# Fake Payslip Detection for Loans > Fake payslip detection for loans is the forensic verification of payslip documents submitted with credit applications - personal loans, mortgages, auto loans, BNPL. TamperCheck catches edited, fabricated, and AI-generated payslips before they reach underwriting, protecting lenders from income fraud that traditional verification misses. **Canonical URL:** https://tampercheck.ai/fake-payslip-detection-for-loans --- ## Why payslip fraud is the #1 loan-application document fraud **Payslips are the most-edited document in loan applications.** Income is the primary underwriting variable, and a payslip is easy to fake: most look similar, the structure is well known, and the figures are small enough to feel low-stakes to fraudsters. The result is that payslip fraud accounts for the largest share of document fraud across personal lending, mortgages, and auto finance. Three patterns dominate: **net pay inflation** (changing a single number), **fabricated payslips** (generated entirely from a template), and **AI-generated payslips** (created by a generative model from a text prompt). All three pass visual review. Even bank-statement cross-checks are unreliable - fraudsters know to inflate both documents consistently. Fake payslip detection has to be forensic, not arithmetic. The signals are in the PDF structure, the font metrics, the producer metadata, and the spectral properties of any embedded images. TamperCheck inspects all of them automatically. ## What TamperCheck detects ### Net-pay arithmetic verification Gross pay, deductions, tax, super/401k, and net pay must all reconcile. A single edited figure breaks the chain; TamperCheck catches it. ### Font metric consistency Edited fields often use slightly different font weights or spacing. Cross-field font metric comparison reveals these edits. ### Producer metadata audit Real payroll systems (ADP, Xero, Gusto, MYOB, BambooHR) produce PDFs with consistent producer signatures. Edits introduce new tool fingerprints. ### AI-generated payslip detection Fully synthetic payslips from generative AI tools have spectral signatures that real payroll-system PDFs don't. The CV layer is trained on these. ### Year-to-date plausibility YTD figures, pay period sequencing, and date consistency are cross-checked against the claimed pay frequency. ### Bank statement cross-validation (optional) When the borrower submits a bank statement, TamperCheck cross-validates that the net pay actually credits the account on the claimed date. ## How lenders use TamperCheck for payslip fraud detection Most lenders integrate TamperCheck into the document collection step of the loan origination workflow. Every payslip uploaded by an applicant is sent to the TamperCheck API, and the verdict gates the application's progress to underwriting. - **Integrates with any LOS** - Encompass, Calyx, nCino, MeridianLink, or custom - **Runs on every payslip** - not just flagged applications - **$0.50 per document** - a fraction of any approved fraudulent loan - **Verdict in ~1 minute** - keeps origination workflows moving - **Risk score for auto-routing** - clean payslips skip manual review - **Works with multiple payslips per applicant** - cross-validates them - **Zero storage** - critical for borrower data compliance ## Frequently asked questions ### How do you detect a fake payslip on a loan application? Real fake payslip detection requires forensic analysis, not just arithmetic. A complete check inspects PDF structure and metadata, font metrics across every field, pixel-level signals on any embedded images, the producer signature (which payroll system made the PDF), and AI-generation spectral signatures. TamperCheck runs all of these automatically and returns a verdict in about a minute. ### What are the most common signs of a fake payslip? The most common signs are: a producer metadata field that doesn't match the claimed employer's payroll system, font metric inconsistencies between fields, net pay that doesn't reconcile with gross minus deductions, AI-generation noise patterns on the document, and YTD figures that don't align with the pay-period sequence. ### Can you detect AI-generated payslips? Yes. Fully synthetic payslips produced by generative AI tools exhibit spectral signatures and noise patterns that real payroll-system PDFs don't have. TamperCheck's CV layer is trained specifically on these signatures and catches AI-generated payslips that pass visual inspection. ### How much does payslip fraud detection cost per loan? $0.50 per payslip with TamperCheck. Most loan applications include 1–3 payslips, so per-application cost is $0.50–$1.50. That's a fraction of any single fraudulent loan that would otherwise be approved - the ROI is straightforward. ### Does payslip fraud detection slow down loan origination? Verdicts return in about a minute per document, and the API supports async webhooks. In practice, payslip checks complete while the applicant is still on the next step of the application. Clean payslips can be auto-approved at the document level, so manual review only sees the suspicious cases. ### Can it work with payslips from any country? Yes. The forensic checks are document-agnostic - PDF structure, font metrics, producer metadata, and AI-generation signatures apply equally to payslips from any country or payroll system. Country-specific tax-line validation is applied automatically when the document is classified. ## Related use cases - https://tampercheck.ai/use-cases/lending-credit - https://tampercheck.ai/use-cases/tenant-screening - https://tampercheck.ai/use-cases/hr-hiring ## Compare with alternatives - https://tampercheck.ai/compare/inscribe-vs-tampercheck-ai - https://tampercheck.ai/compare/ocrolus-vs-tampercheck-ai - https://tampercheck.ai/compare/snappt-vs-tampercheck-ai --- Try TamperCheck free: https://tampercheck.ai/dashboard · Docs: https://tampercheck.ai/docs · Pricing: https://tampercheck.ai/pricing