Intercept fraudulent orders and card payments

Online retailers, banks and payment providers detect fraudulent orders and card payments in real time

Needs setupAI Act: no extra obligationsFinding anomalies & fraudRetail & consumer goods, Banking & finance, FinTech & payments

Fits: Finance & accounting · Customer service · Legal & compliance · IT & software development

Orders with stolen card details, account takeovers and buy-now-pay-later fraud are detected before the goods leave. Honest customers still get through checkout without hurdles.

Is this for you?

  • You have noticeable losses from chargebacks, unpaid invoice purchases or account takeovers.
  • Order and payment data with the later outcome (paid, chargeback, fraud confirmed) is available for at least several months.
  • You can connect the shop, payment service and customer service so that stopped orders are reviewed quickly by a person.
First step today

Write ten made-up orders in a table, including a large order with a new customer account and a different delivery address, and a harmless business order delivered to an office. Have them classified with the template below and check whether the harmless cases get through correctly.

How to go about it

  1. Define types of fraud and warning signs
  2. Link order, payment and chargeback data
  3. Set up rules and a model in checkout
  4. Have stopped orders reviewed by people
  5. Define consequences and a route to resolution
  6. Adapt rules to new scams

Templates to copy

Try it now
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Plan the rollout
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Included in the training

The complete guide

  • 6 steps explaining exactly what to do
  • 3 proven templates to copy into your own AI
  • Safe use: data, checking, deciding, informing
  • Pitfalls, effort and how to recognise success

With the training you get all 430 guides for as long as your access runs.

See the trainingOne-off per person, no subscription

Same approach, other use cases

12 more use cases follow the pattern “Finding anomalies & fraud”.

Check claim notifications for signs of fraudCheck service charge statements for errorsDetect cheaters by their gameplay behaviourDetect market manipulation on trading platformsDetect money laundering patterns in account activityDetect phishing emails and suspicious sign-insDetect serial defects in fleet data before a recallFind duplicate and suspicious incoming invoicesReview client bookkeeping for audit risks in advanceSelect tax cases for review on a risk basisStop fraudulent calls and SIM swap fraudTrack down electricity theft and tampered meters
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G-HK-07 · Legal status 08.10.2026