Edvent AI

Z-Fraud · Loss prevention & audit

Supercharge loss prevention and audit at scale

Losses you never saw become cases you can act on. Z-Fraud automatically detects and route the suspicious cases to the right team for resolve in real-time.

The Z-Fraud review console: store footage with playback controls, a lane-by-lane event timeline, transaction details and a submit-decision panel

Real-time detection & interpretation

    • Cash theft at the till
    • Return and refund fraud
    • Items that left the counter but never scanned
    • Drawer openings with no sale against them

10x the precision of your fraud detection

Illustrative example: three events from one morning at store 1091, exactly as Z-Fraud presents them. Pick one and see the case build itself.

Z-Fraud

Needs review

Convenience store counter seen from overhead, with the models' detection boxes over the staff, the customers and their hands; a customer leaning across the counter has a bottle of cooking oil in hand
Store
1091 · Talat Khae Thaew
Lane
LANE B · POS 2
Date & time
28 Jun 2026 11:05:42
11:05
12:45

What the till recorded

Sale
R260628-004637CASHIER B
Iced americano140.00
Egg sandwich145.00
Oil, seen in handNot on receipt
Total amount85.00
Cash tendered100.00

Your decision

The camera saw three items leave the counter. The till rang up two.

Video vs receipt67% match

Illustrative example. Client footage anonymised; data and markers changed.

What this looks like across a store network

One live deployment, in the client's own numbers: where they started, what we put in, and what changed on the shop floor.

Live deployment · convenience retail · Thailand

How a 2,300-store chain halved its shrinkage

A major Thai convenience store chain knew roughly what it was losing every year. What it could not do was find those losses inside the footage: thousands of stores, a security team of a few dozen, and no way to tell which minute of which camera mattered.

Z-Fraud is now expanding store by store across the chain, reading every transaction against the video of that transaction. In each store that goes live, reviewers open a ranked list each morning instead of searching tape.

A Z-Fraud panel over anonymised store footage: detection boxes and labels across the checkout row, a cash-theft and phantom-return read underneath, and the legend for each object and role the models track
Sector
Convenience retail
Estate
2,300 stores, nationwide
Product
Z-Fraud
Deployment
On-premise

What changed after go-live

50%
Projected annual loss cut
5×
More confirmed fraud found
10×
Faster to act on an event

Measured against the same security team's own case list before deployment.

2.5×

More precise on cash theft

Of the cash-theft cases the team opened, two and a half times as many were confirmed as real.

11×

More precise on return fraud

Refund and return cases are where the gap between video and receipt is widest, and where the models gained the most.

Our process

  1. Coverage check first

    We read their existing cameras and told them which angles gave a usable read before anything was committed. Some of those cameras were over a decade old.

  2. Retailer-specific incidents

    There is no one-size-fits-all. Our forward-deployed team worked with their loss prevention leads to define the key incidents, then built the data foundation and train the models against proprietary footage.

  3. A morning list, on-premise

    Everything runs inside their own estate. The security team opens each flagged event with its footage and its receipt already lined up, confirms it, and closes it.

More detail on the estate, the rollout order and the internal reporting is being added to this page. Ask us for the full write-up in the meantime.

Protect your profit at scale

30 minutes · discovery & free consultation