Same receipt, new phone.
Still caught.

Most promotion fraud is not sophisticated. It is the same receipt again, from a friend’s phone, slightly cropped. Steve matches submissions visually and by identity fields across the whole history of a workflow, and rules catch the rest.

A shopper holding a receipt at a grocery checkout.
Methods
Perceptual image hash, identity fingerprint, similarity matching
Statuses
Clean, flagged, blocked
Scope
Per workflow, across its full submission history

What it does for you.

Visual duplicates

Perceptual hashing matches the same photo even when it is cropped, rotated, or re-compressed.

Data duplicates

Identity fields you choose, such as merchant plus date plus total, define what counts as the same purchase. Exact fingerprint matching, or similarity matching when receipts vary.

Per-participant limits

Attach a customer identity and enforce one payout per person, or whatever the terms say, across every channel.

Flagged, never silently dropped

Every match is recorded with the submission it matched. Reviewers see both side by side and resolve with one click.

How it works in practice.

  1. Choose identity fields

    What makes two submissions the same purchase in your world.

  2. Steve builds history

    Every processed submission joins the match index for its workflow.

  3. Review the matches

    Confirm fraud or clear a false positive. The outcome trains your thresholds.

Where this earns its keep.

Use cases that lean on fraud detection the most.

See it work
on your evidence.

See fraud detection running on your own evidence. Thirty minutes, no slides.

Book a demo 30 minutes. Your use case. Real possibilities.