Coupon and welcome-offer abuse
First-order discounts are claimed again and again under new emails. A visitor ID stored with each order ties the claims to the device behind them.
A quick checkout. For people, not scripts.
Online retail pays for abuse in discounts, chargebacks and sold-out stock. Fingerly adds device and network context to checkout, coupons and logins, so a known customer moves through and a script, a farm or an emulator is noticed.
- Checkout contextA signal beside your payments.
- CouponsOne welcome offer per device.
- BotsHoarders and card testers.
- Returning buyersFewer hoops for familiar devices.
[ 01 / 06 ]Abuse patterns
How abuse reaches e-commerce.
A store sees fraud at the till and abuse long before it. These are the patterns that most often reach online retail, and where each one shows up.
Card testing
Scripts try batches of stolen card numbers at checkout. The session is flagged for automation, hosting networks and unusual request rates; Fingerly does not see card data.
Account takeover and stored cards
Stolen logins are used to buy with saved payment methods. A login from a device the customer never used is worth a closer look.
Inventory hoarding and scalping bots
Bots add limited stock to carts the moment a drop opens. Driven browsers and declared scrapers can be slowed before the stock is gone.
Fake accounts and review fraud
Throwaway accounts created to post reviews or to exploit referral credit. The same device behind many of them becomes visible once it is recorded.
Repeat refund and return abuse
A customer who returns orders under several accounts is easier to see when the accounts share a device, though the policy that follows is your own.
[ 02 / 06 ]Pattern to decision
From pattern to decision.
Fingerly is an input to a retail fraud decision, never a replacement for your payment provider or your own fraud operations.
01
Coupon and referral abuse
Reward new customers, not the same device.
Identify at signup and at coupon redemption, and count redemptions per visitor ID. Review devices that keep coming back for more, and clusters that device farm signals point to. The usual first decision is to hold the discount for review.
- Check at
- SignupCoupon redemptionReferral rewards
- Reads
- Visitor IDNew or returningDevice farm
02
Card testing at checkout
Check the session, not the card.
Identify at add-card and at payment. Automation, a hosting network and unusually high activity together are a strong reason to refuse the attempt; one weak signal is a reason for a challenge.
- Check at
- Adding a cardPayment
- Reads
- AutomationHosting networkHigh activity
03
Hoarding and scalping bots
Put the check where automation gets paid.
Identify at search, at add-to-cart and at checkout for limited stock. Declared scrapers and driven browsers can be slowed or refused before the stock is gone, and real shoppers go through untouched.
- Check at
- Add to cartLimited drops
- Reads
- AutomationDeclared bot or scraperVisitor ID
04
Takeover of customer accounts
Familiar devices skip the second factor.
Recognise the devices a customer shops from. Keep the step-up for a login from a device the account has never used, especially when it is automated or anonymised.
- Check at
- LoginChanging addressCheckout with a saved card
- Reads
- Visitor IDTor exit nodeAutomation
[ 03 / 06 ]Integration
Integrate at the right moments.
Four habits keep a policy for e-commerce effective without turning it against genuine customers. A production identification is billed per request, from $0.0025, so it is worth placing where it earns its cost.
- 01 Identify at the moment
Call identify at signup, login, coupon redemption and checkout, not on every product page. Pass your order or account reference as the tag so the result comes back with what it describes.
await fingerly.identify({ tag: "checkout" }) - 02 Verify on your server
Send only the request ID to your backend, read the stored result with a secret key, and check that the tag matches the action. Combine it with what your payment provider already tells you.
- 03 Observe before you enforce
Run in monitoring mode first. Shared household devices and office networks are ordinary at a shop, so measure who a rule would affect before enforcing it.
- 04 Act in proportion
Challenge or hold uncertain sessions rather than refusing them, route clusters to review, and keep a way for a genuine shopper to complete the purchase.
[ 04 / 06 ]Moments
Where to check, what to do.
Where a retailer usually places the check and what it reads there.
| Moment | Identify when | Read | Usual first decision |
|---|---|---|---|
| Signup | The account is created | Visitor ID, new or returning, device farm | Hold welcome credit for clusters |
| Coupon redemption | Before the discount applies | Visitor ID, high activity | Review repeat redeemers |
| Add to cart (limited stock) | When the drop opens | Automation, declared scraper | Slow or refuse automated sessions |
| Payment | Before authorisation | Suspect score, hosting network, automation | Challenge above your threshold |
| Login | Credentials are submitted | Visitor ID, network signals | Step up for new devices that carry other signals |
Every decision is yours. Fingerly returns the visitor ID, the signals and the suspect score, and never blocks a request by itself.
[ 05 / 06 ]Questions
Questions about e-commerce.
How an online store uses device fingerprinting, and what it does not replace.
Talk to the teamHow can online retailers prevent coupon and promo abuse?
With Fingerly, an online retailer identifies the device at signup and at redemption, stores the visitor ID with each claim, and reviews devices that claim repeatedly. Device farm and emulator signals point to clusters worth reviewing, rather than every customer.
Can device fingerprinting detect card testing?
Fingerly flags the session around card testing, not the card: automation, hosting networks, virtual machines and unusually high activity from one device. It never sees card numbers, so use the result beside the checks your payment provider already runs.
Does Fingerly replace my payment fraud tool?
No. Fingerly is an additional input that sits beside payment authorisation, payment authentication and your fraud operations. It describes the device and network behind a request and returns a suspect score, and your systems decide what to do with it.
Will it slow down checkout for genuine customers?
It should not. Fingerly shows no captcha and no prompt, the browser SDK answers after an initial collection budget of 300 milliseconds, and a customer on a familiar device can skip extra steps. Friction is kept for sessions that carry evidence.
Can I stop bots from buying limited stock?
You can make them visible and slow them. Fingerly reports driven browsers, declared scrapers, virtual machines and unusually busy devices at add-to-cart and checkout, and your application decides whether to challenge or refuse. No detection stops every bot, so keep rate limits too.
Less guessing. More knowing.
Make the next connection a trusted one.
Start with the signals, keep your own decisions, and pay as you go, only for what you identify. No credit card needed.