gbanyan
939a348da4
Paper draft includes all sections (Abstract through Conclusion), 36 references, and supporting scripts. Key methodology: Cosine similarity + dHash dual-method verification with thresholds calibrated against known-replication firm (Firm A). Includes: - 8 section markdown files (paper_a_*.md) - Ablation study script (ResNet-50 vs VGG-16 vs EfficientNet-B0) - Recalibrated classification script (84,386 PDFs, 5-tier system) - Figure generation and Word export scripts - Citation renumbering script ([1]-[36]) - Signature analysis pipeline (12 steps) - YOLO extraction scripts Three rounds of AI review completed (GPT-5.4, Claude Opus 4.6, Gemini 3 Pro). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
1.2 KiB
Impact Statement
Auditor signatures on financial reports are a key safeguard of corporate accountability. When Certified Public Accountants digitally copy and paste a single signature image across multiple reports instead of signing each one individually, this safeguard is undermined---yet detecting such practices through manual inspection is infeasible at the scale of modern financial markets. We developed an artificial intelligence system that automatically extracts and analyzes signatures from over 90,000 audit reports spanning over a decade of filings by publicly listed companies. By combining deep learning-based visual feature analysis with perceptual hashing, the system distinguishes genuinely handwritten signatures from digitally replicated ones. Our analysis reveals substantial variation in signature similarity patterns across accounting firms, with a calibration group independently identified as using digital replication exhibiting distinctly higher similarity scores. After further validation, this technology could serve as an automated screening tool to support financial regulators in monitoring signature authenticity at national scale.