贷款审核员能否识别 AI 篡改的贷款文件？一项实证研究

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Abstract:AI-assisted editing can produce loan documents that preserve familiar issuer layouts while changing the applicant's identity. We investigated whether experienced underwriters and loan processors identify such forgeries in first-pass, document-only review. Three practitioners recruited through Upwork each received one applicant package drawn from 134 forged PDFs derived from 47 genuine financial documents supplied with full consent. The edits preserved amounts, dates and balances; identifier inconsistencies introduced by the pipeline were knowingly retained before distribution. Reviewers were not told that files had been altered and returned 132 file-level judgements. Checking their explanations against the originals yielded 14 edit-supported concerns (10.6%): 12 Suspicious judgements and two Insufficient information judgements. All supported concerns involved identifier disagreements within or across documents, and no reported visual cue identified an actual edit. Thus, approximately one in ten judgements contained evidence of the forgery, despite known inconsistencies remaining in the files. These observations motivate evaluating consistency checks and independent source verification alongside human review. With three reviewer-package pairs and no genuine-file controls, the findings describe this exploratory setting rather than population-wide detection accuracy.

Comments:	6 pages, 2 figures
Subjects:	Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Human-Computer Interaction (cs.HC); Software Engineering (cs.SE)
Cite as:	arXiv:2610.30758 [cs.AI]
	(or arXiv:2610.30758v2 [cs.AI] for this version)
	https://doi.org/10.48550/arXiv.2610.30758 arXiv-issued DOI via DataCite

Submission history

From: Simiao Ren [view email]
[v1] Thu, 22 Oct 2026 05:26:04 UTC (133 KB)
[v2] Sun, 25 Oct 2026 20:07:52 UTC (134 KB)