Document fraud detection
CheckFile's AI identifies tampered documents, inconsistencies and forgeries. Modified PDF metadata, photoshopped payslip amounts, mismatched fonts — nothing slips through.
Multi-layer detection
AI simultaneously analyzes metadata, visual structure, text content and cross-document consistency to catch every fraud attempt.
Metadata analysis
Detects PDF modifications (editing software traces, inconsistent creation dates, suspicious versions) invisible to the naked eye.
Visual consistency
Identifies image tampering: non-standard fonts, suspicious alignments, compression artifacts from copy-paste edits.
Confidence scoring
Every document receives a confidence score from 0 to 100, with a breakdown of detected anomalies and their severity level.
How it works
Structural analysis
CheckFile examines the document's internal structure: PDF metadata, image layers, embedded fonts and modification history.
AI anomaly detection
Deep learning models trained on millions of documents identify fraud patterns: altered amounts, copied signatures, forged stamps.
Cross-validation
Extracted information is cross-checked across documents in the same case. A mismatch between the payslip and the tax notice triggers an alert.
Detection report
A detailed report shows each anomaly with its location in the document, type and risk score.
Use cases
Mortgage fraud
CheckFile detects a payslip with amounts altered using Photoshop, preventing a 350,000 EUR loan to an insolvent borrower.
Fake diplomas and certificates
The agency detects a fake diploma through metadata analysis revealing a recent creation date for a document supposedly from 2015.
Forged vehicle registration
The dealer identifies a registration document with an altered serial number, preventing the sale of a stolen vehicle.