How I Went from Paying $4,000/Month for Fraud Protection to Building My Own System
When I first started scaling eBike Generation, fraud wasn’t something I obsessed over.
At low order volume, it’s easy to manage risk. You might get the occasional suspicious order, but it’s not constant pressure. At that stage, I decided to use ClearSale. They were solid, easy to set up, and gave me something I valued a lot at the time: peace of mind.
Their model was simple — roughly 1% per order.
And honestly, in the early days, that made complete sense.
I didn’t have much fraud to begin with, but knowing someone else was handling it removed a lot of mental overhead. I could focus on growing the business instead of worrying about chargebacks or stolen cards.
The Problem with Scaling
As the store grew, everything changed.
We went from a few orders a week to multiple orders every single day. Revenue was increasing — which is great — but so were the fees.
That 1% started to hurt.
Before I knew it, I was paying between $3,000 and $4,000 every single month just for fraud protection.
And here’s the thing:
My fraud rates weren’t exploding. I wasn’t dealing with massive losses.
I was paying for insurance I rarely needed.
At that point, I had to ask myself a hard question:
Does this still make sense for the business?
The Turning Point
I realized I didn’t really have a choice.
If I wanted to protect margins while continuing to scale, I needed a different approach.
So I decided to take control.
I licensed similar underlying technology and started building my own internal fraud system. At first, it wasn’t perfect — far from it. But I committed to understanding how fraud detection actually works at a deeper level.
I studied the data behind each order.
I learned the patterns.
I started identifying the common red flags:
mismatched billing and shipping details
unusual IP locations
high-risk email behavior
inconsistent customer signals
Instead of relying on a black-box decision, I began interpreting the raw signals myself.
Building an In-House Fraud Team
Once I understood the system, I did something that changed everything:
I trained my team.
We built internal processes for reviewing orders.
We created guidelines for identifying high-risk transactions.
We became faster and more confident with every decision.
Over time, what started as a cost-cutting move became a real operational advantage.
The Results
The difference was massive.
Before: $3,000–$4,000/month in fees
After: A few hundred dollars per month in tooling
Yes, I gave up the “insurance” of guaranteed chargeback coverage.
But in return, I gained something far more valuable:
Control and intelligence.
We weren’t just approving or rejecting orders — we were understanding them.
That meant better decisions, fewer unnecessary declines, and more confidence in scaling the business.
The Unexpected Outcome
What started as a necessity turned into something much bigger.
As we refined the system, improved our processes, and trained our team, it became clear that this approach could help other Shopify stores facing the same problem.
That’s ultimately how FRIQ was born.
Not from theory.
Not from a pitch deck.
But from a real problem, experienced firsthand, at scale.
Let me leave you with this…
Fraud tools are incredibly useful — especially in the early stages.
But as your store grows, you need to constantly reevaluate:
Are you paying for protection you don’t actually need?
Are you relying too much on automation without understanding the data?
Could you make better decisions with more visibility?
For me, the answer was clear.
And making that shift didn’t just save money — it fundamentally changed how I operated.
If you’re scaling a Shopify store and starting to feel the weight of fraud costs, it might be time to take a closer look at what’s really going on under the surface.
Because once you understand the signals, everything changes.