False Positives

Spot the Difference: Real Customers vs. Real Fraud

Fraudsters know how to blend in. A high-value order from a new customer. A login attempt from an unfamiliar device. An email address created just a few weeks ago. All potential red flags — but are they really fraud?

For you, telling the difference between a real customer and a real fraudster isn’t always straightforward. And every false decline comes at a cost.

• A customer gets flagged for suspicious behavior when they just logged in from a new device.
• A big purchase gets blocked when it’s actually a loyal customer making a holiday splurge.
• A transaction goes through manual review because the email is “new,” but the buyer is just new to your store.

The result? Frustrated customers, lost revenue, and a fraud model that’s working against you.

With AtData’s help, you don’t have to guess. Our real-time insights help you spot the difference between fraudsters and trusted customers, so you can reduce false positives without increasing fraud risk.

Why False Positives Are a Bigger Problem Than You Think

Most fraud prevention models err on the side of caution. That makes sense, until it starts costing you more than it’s protecting. Fraud prevention should stop actual fraud, not create unnecessary roadblocks for paying customers.

1. Lost Revenue: Every false decline is a sale you’ll never recover and future sales you’ll never see.
2. Customer Frustration: Blocked transactions drive away real buyers and hurt retention.
3. Increased Costs: Your fraud team wastes time reviewing legitimate transactions.
4. Marketing Waste: Good leads get flagged, undermining your customer acquisition efforts.

How AtData Helps You Spot the Difference

Traditional fraud detection models often rely on inflexible rules, flagging transactions based on factors like:

• New email address
• Unusual login location
• High-value purchase

But fraud isn’t that simple, and real customers don’t all behave the same way. AtData helps you go deeper than surface-level risk signals or blocklists. By layering these insights into your fraud detection model, you can approve more legitimate customers while still blocking actual fraud.

• Email Activity History: Is this email linked to an engaged, real user—or was it just created for fraud?
• Domain Reputation: Is this email from a trusted provider, or one commonly used for fraud?
• Behavioral Patterns: Has this email been used across multiple platforms consistently, or is it acting suspiciously?

Two Transactions, Two Very Different Risks

Let’s look at two nearly identical scenarios— and how AtData helps tell them apart.

• Scenario 1: A New Email, Big Purchase, Different Shipping Address

  On paper, this transaction looks risky: a high-value order, a recently created email, and a different shipping address. But a deeper look shows:

  • The email, while newer, has shown a realistic engagement history across a network of activity.
  • It isn’t disposable, the domain is from a reputable provider, and it correlates with the name on the account.
  • The user has consistent behavioral patterns before checkout and no red flags from the fraud consortium.

  Verdict? A real customer making a legitimate purchase. AtData’s intelligence helps prevent an unnecessary false decline.

• Scenario 2: A New Email, Big Purchase, Different Shipping Address

  On the surface, this looks exactly the same, but a closer analysis reveals:

  • The email was created just hours before the purchase and shows signs of being disposable.
  • There has been a spike in activity from similar structured emails across the activity network showing signs of tumbling.
  • The behavioral patterns are sporadic and clustered, potentially bot activity.

  Verdict? Likely fraud. AtData’s intelligence helps you stop the transaction before it becomes a chargeback.

Without email address intelligence, both scenarios might have been treated the same. With AtData, you make smarter fraud decisions in real time.

Better Fraud Prevention, Fewer False Positives

Fraud models that rely on rigid rules block too many good customers. Fraud models that rely on smart data intelligence? They evolve, refine, and improve over time. AtData helps you:

• Reduce false declines by identifying legitimate customers with real-time email data.
• Improve fraud detection by layering email intelligence into existing security models.
• Minimize friction by letting trusted customers move through seamlessly.

Refine Your Fraud Model Without Losing Customers

Every blocked transaction should be a real fraudster, not a frustrated customer. With AtData, you can fine-tune fraud prevention models to maximize approvals, minimize false positives, and keep revenue flowing.