Common types of fraud
| Fraud type | What happens | Who it hits |
|---|---|---|
| Stolen payment details | Purchases with someone else's card or account | Online stores |
| Account takeover | Criminal logs into a real customer's account | Stores, wallets, fintech apps |
| Promo and referral abuse | Fake accounts claim offers repeatedly | Apps with sign-up bonuses |
| Friendly fraud | Real customer disputes a valid charge | Online stores |
| Fake reversals and payment SMS | Customer shows a fake confirmation or reverses after delivery | Shops and delivery businesses |
How AI fraud detection works
Traditional fraud checks use fixed rules: "block orders over X", "block this country". They're easy to understand but miss new tricks and block good customers. AI models instead learn what normal behaviour looks like from your past transactions and flag what's unusual, combining many small signals at once:
- Device and location: new device, unusual location, or a location that doesn't match the delivery address.
- Behaviour: typing speed, how fast a form was filled, many failed attempts.
- Order patterns: unusual basket, many high-value items, rushed delivery to a new address.
- Account history: brand-new account, recently changed email or phone, many accounts from one device.
- Velocity: many orders or sign-ups in a short time.
Each transaction gets a risk score. Low risk passes automatically, high risk is blocked, and the uncertain middle gets an extra check or a human review.
What smaller businesses can do today
- Switch on your payment provider's fraud tools. Most card processors include machine-learning fraud screening. Make sure it's enabled and tuned.
- Verify payments on the server. Never ship because a customer shows a screenshot or SMS; confirm the payment through the provider's API or callback first.
- Protect accounts: strong login (passkeys or two-factor), alerts when email, phone or password change, and limits on failed attempts.
- Add velocity limits: maximum orders, sign-ups or promo uses per device, card or phone number.
- Review the risky middle by hand before shipping high-value orders.
- Keep evidence (delivery confirmations, logs, messages) to win disputes.
The hidden cost of fraud tools is false positives: real customers wrongly blocked. Track how many good orders you decline, not just how much fraud you stop.
For payment and fintech apps
- Score every login and transaction, not just sign-up.
- Use step-up checks (PIN, biometrics, one-time code) only when risk is high, so normal users aren't slowed down.
- Watch for SIM-swap patterns: a phone number change followed quickly by large transfers.
- Build an investigation dashboard so your team can see why something was flagged.
- Keep humans in the loop for account freezes and large reversals.
Build, buy, or combine?
Most businesses should start with their payment provider's built-in screening, then add custom rules and checks around their own data: orders, accounts, delivery and M-PESA records. A custom risk model makes sense once you have enough transaction history and fraud losses justify it.
Fraud prevention isn't about blocking everything suspicious. It's about letting good customers through instantly and putting friction only where the risk is. AI makes that balance far easier than fixed rules alone. We build secure online stores, wallets and payment apps with server-side payment verification, account protection and fraud checks designed for your business.
See our mobile app development and custom software services, or read passkeys and passwordless login.
