Detecting Disclosure Discrepancies in SEC Filings: A Deep Learning Approach for Regulatory Compliance Verification

Article Sidebar

PDF
Published: 2026-01-18
Keywords:
financial disclosure, SEC filings, deep learning, XBRL validation, regulatory compliance

Main Article Content

Dun Liang
Business Analytics, Fordham University, NY, USA

Abstract

The accuracy of financial disclosures filed with the Securities and Exchange Commission (SEC) remains fundamental to maintaining market integrity and investor confidence. This research presents a comprehensive deep learning approach for automated detection of disclosure discrepancies in SEC filings, specifically targeting 10-K and 10-Q annual reports and XBRL-tagged financial statements. Our methodology employs a hybrid architecture combining deep learning classification models with rule-based validation frameworks. The core innovation lies in a Transformer-based discrepancy classifier that processes cross-period text alignments to distinguish substantive changes from routine modifications, achieving 94.3% accuracy on 3,200 expert-labeled disclosure pairs. This classifier integrates with XBRL validation rule engines and intelligent accounting standards checklists to identify numerical contradictions, formatting irregularities, and narrative inconsistencies across 10-K annual reports, 10-Q quarterly reports, and XBRL-tagged financial statements. Experimental validation using 2,847 SEC filings from publicly traded companies demonstrates detection accuracy of 94.3% for cross-period discrepancies and 91.7% for XBRL tagging errors, significantly outperforming traditional rule-based validation tools. The practical implementation reduces manual review time by 67% while maintaining high precision in identifying material misstatements requiring correction before filing.

Article Details

Issue

Vol. 2 No. 1 (2026)

Section

Articles

How to Cite

Detecting Disclosure Discrepancies in SEC Filings: A Deep Learning Approach for Regulatory Compliance Verification. (2026). Journal of Sustainability, Policy, and Practice, 2(1), 101-114. https://schoalrx.com/index.php/jspp/article/view/80

References

1. Y. Bao, B. Ke, B. Li, Y. J. Yu, and J. Zhang, "Detecting accounting fraud in publicly traded US firms using a machine learning approach," Journal of Accounting Research, vol. 58, no. 1, pp. 199-235, 2020.

2. A. Fedyk, J. Hodson, N. Khimich, and T. Fedyk, "Is artificial intelligence improving the audit process," Review of Accounting Studies, vol. 27, no. 3, pp. 938-985, 2022.

3. M. N. Ashtiani, and B. Raahemi, "Intelligent fraud detection in financial statements using machine learning and data mining: A systematic literature review," IEEE Access, vol. 10, pp. 72504-72525, 2021. doi: 10.1109/access.2021.3096799

4. A. H. Huang, H. Wang, and Y. Yang, "FinBERT: A large language model for extracting information from financial text," Contemporary Accounting Research, vol. 40, no. 2, pp. 806-841, 2023.

5. I. C. Chiu, and M. W. Hung, "Finance-specific large language models: Advancing sentiment analysis and return prediction with LLaMA 2," Pacific-Basin Finance Journal, vol. 90, p. 102632, 2025.

6. S. Ravula, "Text analysis in financial disclosures," arXiv preprint arXiv:2101.04480, 2021.

7. P. Craja, A. Kim, and S. Lessmann, "Deep learning for detecting financial statement fraud," Decision Support Systems, vol. 139, p. 113421, 2020. doi: 10.1016/j.dss.2020.113421

8. Y. Chen, C. Zhao, Y. Xu, C. Nie, and Y. Zhang, "Deep learning in financial fraud detection: Innovations, challenges, and applications," Data Science and Management, 2025. doi: 10.1016/j.dsm.2025.08.002

9. L. Hernandez Aros, L. X. Bustamante Molano, F. Gutierrez-Portela, J. J. Moreno Hernandez, and M. S. Rodríguez Barrero, "Financial fraud detection through the application of machine learning techniques: A literature review," Humanities and Social Sciences Communications, vol. 11, no. 1, pp. 1-22, 2024. doi: 10.1057/s41599-024-03606-0

10. R. Wang, "Standardizing XBRL financial reporting tags with natural language processing," 2023. doi: 10.2139/ssrn.4613085

11. Y. Zhang, T. Du, Y. Sun, L. Donohue, and R. Dai, "Form 10-Q itemization," In Proceedings of the 30th ACM International Conference on Information & Knowledge Management, October, 2021, pp. 4817-4822. doi: 10.1145/3459637.3481989

12. K. Mishra, H. Pagare, and K. Sharma, "A hybrid rule-based NLP and machine learning approach for PII detection and anonymization in financial documents," Scientific Reports, vol. 15, no. 1, p. 22729, 2025. doi: 10.1038/s41598-025-04971-9

13. C. Wang, M. Wang, X. Wang, L. Zhang, and Y. Long, "Multi-relational graph representation learning for financial statement fraud detection," Big Data Mining and Analytics, vol. 7, no. 3, pp. 920-941, 2024. doi: 10.26599/bdma.2024.9020013

14. R. Ding, "An enterprise intelligent audit model by using a deep learning approach," Computational Economics, vol. 59, no. 4, pp. 1335-1354, 2022.

15. C. L. Jan, "Detection of financial statement fraud using deep learning for sustainable development of capital markets under information asymmetry," Sustainability, vol. 13, no. 17, p. 9879, 2021.