# Bank Statement Fraud Detection > Edited balances, inserted salary credits, and fully AI-generated statements are the backbone of income and affordability fraud. TamperCheck runs 130+ forensic checks on every statement - digital PDF, scan, or phone photo - and returns a plain-English verdict in about a minute. **Canonical URL:** https://tampercheck.ai/bank-statement-fraud-detection --- ## Why bank statement fraud gets through A fraudulent applicant rarely fabricates a whole statement. They take a genuine one and change three numbers: the opening balance, a deposit or two, and the closing balance. Done carefully in a free PDF editor, the result is **95% authentic** - which is exactly why visual review fails. The template-matching tools many teams rely on fail for the opposite reason: a tampered statement still matches the bank's template perfectly, because it started life as a real statement from that bank. And the newest wave skips editing entirely: **AI-generated statements** built from a text prompt, with plausible transaction histories and correct branding, that have never touched a bank's systems at all. ## What TamperCheck detects ### Running balance arithmetic Every transaction row's running balance is recomputed against the previous row. A single edited amount breaks the chain - even when the edit is visually perfect. ### Font metrics and substitution Edited figures are usually re-typed in a near-match font. TamperCheck compares character-level font metrics across the document and flags fields that don't match their neighbours. ### Editing tool fingerprints Banks produce statements with a small set of known PDF generators. Traces of Acrobat, LibreOffice, or online PDF editors in the file's metadata and object structure are a strong tampering signal. ### Transaction row insertion artifacts Inserted or deleted rows leave structural residue - inconsistent line spacing, misaligned columns, orphaned formatting objects - that forensic parsing exposes. ### Cross-document consistency When statements are submitted alongside payslips or employment letters, salary credits, employer names, and dates are checked against each other. Fabricated bundles rarely stay consistent. ### AI-generation signatures Fully synthetic statements carry noise patterns, compression artifacts, and layout regularities that differ from genuine bank output. TamperCheck's computer-vision layer detects these signatures. ## How TamperCheck detects bank statement fraud TamperCheck treats every statement as a forensic object, not a set of numbers to be read. Structural analysis, computer vision, and AI adjudication run in parallel, and the verdict comes back with specific findings tied to specific regions of the document. - **Upload via API or dashboard** - single endpoint, single file, verdict in about a minute - **130+ forensic checks** run automatically on every statement - **Works on any bank** - no template library to maintain, institutions covered globally - **Plain-English findings** your credit or ops team can act on directly - **$0.50 per document** - no subscriptions, no minimums - **Zero document storage** - files are processed ephemerally and never retained ## Frequently asked questions ### What is bank statement fraud detection? Bank statement fraud detection is the automated forensic analysis of a submitted statement to determine whether it was genuinely issued by the bank in its current form - catching edited balances, inserted transactions, and fully AI-generated statements before they enter a lending, tenancy, or onboarding decision. ### Can TamperCheck detect statements edited in a PDF editor? Yes - this is the most common attack and the core use case. Edits leave traces in running-balance arithmetic, font metrics, the PDF's internal structure, and its metadata, even when the change is invisible on screen. ### Does it work on scanned statements and phone photos? Yes. Image-based checks (noise analysis, lighting consistency, AI-generation detection) run on scans and photos; PDF-structure checks run on digital files. TamperCheck applies the right check set for each file type automatically. ### How is this different from bank transaction data via open banking? Open banking verifies income when the applicant connects their account - but many workflows still receive statements as documents, and applicants can decline to connect. Document-level fraud detection covers the submissions open banking never sees. ### How do lenders integrate bank statement fraud detection? One REST API call per document: upload the file, receive a JSON verdict with findings in about a minute. Most teams wire it into their loan origination or application workflow in under an hour. There's also a dashboard for manual checks, and $5 in trial credits to evaluate accuracy on your own documents. ## Related use cases - https://tampercheck.ai/use-cases/lending-credit - https://tampercheck.ai/use-cases/tenant-screening - https://tampercheck.ai/use-cases/kyc-identity-verification ## Compare with alternatives - https://tampercheck.ai/compare/ocrolus-vs-tampercheck-ai - https://tampercheck.ai/compare/inscribe-vs-tampercheck-ai --- Try TamperCheck free: https://tampercheck.ai/dashboard · Docs: https://tampercheck.ai/docs · Pricing: https://tampercheck.ai/pricing