If you're running affiliate traffic in iGaming, crypto, or nutra, you probably have some form of antifraud protection. Maybe it's built into your CRM. Maybe you're using SEON, Covery, or FraudScore.

And it's probably costing you money.

Not because these tools are bad — they do exactly what they're designed to do. The problem is what they're designed to do: cut traffic.

The Binary Problem

Traditional antifraud works like a bouncer at a club:

Simple. Effective. And incredibly wasteful.

Here's what that bouncer doesn't ask:

The answer is always binary: yes or no, in or out.

But affiliate marketing isn't binary. It's a spectrum.

The 30% You're Throwing Away

Let me paint a picture.

Your antifraud flags a lead as "suspicious" because:

Traditional approach

Cut it. Zero revenue. Next.

Reality check: That lead might still convert at 6-8% on a backup offer. It might be a real person using a VPN. It might be someone who filled forms quickly because they've done it before.

When you cut "suspicious" traffic automatically, you're not just blocking bots. You're blocking:

Key insight

Our data shows up to 30% of "suspicious" traffic can still be monetized when routed correctly. That's not fraud prevention. That's revenue prevention.

Cutting vs. Routing: A Different Philosophy

Here's how BackNova approaches the same problem:

Antifraud Pre-CRM Scoring
Binary: bot or not Granular: 0-100% score
Decision: cut or pass Decision: route to best offer
Static rules ML learns from YOUR data
No feedback loop Compares with postback results
Protects FROM traffic Helps MONETIZE traffic

Instead of asking "Is this fraud?", we ask "Where should this lead go?"

Nothing gets thrown away unless it's clearly worthless.

How It Works in Practice

💡 Example Scenario

Situation: An affiliate team processes thousands of leads monthly, but approval rates are stuck.

Problem: Antifraud cuts ~15-20% of traffic as "suspicious." Many of these leads could still convert on backup offers.

Pre-CRM scoring approach:

  • Score all incoming leads (including previously "suspicious" ones)
  • Route low-score leads to backup offers instead of cutting
  • Feed postback data back into the model to improve over time

Expected outcome

Teams typically see significant improvement in approval rates simply by routing instead of cutting. The traffic doesn't get better — the decisions do.

The Behavioral Layer

Here's what makes pre-CRM scoring different from just "softer antifraud."

We don't just look at device signals. We collect behavioral data:

Then we compare this with postback data.

Lead scored 45% quality → Converted with $200 FTD?
The model learns. Next similar lead scores higher.

Lead scored 75% quality → Charged back after 30 days?
The model adjusts. Similar patterns get flagged.

This is the feedback loop antifraud doesn't have.

PII-0: Why This Matters for Compliance

One more thing.

BackNova uses a PII-0 architecture. We don't receive or store any personal data — no names, emails, or phone numbers. Only metadata and scores.

Why does this matter?

You get the scoring benefits without the compliance headaches.

The Bottom Line

Antifraud tools answer: "Is this a bot?"

Pre-CRM scoring answers: "What's this lead worth, and where should it go?"

Different questions. Different results.

If you're still binary-cutting "suspicious" traffic, you're leaving money on the table. Probably a lot of it.

Ready to see how much revenue you're routing to the trash?

15 minutes, no pitch — just your numbers.

Book a Demo →
AK

Anatolii Krylov

Founder of BackNova. 15 years in affiliate marketing, including managing $1M+ in ad spend at a crypto affiliate network. Building pre-CRM scoring to help teams stop bleeding money on leads they can't evaluate.