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ARCHIVED. Not part of the IEEE Access submission. The block below is wrapped
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signatures from reproduced ones" overstates what a five-way confidence
classifier without a fully labeled test set establishes; soften before
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# Impact Statement (archived; not in IEEE Access submission)

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 a pipeline that automatically extracts and analyzes signatures from over 90,000 audit reports spanning a decade of filings by publicly listed companies in Taiwan.
Combining deep-learning visual features with perceptual hashing, distributional diagnostics, and anchor-based inter-CPA coincidence-rate calibration, the system stratifies signatures into a five-way confidence-graded classification and quantifies how the practice varies across firms and over time.
With a future labelled evaluation set, the technology could support financial regulators in screening candidate non-hand-signed signatures at national scale.
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