Sovereign Nations. Different Laws. One Complicated Payments System.
Sovereign Nations. Different Laws. One Complicated Payments System. On Indigenous Peoples' Day, it's time to examine why tribal gaming, ...
For more than two decades, the payments industry has invested billions of dollars in improving merchant onboarding.
We've digitized applications.
We've automated document collection.
We've integrated KYC, OFAC, sanctions screening, beneficial ownership verification, fraud detection, credit scoring, and identity verification.
We've built APIs.
We've added machine learning.
We've created digital onboarding experiences that can approve some merchants in minutes.
Yet despite all of this innovation, one critical part of merchant underwriting has remained virtually unchanged:
The actual evaluation of merchant risk.
That should concern every acquiring bank, payment processor, payment facilitator, ISO, marketplace, and regulator.
Because while the technology surrounding underwriting has evolved, the fundamental model used to determine merchant risk is largely the same model our industry relied upon twenty years ago.
And that's becoming an increasingly dangerous problem.
The first question asked during almost every underwriting process is surprisingly simple:
| "What type of business is this?" |
From that answer comes an MCC (Merchant Category Code).
From that code comes a risk profile.
From that profile comes underwriting policies, documentation requirements, monitoring rules, pricing, reserves, and approval decisions.
The entire process assumes one thing:
Businesses that share the same industry classification present similar levels of risk.
That assumption may have been reasonable twenty years ago.
It isn't today.
They Sell Products.
A CBD merchant isn't risky because they're classified as a CBD merchant.
They're risky—or not—based on what they're actually selling.
The same is true for:
Even restaurants.
One restaurant might sell hamburgers.
Another might primarily sell CBD beverages, THC-infused products, or regulated supplements online.
They could carry the exact same MCC.
Yet their regulatory obligations, compliance requirements, sponsor bank exposure, and card brand risk profiles are dramatically different.
The MCC doesn't tell you that.
The application doesn't tell you that.
In many cases, neither the merchant nor the sales agent fully understands the implications.
Once the application is submitted, underwriting begins a familiar routine:
Review the website.
Review product pages.
Search Google.
Look through social media.
Check lab reports.
Read policies.
Request additional documentation.
Ask compliance questions.
Review labels.
Look for prohibited products.
Validate licenses.
Repeat.
This isn't underwriting.
It's detective work.
And every underwriter performs that investigation differently.
Some review five pages.
Others review fifty.
Some recognize hidden compliance risks.
Others don't.
Two underwriters reviewing the same merchant may reach entirely different conclusions—not because they're wrong, but because the process depends heavily on manual interpretation.
That inconsistency creates operational inefficiency, delays merchant onboarding, and increases institutional risk.
Even if a merchant is properly underwritten today, what happens tomorrow?
Websites change.
Products are added.
Marketing claims evolve.
Ingredients change.
Compliance requirements shift.
Third-party sellers expand catalogs.
Regulators introduce new guidance.
State laws change.
New pages are published.
Legacy underwriting captures a single moment in time.
Merchant risk evolves continuously.
Yet many institutions still rely on annual reviews—or worse, no meaningful ongoing review at all unless an issue surfaces.
The gap between onboarding and ongoing monitoring has become one of the industry's largest blind spots.
Most innovation over the past twenty years has automated paperwork.
Electronic applications.
Digital signatures.
Document uploads.
Workflow automation.
Case management.
These improvements matter, but they don't answer the most important question:
| What is this merchant actually selling today, and does it align with our policies? |
That answer isn't found in PDFs.
It's found across websites, product catalogs, images, claims, pricing, content, policies, certifications, and publicly available information.
The industry has automated the collection of information while leaving the interpretation of that information largely manual.
Artificial intelligence is changing what is possible.
Rather than relying primarily on industry classifications and manual website reviews, underwriting can begin with a comprehensive understanding of the merchant's actual business.
Imagine a system that automatically:
Instead of asking:
"What industry is this merchant in?"
We can begin asking:
| "What products and services create risk, and how should we respond?" |
That's a fundamentally different approach.
Merchant underwriting should no longer be viewed as a single approval event.
It should become an ongoing intelligence process.
Applications establish identity.
AI establishes context.
Continuous monitoring maintains confidence.
As commerce becomes increasingly digital and product catalogs evolve in real time, institutions that continue relying primarily on static onboarding models will struggle to keep pace with regulatory expectations, operational demands, and merchant growth.
The future belongs to organizations that can transform underwriting from a periodic review into a living, continuously updated view of merchant risk.
The payments industry has spent twenty years making underwriting faster.
Perhaps it's time we ask whether we've been trying to optimize the wrong process.
Maybe the real opportunity isn't improving manual underwriting.
Maybe it's replacing the assumptions that manual underwriting was built upon.
The question isn't whether merchant underwriting will change.
The question is who will lead that change—and who will still be relying on yesterday's assumptions when the rest of the industry moves forward?
Qredible is redefining merchant underwriting through Merchant Risk Intelligence (MRI)—a product-first approach that continuously analyzes what businesses sell, how they market those products, and the evidence required to support compliant payment acceptance. By moving beyond static industry classifications, Qredible helps banks, payment processors, ISOs, and sponsor banks make faster, more informed, and more defensible underwriting decisions while reducing manual effort and strengthening ongoing portfolio oversight. Learn more about Qredible's product-first automated compliance management platform for regulated industries →
