Order fraud scoring on autopilot:
a risk number before the warehouse packs it
Most stores catch fraud after a chargeback arrives, weeks after the product already shipped. We build a model that scores every order's fraud risk before it reaches the warehouse, trained on your own confirmed fraud and chargeback history, so the riskiest orders get a second look before they cost you a product and a chargeback fee both.
The process today
Fraud on most stores gets caught the expensive way: a chargeback notice arrives from the payment processor weeks after the order shipped, the product is gone, the chargeback fee is owed on top of the lost merchandise, and the store’s only recourse is to try to tighten a rule for next time. By then the pattern that should have been caught, a mismatched billing and shipping address, an unusually fast sequence of orders from a new account, a shipping address already flagged once before, is old news.
Manual review of every order does not scale, and manual review of none leaves every risky order through unchecked, so most stores end up somewhere in the middle: a few obvious rules, like blocking a known bad billing country, that catch the crudest attempts and miss everything subtler, while a team only finds out about the fraud that got through once the chargeback arrives.
The deeper issue is that the store’s own history of what fraud actually looked like for its specific products and customers sits unused. A generic fraud rule set built for e-commerce broadly does not know that for this store, fraud tends to show up as a specific combination of signals that a model trained on this store’s own cases could learn directly.
What the agent does
The model trains on your own confirmed fraud and chargeback history, learning which combinations of signals, device and browser fingerprint, address mismatch, order velocity from a new account, product category, actually preceded fraud for your specific business, and scores every new order against that pattern before it reaches fulfilment. Orders above a risk threshold you set go into a hold queue with the reasoning attached, so a reviewer sees exactly why an order was flagged, new device plus mismatched address plus an unusually large first order, instead of a bare numeric score.
The vast majority of orders score low risk and move straight through to fulfilment without any added friction, since the goal is catching the risky minority, not slowing down every legitimate customer. A dashboard tracks risk distribution and, over time, how flagged orders actually resolved, confirmed fraud, false positive, legitimate but unusual, so the model’s accuracy is visible and tunable rather than a black box.
Before go-live, the model is backtested against your past confirmed fraud and chargeback cases, so you can see how many it would have caught and how many legitimate orders it would have unnecessarily held, letting you set a threshold that matches your actual risk tolerance.
What stays with humans
Deciding what to do with a flagged order, release it, request ID verification, cancel it, stays with your team, and no order is cancelled automatically by the model itself. Payment disputes and chargeback responses also stay with your team; the model’s job ends at flagging the risk and explaining why.
Guards
Every score is logged with the signals behind it, and the eventual outcome of each flagged order is tracked, so the model’s accuracy is measured against reality rather than assumed. A backtest against your past fraud and chargeback cases runs before go-live, and a kill switch reverts to your previous manual or rule-based process in one message if the scoring ever looks unreliable.
Price and timeline
| Option | Price | What it covers | Timeline |
|---|---|---|---|
| Single automation | from $900 | Main storefront, risk model, hold queue | 7 to 14 days |
| Department package | from $2,800 | Fraud scoring plus refund and chargeback workflow automation | 3 to 5 weeks |
Running cost is usually $20 to $80 a month depending on order volume.
Related
Pair this with refund and return handling so a confirmed fraud case flows straight into the right resolution path, and with data quality monitoring so the order and customer data feeding this model stays clean. The simpler, metrics-wide version of anomaly detection lives at fraud and anomaly alerts. The full package breakdown is on the AI agents service page and the automation-everything overview; for a real marketplace’s fraud work, see the ProBay marketplace case study and the digital goods marketplace case study.
Ready to score orders before they ship instead of after a chargeback? Get in touch and we will look at your order history in the first call.
Tired of doing this by hand? We can take the whole routine off your team, not just this step: Routine takeover, from $400 →
FAQ
How much does order fraud scoring cost?
From $900 for a model covering your main storefront, live in 7 to 14 days. A department package adding refund and chargeback workflow automation usually starts at $2,800.
How does this differ from our payment processor's built-in fraud checks?
Processor-level checks are generic and tuned across many merchants. This model trains specifically on your own confirmed fraud and chargeback history, which usually catches patterns specific to your product and customer base that a generic check misses.
What happens to a flagged order?
It goes to a hold queue with the reasoning attached, so a team member can review and release it, request additional verification, or cancel it, rather than it shipping automatically or being blocked outright without review.
How much fraud history do you need to train on?
Ideally a few dozen confirmed fraud or chargeback cases alongside a larger set of clean orders. With less history, the model leans on a smaller, more conservative set of signals and flags more for review rather than guessing confidently.
Does this slow down legitimate orders?
The vast majority of orders score low risk and move through fulfilment without any delay. Only orders above your chosen threshold are held, and that threshold is yours to tune based on how much review capacity you have.