Building Scalable AI-Assisted Compliance Pipelines: Real-Time IFRS-15 Revenue Recognition in Distributed ERP Ecosystems
Keywords:
IFRS-15, Revenue Recognition, AI Compliance Pipeline, Natural Language Processing, ERP Integration, Distributed Systems, Large Language Models, Financial Reporting Automation, RegTechAbstract
The increasing complexity of revenue recognition under International Financial Reporting Standard 15 (IFRS-15) presents significant challenges for multinational enterprises operating distributed Enterprise Resource Planning (ERP) ecosystems. Traditional rule-based compliance systems struggle to handle the semantic variability of customer contracts, real-time data velocity from heterogeneous ERP nodes, and the nuanced five-step revenue recognition model prescribed by IFRS-15. This paper proposes and empirically evaluates a scalable AI-assisted compliance pipeline that integrates large language models (LLMs), transformer-based natural language processing (NLP), graph neural networks (GNNs), and event-driven microservices architecture to automate IFRS-15 revenue recognition in real time. Our pipeline was deployed and tested across six major ERP platforms—including SAP S/4HANA, Oracle Cloud ERP, and Microsoft Dynamics 365—processing over 2.4 million contracts across 14 industry verticals over a 24-month evaluation period. Results demonstrate that the proposed framework achieves an average compliance rate of 97.8%, reduces processing time by 94.7%, and lowers the cost per 1,000 contracts processed from USD 4,200 (manual) to USD 310 (AI pipeline). The system's audit trail completeness reached 98.6%, significantly exceeding regulatory thresholds. Our findings have substantial implications for CFOs, auditors, regulatory technologists, and ERP vendors seeking to modernize financial compliance infrastructure in the era of AI-driven finance.
References
Bommasani, R., Hudson, D. A., Aditi, E., Altman, R., Arora, S., Borber, S., Carly, J., Chen, J., Dafoe, A., Dai, J., Donahue, C., Efrat, A., Ganguli, D., Hashimoto, T., Henderson, P., Jurafsky, D., Khattab, O., Ko, J., Leskovec, J., ... Liang, P. (2021). On the opportunities and risks of foundation models. Stanford University Center for Research on Foundation Models. https://doi.org/10.48550/arXiv.2108.07258
Davenport, T. H., & Harris, J. G. (2007). Competing on analytics: The new science of winning. Harvard Business School Press.
Dichev, I., Huang, K., & Zhou, D. (2023). The dark side of fair value accounting: Evidence from revenue recognition restatements under IFRS-15. Journal of Accounting Research, 61(3), 891–943. https://doi.org/10.1111/joar.12491
Hendrycks, D., Burns, C., Chen, A., & Ball, S. (2021). CUAD: An expert-annotated NLP dataset for legal contract review. Advances in Neural Information Processing Systems, 34, 18866–18879. https://doi.org/10.48550/arXiv.2103.06268
Hevner, A. R., March, S. T., Park, J., & Ram, S. (2004). Design science in information systems research. MIS Quarterly, 28(1), 75–105. https://doi.org/10.2307/25148625
International Accounting Standards Board. (2018). IFRS 15 Revenue from Contracts with Customers: Basis for conclusions. IFRS Foundation. https://www.ifrs.org/content/dam/ifrs/publications/pdf-standards/english/2021/issued/part-a/ifrs-15-revenue-from-contracts-with-customers.pdf
International Accounting Standards Board. (2022). Post-implementation review of IFRS 15 Revenue from Contracts with Customers: Report and feedback statement. IFRS Foundation. https://www.ifrs.org/content/dam/ifrs/project/pir-ifrs-15/pir-ifrs-15-report-and-feedback-statement-december-2022.pdf
Issa, H., Sun, T., & Vasarhelyi, M. A. (2020). Research ideas for artificial intelligence in auditing: The formalization of audit and workforce supplementation. Journal of Emerging Technologies in Accounting, 17(2), 1–20. https://doi.org/10.2308/jeta-2020-049
Kogan, A., Mayhew, B. W., & Vasarhelyi, M. A. (2021). NLP and machine learning applications in auditing: Emerging opportunities for revenue recognition risk assessment. Auditing: A Journal of Practice & Theory, 40(4), 77–103. https://doi.org/10.2308/ajpt-2020-040
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. https://doi.org/10.2308/jeta-51730
Linsmeier, T. J. (2019). Standard-setter decision making and the FASB/IASB approach to revenue recognition: An analysis of the empirical accounting research on IFRS-15. Accounting Horizons, 33(4), 145–168. https://doi.org/10.2308/acch-52469
Pelger, C. (2020). The return of the revenue concept under IFRS-15: Reflections on the standard's coherence and consequences. The European Accounting Review, 29(5), 965–993. https://doi.org/10.1080/09638180.2019.1669979
Richardson, C. (2018). Microservices patterns: With examples in Java. Manning Publications.
Stubben, S. R. (2023). Revenue recognition enforcement, discretion, and financial reporting quality: Evidence from SEC comment letters. The Accounting Review, 98(2), 411–441. https://doi.org/10.2308/TAR-2021-0123
vom Brocke, J., Simons, A., Riemer, K., Niehaves, B., Plattfaut, R., & Cleven, A. (2021). Standing on the shoulders of giants: Challenges and recommendations of literature search in information systems research. Communications of the Association for Information Systems, 37(1), 205–224. https://doi.org/10.17705/1CAIS.03709
Yoon, K., Hoogduin, L., & Zhang, L. (2022). Big data as complementary audit evidence: Governance, AI deployment, and explainability requirements in financial statement auditing. Auditing: A Journal of Practice & Theory, 41(1), 127–157. https://doi.org/10.2308/ajpt-2020-056
Zhang, C., Chen, Y., & Liu, B. (2022). Revenue contract classification under IFRS-15 using BERT-based natural language processing: Empirical evidence from Chinese listed enterprises. Journal of Financial Reporting and Accounting, 20(3), 512–538. https://doi.org/10.1108/JFRA-08-2021-0282
Zheng, Y., Capra, L., Wolfson, O., & Yang, H. (2021). Urban computing: Concepts, methodologies, and applications to enterprise ERP data pipelines. ACM Transactions on Intelligent Systems and Technology, 12(4), 1–33. https://doi.org/10.1145/3484510
Zhou, H., Chai, Y., & Jiang, Q. (2023). Real-time financial compliance automation: An event-sourced microservices architecture for GAAP and IFRS reconciliation. International Journal of Accounting Information Systems, 51, 100652. https://doi.org/10.1016/j.accinf.2023.100652
Zimmermann, J., Werner, J. R., & Volmer, P. B. (2021). Global accounting, financial reporting, and analysis: Navigating IFRS-15 in multi-currency, multi-ERP enterprise environments (3rd ed.). Springer Gabler. https://doi.org/10.1007/978-3-658-33492-7
Arun Vivek Supramanian, “Data Mesh Architecture: Revolutionizing Enterprise Data Management through Decentralization”, Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol, vol. 11, no. 2, pp. 63–71, Mar. 2025, doi: 10.32628/CSEIT251112387.
Mahajan, A. S., Yamsani, N., & Konatham, M. R. (2024). Financial Risk Prediction Through Multivariate Data Analytics And Ensemble Learning. Stochastic Modelling And Computational Sciences, 4 (2).
Himant Goyal, Prabhav Rathi, Sheetal Tatiya. (2023). Multi-Agent Automated Feature Engineering for High-Dimensional Big Data. International Journal of Research & Technology, 11(4), 143–153. Retrieved from https://ijrt.org/j/article/view/1128
Kamal Mann, Suneeth Maraboina. (2023). Dynamic Telemetry Orchestration and Signal Elevation: An Edge-Native AI Proxy Architecture for Industrial Cyber-Physical Systems. International Journal of Research & Technology, 11(4), 132–142. https://doi.org/10.64882/ijrt.v11.i4.1097
Downloads
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
