A Scalable Enterprise Framework for AI-Driven Invoice Processing Using Document Intelligence
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V6I4P121Keywords:
Document AI, IDP-Intelligent Document Processing, Invoice Automation, OCR-Optical Character Recognition, NLP-Natural Language Processing, ML-Machine Learning, Human-in-the-Loop, ERP Integration, Enterprise SystemsAbstract
Invoice processing remains a critical yet labor-intensive back-office function in many organizations, even those that have adopted modern ERP platforms. Manual validation of heterogeneous invoices introduces delays, errors, and scalability constraints, particularly during volume spikes such as quarter-end or year-end. This paper presents a scalable, enterprise-grade framework for automating invoice processing using Document AI (DocAI). The proposed architecture combines optical character recognition (OCR), natural language processing (NLP), and machine learning (ML) with a confidence-driven, human-in-the-loop workflow and robust ERP integration. Unlike many prior works that focus primarily on algorithms or small, static datasets, this work reports on the design, deployment, and evaluation of a production system implemented in a live finance environment. The framework supports multi-format ingestion, modular AI components, and elastic scaling to handle monthly invoices without service degradation. In the evaluated deployment, the system reduced end-to-end processing time by improving field-level extraction accuracy and decreasing the human review rate after iterative retraining. The paper details the system architecture, design decisions, implementation methodology, and operational results, and concludes with lessons learned and future directions for extending document intelligence across related financial processes
References
1. K. Chen, D. Liu, and Y. Wang, “An overview of optical character recognition,” Pattern Recognit. Lett., vol. 26, no. 1, pp. 3–12, 2005.
2. D. Jurafsky and J. H. Martin, Speech and Language Processing, 3rd ed. Pearson, 2023.
3. S. J. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 4th ed. Pearson, 2020.
4. M. Jain, A. Goel, and A. Bajaj, “AI-driven invoice processing using deep learning and NLP,” IEEE Access, vol. 9, pp. 125432–125445, 2021.
5. Y. Xu, M. Li, L. Cui, S. Huang, F. Wei, and M. Zhou, “LayoutLM: Pre-training of text and layout for document image understanding,” in Proc. ACM SIGKDD Int. Conf. Knowl. Discov. Data Min. (KDD), 2020, pp. 1192–1200.
6. A. Madaan, P. Arora, and V. Goel, “A review on intelligent document processing,” Int. J. Adv. Comput. Sci. Appl. (IJACSA), vol. 13, no. 5, pp. 1–9, 2022.
7. L. Cui, Y. Xu, T. Lv, and F. Wei, “Document AI: Benchmarks, models and applications,” arXiv preprint, 2021.
Y. Xu, M. Li, L. Cui, and F. Wei, “LayoutLMv2: Multi-modal pre-training for visually-rich document understanding,” arXiv preprint, 2020.
