Simiao Ren, Xingyu Shen, Tommy Duong, Ankit Raj · 2026 · arXiv (Cornell University)
DOI: 10.48550/arxiv.2610.30758 · cited by 0 works (OpenAlex) · open-access PDF
AI 보조 편집은 지원자의 정체성을 변경하는 동안 친숙한 발행자 레이아웃을 보존하는 대출 문서를 생산할 수 있습니다. 우리는 경험이 풍부한 계약자와 대출 프로세서가 첫 통과, 문서만 검토에서 그러한 가짜를 식별하는지 여부를 조사했습니다. Upwork를 통해 채용 된 3 명의 실무자는 각각 134 개의 가짜 금융 문서에서 추출 된 1 개의 신청자 패키지를받지 못했습니다. 편집은 금액, 날짜 및 균형을 보존했습니다. 파이프라인에 의해 도입 된 식별 불일치가 배포 전에 의식적으로 보존되었습니다. 리뷰는 파일이 변경되었으며 132 개의 파일 수준 판단을 반환하지 않았습니다. 원본에 대한 그들의 설명을 검토하면 14 개의 편집 지원
Machine translation (Meta M2M100); the English original follows.
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.
Read in: English · Spanish · Chinese · Hindi · Arabic · Portuguese · French · German · Japanese · Russian · Korean · Indonesian · Vietnamese
Topics: Underwriting, Loan, Identifier, Consistency (knowledge bases), Issuer, Actuarial science
GET https://store.agentexchange.work/gw/papers/verify?title=...&year=... (0.003 USD), fetch this record as JSON with /gw/papers/paper?id=2610.30758, or list what it cites with /gw/papers/references?id=.... Pay per call in USDC (x402) or with a 19 USD card key for 1,000 calls. Catalogue · llms.txtSources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange