven tests enhance the framework. Control objectives aligned with mother regulations support validation and assurance. Updated AI model governance principles allow support for production without expertise, acquiring test data during the normal course of business. As datasets accumulate, AI can evolve to operate with minimal specification. With present advances in AI and data-and-compute-storage cloud solutions, the necessary compute and storage resources are available to combine operation and audit seamlessly.
References
1. Abdullah, A. A. H., & Almaqtari, F. A. (2024). The impact of artificial intelligence and Industry 4.0 on transforming accounting and auditing practices. Journal of Open Innovation: Technology, Market, and Complexity, 10(1), Article 100218.
2. Dai, J., & Vasarhelyi, M. A. (2023). Management accounting 4.0: The future of management accounting. Journal of Emerging Technologies in Accounting, 20(1), 1–13.
3. Fedyk, A., Hodson, J., Khimich, N., & Fedyk, T. (2022). Is artificial intelligence improving the audit process? Review of Accounting Studies, 27(3), 938–985.
4. Han, H., Shiwakoti, R. K., Jarvis, R., Mordi, C., & Botchie, D. (2023). Accounting and auditing with blockchain technology and artificial intelligence: A literature review. International Journal of Accounting Information Systems, 48, Article 100598.
5. Mangalampalli, B. M. (2024). Transparent Intelligence Explainability Frameworks for AI-Driven Clinical Decision Support in Healthcare Business Intelligence. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(3), 10566-10579.
6. Lehner, O. M., Ittonen, K., Silvola, H., Ström, E., & Wührleitner, A. (2022). Artificial intelligence based decision-making in accounting and auditing: Ethical challenges and normative thinking. Accounting, Auditing & Accountability Journal, 35(9), 109–135.
7. Lehner, O. M., Leitner-Hanetseder, S., Eisl, C., & Knoll, C. (2022). Artificial intelligence-driven accounting (AIDA): Future insights and organisational implications. In O. M. Lehner & C. Knoll (Eds.), Artificial intelligence in accounting: Organisational and ethical implications (pp. 6–34). Routledge.
8. Lehner, O. M., Knoll, C., Leitner-Hanetseder, S., & Eisl, C. (2022). The dynamics of artificial intelligence in accounting organisations: A structuration perspective. In E. Strauss & M. Quinn (Eds.), The Routledge handbook of accounting information systems (2nd ed., pp. 121–139). Routledge.
9. Minkkinen, M., Laine, J., & Mäntymäki, M. (2022). Continuous auditing of artificial intelligence: A conceptualization and assessment of tools and frameworks. Digital Society, 1, Article 21.
10. Pizzi, S., Venturelli, A., Variale, M., & Macario, G. P. (2021). Assessing the impacts of digital transformation on internal auditing: A bibliometric analysis. Technology in Society, 67, Article 101738.
11. Aravind, R., & Surabhi, S. N. R. D. (2024). Smart charging: AI solutions for efficient battery power management in automotive applications. Educational Administration: Theory and Practice, 30(5), 14257-1467.
12. Yoon, K., Liu, Y., Chiu, T., & Vasarhelyi, M. A. (2021). Design and evaluation of an advanced continuous data level auditing system: A three-layer structure. International Journal of Accounting Information Systems, 42, Article 100524.
13. Barros, C., & Marques, R. P. (2022). Continuous assurance for the digital transformation of internal auditing. International Journal of Digital Accounting Research, 22, 1–24.
14. Tharouma, S., & Oudai, M. (2022). A review of the literature on internal audit in the era of digital transformation. Finance and Business Economies Review, 6(4), 215–225.
15. Erişen, O., & Erer, M. (2023). Exploring the impacts of digitalization on the internal audit profession. Journal of Research in Business, 8(1), 1–19.
16. Mani, V. (2023). Auditing and digital transformation are at a crossroads. ISACA Journal, 2023(2), 1–8.
17. Booker, A., Chiu, V., Groff, N., & Richardson, V. J. (2024). AIS research opportunities utilizing machine learning: From a meta-theory of accounting literature. International Journal of Accounting Information Systems, 52, Article 100661.
18. Bavaresco, R. S., Nesi, L. C., Barbosa, J. L. V., Antunes, R. S., Righi, R. da R., da Costa, C. A., Vanzin, M., Dornelles, D., Junior, S. C., Gatti, C., Ferreira, M., & Silva, E. (2023). Machine learning-based automation of accounting services: An exploratory case study. International Journal of Accounting Information Systems, 49, Article 100618.
19. Duan, H. K., Va Yandamuri, U. S. (2024). AI-Driven Decision Support Systems for Operational Optimization in Hospitality Technology. Metallurgical and Materials Engineering. sarhelyi, M. A., Codesso, M., & Alzamil, Z. (2023). Enhancing the government accounting information systems using social media information: An application of text mining and machine learning. International Journal of Accounting Information Systems, 48, Article 100600.
20. Borgi, H., & Alessa, N. (2023). What is the role of artificial intelligence in shaping accounting information systems? A literature review. International Journal of Economic Performance, 6(3), 33–46.
21. Sahoo, S., & Lo, C.-Y. (2022). Smart manufacturing powered by recent technological advancements: A review. Journal of Manufacturing Systems, 64, 236–250.
22. Rajesh, A. S. (2022). Smart manufacturing through machine learning: A review, perspective, and future directions to the machining industry. Journal of Engineering, 2022, Article 9735862.
23. Shojaeinasab, A., Charter, T., Jalayer, M., Khadivi, M., Ogunfowora, O., Raiyani, N., Yaghoubi, M., & Najjaran, H. (2022). Intelligent manufacturing execution systems: A systematic review. Journal of Manufacturing Systems, 62, 503–522.
24. Morgan, J., Halton, M., Qiao, Y., & Breslin, J. G. (2021). Industry 4.0 smart reconfigurable manufacturing machines. Journal of Manufacturing Systems, 59, 481–506.
25. Leng, J., Wang, D., Shen, W., Li, X., Liu, Q., & Chen, X. (2021). Digital twins-based smart manufacturing system design in Industry 4.0: A review. Journal of Manufacturing Systems, 60, 119–137.
26. Yu, Y., Zhang, J. Z., Cao, Y., & Kazancoglu, Y. (2021). Intelligent transformation of the manufacturing industry for Industry 4.0: Seizing financial benefits from supply chain relationship capital through enterprise green management. Technological Forecasting and Social Change, 172, Article 120999.
27. Meindl, B., Ayala, N. F., Mendonça, J., & Frank, A. G. (2021). The four smarts of Industry 4.0: Evolution of ten years of research and future perspectives. Technological Forecasting and Social Change, 168, Article 120784.
Additional Files
Published
Data Availability Statement
None
