The chargeback arrives weeks later
The order looked normal. It was paid, packed and shipped. Weeks later a chargeback notice arrives: the card was stolen. You lose the goods, the payment and a fee on top. Or a different problem: an account that places large orders on credit and never pays, a reseller exploiting a promotion, or a string of orders to addresses that turn out to be forwarding services.
Each time, someone looks back and sees the signs: a new account, an address that did not match, an unusual mix of products, a rushed delivery option. But nobody was looking at the time, because there are hundreds of orders a day and the warehouse needs to get them out.
Why fraud gets through
Payment providers such as Stripe, PayPal and Shopify Payments run their own fraud checks, and they help. But they judge the payment, not your business. They do not know that your normal customer is a trade buyer ordering the same lines each month, or that nobody genuinely orders five of a high-value item with next-day delivery to a new address.
Manual checks do not scale, and fixed rules either catch too little or flag so many good orders that staff stop paying attention. The patterns that matter are specific to your products, customers and channels, and they change as fraudsters adapt.
What late detection costs
| Problem | Cost |
|---|---|
| Chargebacks | Lost goods, lost payment and fees |
| Promotion abuse | Discounts taken by people they were not meant for |
| Bad credit accounts | Goods shipped on terms that are never paid |
| Too many false alarms | Good customers delayed and annoyed |
| High chargeback rates | Risk of penalties or restrictions from payment providers |
False alarms are a cost too. Holding a genuine customer's order for a check they did not expect can lose a sale as surely as fraud loses one.
How we score orders before they ship
- We gather order history from your e-commerce platform or ERP (Shopify, WooCommerce, Magento or a custom system) together with chargebacks, refunds, write-offs and any orders your team flagged as suspicious.
- We build features that describe each order in context: account age, order history, address match and type, product mix, order value against the customer's normal, delivery choice, device and time patterns where available.
- We train a model on your past good and bad orders, handling the fact that fraud is rare, and test it on a later period it has not seen.
- We connect it to the order flow through the platform's API or webhooks, so each new order is scored before it reaches fulfilment.
- Low-risk orders go straight through. The riskiest few are held in a review queue with the reasons shown, such as "new account, high value, address mismatch", so a person can check and release or cancel quickly.
- Review decisions and later outcomes feed back into the model, and we monitor it so it keeps up as patterns change.
You choose where the review threshold sits, trading how many orders a person checks against how much risk gets through. We show you what each setting would have done on your past orders.
What the team works with
A short queue of orders that genuinely deserve a second look, each with the reasons laid out. Everything else ships as normal. Staff stop scanning every order by eye and focus on the ones that matter.
Chargebacks and bad debts show up in the data as they happen, so you can see whether the pattern is changing and adjust.
The reasons shown in the queue also help with the other side of the problem. When a genuine customer is held, staff can see why and release the order quickly, and those decisions teach the model which patterns are harmless in your business.
Is this your situation?
- You regularly discover fraud or bad orders after they have shipped.
- Chargebacks or unpaid trade orders are a recurring cost.
- Your payment provider's checks do not catch the patterns you see.
- Staff check orders by eye, or not at all when it is busy.
- Manual rules flag so many good orders that people ignore them.