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

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.