# Bank Statement Fraud Detection > Edited balances, inserted salary credits, and fully AI-generated statements are the backbone of income and affordability fraud. TamperCheck analyses every statement across structural, visual, and metadata layers - digital PDF, scan, or phone photo - and returns a verdict with the specific evidence your team can act on. **Canonical URL:** https://tampercheck.ai/bank-statement-fraud-detection **Last updated:** 2026-10-05 --- ## 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. ## Every statement treated as a forensic object, not a set of numbers to read Every statement gets the same treatment: structural analysis, computer vision, AI adjudication in parallel. Verdict comes back with findings tied to specific regions of the document. - **Upload via API or dashboard** - one endpoint, one file, verdict fast - **200+ forensic checks** run automatically on every statement - **Works on any bank** - no template library to maintain, institutions covered globally - **Findings your team can act on directly** - not just a risk score - **$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? 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? This is 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? Image-based checks (noise analysis, lighting consistency, AI-generation detection) run on scans and photos; PDF-structure checks run on digital files. The right check set is applied 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 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. Most teams wire it into their loan origination or application workflow in under an hour. There's also a dashboard for manual checks, and 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 now: https://tampercheck.ai/dashboard · Docs: https://tampercheck.ai/docs · Pricing: https://tampercheck.ai/pricing