gbanyan
9b11f03548
Major changes from v2: Terminology: - "digitally replicated" -> "non-hand-signed" throughout (per partner v3 feedback and to avoid implicit accusation) - "Firm A near-universal non-hand-signing" -> "replication-dominated" (per interview nuance: most but not all Firm A partners use replication) Target journal: IEEE TAI -> IEEE Access (per NCKU CSIE list) New methodological sections (III.G-III.L + IV.D-IV.G): - Three convergent threshold methods (KDE antimode + Hartigan dip test / Burgstahler-Dichev McCrary / EM-fitted Beta mixture + logit-GMM robustness check) - Explicit unit-of-analysis discussion (signature vs accountant) - Accountant-level 2D Gaussian mixture (BIC-best K=3 found empirically) - Pixel-identity validation anchor (no manual annotation needed) - Low-similarity negative anchor + Firm A replication-dominated anchor New empirical findings integrated: - Firm A signature cosine UNIMODAL (dip p=0.17) - long left tail = minority hand-signers - Full-sample cosine MULTIMODAL but not cleanly bimodal (BIC prefers 3-comp mixture) - signature-level is continuous quality spectrum - Accountant-level mixture trimodal (C1 Deloitte-heavy 139/141, C2 other Big-4, C3 smaller firms). 2-comp crossings cos=0.945, dh=8.10 - Pixel-identity anchor (310 pairs) gives perfect recall at all cosine thresholds - Firm A anchor rates: cos>0.95=92.5%, dual-rule cos>0.95 AND dh<=8=89.95% New discussion section V.B: "Continuous-quality spectrum vs discrete- behavior regimes" - the core interpretive contribution of v3. References added: Hartigan & Hartigan 1985, Burgstahler & Dichev 1997, McCrary 2008, Dempster-Laird-Rubin 1977, White 1982 (refs 37-41). export_v3.py builds Paper_A_IEEE_Access_Draft_v3.docx (462 KB, +40% vs v2 from expanded methodology + results sections). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
1.1 KiB
Impact Statement
Auditor signatures on financial reports are a key safeguard of corporate accountability. When the signature on an audit report is produced by reproducing a stored image instead of by the partner's own hand---whether through an administrative stamping workflow or a firm-level electronic signing system---this safeguard is weakened, yet detecting the practice 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 a decade of filings by publicly listed companies in Taiwan. By combining deep-learning visual features with perceptual hashing and three statistically independent threshold-selection methods, the system distinguishes genuinely hand-signed signatures from reproduced ones and quantifies how this practice varies across firms and over time. After further validation, the technology could support financial regulators in automating signature-authenticity screening at national scale.