about : Upload
Drag and drop your PDF or image, or select it manually from your device via the dashboard. You can also connect to our API or document processing pipeline through Dropbox, Google Drive, Amazon S3, or Microsoft OneDrive.
Verify in Seconds
Our system instantly analyzes the document using advanced AI to detect fraud. It examines metadata, text structure, embedded signatures, and potential manipulation.
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Receive a detailed report on the document's authenticity—directly in the dashboard or via webhook. See exactly what was checked and why, with full transparency.
How modern technology and visual clues combine to detect fake receipts
Detecting a counterfeit receipt requires a blend of technical analysis and attention to visual cues. At the technical level, metadata is a powerful signal: PDF and image files often contain creation timestamps, software identifiers, and editing histories. When a receipt claims to be generated at a specific time or by a particular point-of-sale system, mismatches in metadata can indicate tampering. Optical character recognition (OCR) converts scanned text into analyzable content; advanced OCR engines preserve font, spacing, and alignment details so that anomalies—such as inconsistent font families or irregular line heights—stand out.
Visual inspection remains essential. Look for uneven margins, blurring around printed text, and irregular logo placement. Genuine receipts follow consistent templates; altered amounts, added line items, or duplicated logos are red flags. Micro-details like faded ink patterns, inconsistent spacing between characters, or misaligned columns often point to manual editing. High-resolution inspection can reveal layering artifacts—pixels or compression remnants—that occur when elements are pasted into an image.
On the algorithmic side, machine learning models trained on large receipt datasets can learn typical structure: header blocks, itemized lines, tax calculations, and totals. These models flag receipts where arithmetic doesn't add up (for example, subtotal plus tax not matching total) or where typical merchant fields are missing. Natural language processing helps detect unnatural phrasing or missing locality markers that real merchants include. Combining these technical checks with human review improves accuracy: a system that highlights suspect fields enables efficient verification while reducing false positives. Employing both automated and manual scrutiny ensures a robust approach to identifying forged or manipulated receipts.
Step-by-step verification: Upload, instant analysis, and interpretable results
Start by uploading the document to a secure platform or using an integrated API. The upload step should accept common formats—PDF, JPG, PNG—and preserve file metadata during transmission. Once uploaded, the system runs a rapid series of checks. First, metadata analysis compares file creation and modification timestamps against claimed transaction times. Second, OCR extracts line items, merchant names, dates, and totals. Third, image forensic techniques examine compression artifacts, noise patterns, and edge continuity to detect pasted or cloned regions.
After these low-level checks, higher-level validation occurs. The system performs arithmetic reconciliation: verifying that subtotals, discounts, taxes, and totals follow expected formulas. It cross-references merchant information with public records or known templates; many point-of-sale systems generate receipts with predictable headers and tax IDs that can be validated. Signature and watermark detection looks for embedded security features; if a receipt claims an electronic signature or QR code, the verifier attempts to decode and validate those tokens.
Results are presented in a transparent, actionable report showing which checks passed and which failed. Each flagged item includes an explanation—e.g., "file timestamp older than claimed purchase date" or "discrepancy between itemized total and grand total"—so reviewers understand the risk. For hands-free workflows, webhook notifications can push the report to ticketing systems or accounting software. For tools designed to detect fake receipt fraud, the combination of fast upload, layered analysis, and clear reporting reduces manual workload while improving detection rates and compliance with auditing standards.
Real-world examples, case studies, and prevention practices
Case 1: Expense fraud in a mid-size company. An employee submitted clustered receipts with identical totals and suspiciously similar layout across different merchants. Forensic analysis revealed identical metadata footprints and duplicated image regions—clear signs of copy-paste manipulation. After cross-referencing merchant IDs and transaction times, the finance team recovered overpayments and tightened submission policies, requiring original card statements for high-value claims.
Case 2: Online merchant chargeback dispute. A customer claimed a refund based on a receipt image. Image forensics detected inconsistent compression levels and mismatched font families in the item lines. OCR extracted a merchant registration number that did not match the claimed store. The merchant used these findings to win the dispute by proving the document had been altered and did not originate from their point-of-sale system.
Prevention practices reduce both deliberate fraud and accidental errors. Require original receipts or digital receipts emailed directly from merchant systems whenever possible; digital receipts often contain cryptographic tokens or unique transaction IDs that are much harder to fake. Implement two-factor verification for expense approvals: pairing receipt upload with bank statement verification cuts fraud significantly. Educate staff about common red flags—typos in merchant names, mismatched logos, unusual totals—and build automated checks into expense management systems to catch arithmetic and template anomalies. Finally, maintain an audit log of uploads and verification reports so suspicious patterns—such as repeated submissions from the same device or repeated editing signatures—can be trended and investigated. These real-world controls, combined with forensic tools, create a resilient defense against receipt fraud.
Granada flamenco dancer turned AI policy fellow in Singapore. Rosa tackles federated-learning frameworks, Peranakan cuisine guides, and flamenco biomechanics. She keeps castanets beside her mechanical keyboard for impromptu rhythm breaks.