VAMP Is Measuring the Smoke. What If We Could See the Fire Starting?

Why Merchant Intelligence and Product Intelligence May Become the Leading Indicators of Payment Risk

For decades, medicine has struggled with a familiar problem: treating the symptom without fully addressing the underlying cause.

The pain appears, so we treat the pain. The inflammation appears, so we treat the inflammation. The blood pressure rises, so we manage the number.

Good medicine, of course, goes further. It asks a more important question:

What is causing the symptom?

Payments should be asking the same question about fraud, disputes and chargebacks.

The industry has become extraordinarily sophisticated at measuring what happens after a transaction occurs. We track fraud reports, disputes, chargebacks, authorization behavior, enumeration attacks, transaction velocity and dozens of other signals.

Visa's Acquirer Monitoring Program, or VAMP, is accelerating that focus. VAMP consolidates fraud and dispute monitoring into a single framework and measures acquirers and merchants using transaction-level fraud, dispute and enumeration data. Visa describes the evolution as a move from "outlier management" toward a "lifecycle risk management approach."

That's an important evolution.

But I believe it creates an even bigger question for the payments industry:

• What happened inside the merchant before the fraud, disputes and chargebacks started?

Because the chargeback may not be the disease.

It may be the symptom.

We Are Getting Very Good at Measuring the Smoke

VAMP deserves the attention it is receiving.

Visa calculates its core VAMP ratio using counts of reported fraud and disputes relative to settled card-not-present transactions. It monitors both acquirer portfolios and merchants, while separately incorporating enumeration activity. Visa's published framework also reduced the excessive-merchant threshold in the U.S., Canada, Europe and Asia Pacific from 220 basis points to 150 basis points effective April 1, 2026, subject to the program's applicable count criteria.

This creates very real consequences for processors.

A merchant's problems are no longer simply the merchant's problems.

Enough problematic activity across enough merchants can become a portfolio problem for the acquirer.

That should force payment organizations to think differently about merchant risk.

But here's where I think the conversation needs to evolve.

Most VAMP strategies I've encountered naturally focus on questions such as:

• Which merchants are approaching our thresholds?
• Which merchants are generating excessive disputes?
• Where is fraud increasing?
• Which portfolios are creating exposure?
• Which merchants need remediation?
• How quickly can we bring ratios back down?

Those are important questions.

But they are questions being asked after risk has already begun manifesting in the payment stream.

What if we moved farther upstream?

Chargebacks Rarely Come Out of Nowhere

Think about a merchant that operated successfully for three years and suddenly begins generating significantly more disputes.

Something probably changed.

Maybe fulfillment deteriorated.

Maybe delivery times increased.

Maybe the merchant introduced a subscription.

Maybe its cancellation policy changed.

Maybe customer service deteriorated.

Maybe its marketing became more aggressive.

Maybe it began making claims its products couldn't support.

Maybe it introduced an entirely new product category.

Maybe the products themselves changed.

Maybe pricing changed.

Maybe a new affiliate began generating traffic using deceptive advertising.

Maybe ownership or management changed.

Maybe the merchant wasn't what we thought it was in the first place.

Or maybe fraudsters discovered the merchant and began exploiting its checkout environment.

The eventual chargeback is important.

But it's often the end of a much longer story.

Chargebacks are often the receipt for risk that already existed.

What if we could identify more of that risk before the receipt arrived?

From Lagging Indicators to Leading Indicators

This is where I believe Merchant Intelligence changes the conversation.

Traditional payment-risk systems are exceptionally good at analyzing transactional behavior.

That's essential.

But much of that information is inherently downstream.

Consider the sequence:

Something changes inside the merchant

→ Customer experiences the change

→ Transaction occurs

→ Something goes wrong

→ Customer complains

→ Refund may be requested

→ Dispute occurs

→ Chargeback occurs

→ Fraud/dispute data accumulates

→ Ratio increases

→ Threshold is approached

→ Processor intervenes

By the time we reach the bottom of that chain, the processor has excellent evidence that something is wrong.

But consumers may already have been harmed.

The merchant may already have accumulated significant exposure.

The processor may already be approaching VAMP thresholds.

The sponsor bank may already be asking questions.

And remediation becomes much more urgent.

Now imagine another intelligence layer operating at the beginning of that sequence.

What If We Could See the Change Before We Saw the Chargeback?

Suppose a processor has a nutraceutical merchant that has historically performed extremely well.

Low disputes.

Low fraud.

Stable volume.

Strong processing history.

Then Merchant Intelligence detects something.

The merchant has added 27 products in two weeks.

Product Intelligence discovers several products containing ingredients the merchant didn't previously sell.

Website Intelligence detects new weight-loss and health claims.

The return policy changed.

A continuity offer appeared.

Shipping commitments changed.

New consumer complaints begin appearing online.

A new affiliate channel is promoting products using language that doesn't appear on the merchant's primary website.

At that moment, the merchant's VAMP ratio might still look perfect.

Transaction monitoring might see nothing unusual.

The MCC hasn't changed.

Ownership hasn't changed.

The MID hasn't changed.

Processing volume might be perfectly normal.

But has the merchant's risk changed?

Of course it has.

The question is whether our systems can see it.

Product Intelligence Adds a Signal Payments Has Historically Been Missing

This becomes especially important as commerce becomes more product-driven.

Imagine a merchant approved to sell 500 products.

Six months later, it sells 750.

Traditional merchant monitoring may still see the same business.

Product Intelligence sees 250 new variables.

One newly introduced product could create:

A regulatory issue.

A sponsor-bank policy violation.

Consumer complaints.

Misleading expectations.

Fulfillment problems.

Reputational exposure.

Or increased disputes.

The product doesn't have to be illegal to create payment risk.

Perhaps its marketing makes promises customers don't believe were fulfilled.

Perhaps the product is materially different from how it was advertised.

Perhaps a subscription is attached to it.

Perhaps its labeling creates confusion.

Perhaps customers believe they're purchasing one thing and receive another.

Perhaps it is being promoted by affiliates in ways the merchant doesn't adequately control.

Those are not simply compliance issues.

They can become chargeback issues.

And that means Product Intelligence can potentially contribute to fraud and dispute prevention.

Merchant Intelligence + Product Intelligence + Payment Intelligence

I believe the next generation of merchant risk management will connect three intelligence layers that have historically operated separately.

Merchant Intelligence

What is happening with the business?

Ownership changes. Website changes. Business-model changes. Licensing. Reputation. New domains. New sales channels. Customer complaints. Marketing practices. Fulfillment changes.

Product Intelligence

What is happening inside the commerce?

Products added or removed. Product categories. Ingredients. Claims. Pricing. Supporting evidence. COAs. Regulatory status. Product-policy relationships. Product changes.

Payment Intelligence

What is happening in the transaction stream?

Authorization patterns. Fraud. Disputes. Refunds. Enumeration. Velocity. Ticket sizes. Chargeback reason codes. Cross-border behavior. Transaction anomalies.

Each tells us something different.

But together they tell a much more complete story.

Merchant Intelligence tells us what changed.

Product Intelligence tells us what is being sold.

Payment Intelligence tells us what is happening financially.

When those signals are connected, risk management can begin moving from:

Detection after impact

toward:

Intervention before impact.

Mastercard Is Already Moving in This Direction

This isn't simply theoretical.

Mastercard's recently announced Merchant Trust Services explicitly recognizes the need for earlier merchant-risk signals.

Mastercard says traditional warning signals can arrive too late and describes a dynamic merchant trust profile using behavior both on and off its network. Importantly, Mastercard specifically
identifies external signals including changes in business data, goods and services sold, and negative social content.

Think about the significance of that.

One of the world's largest payment networks is effectively acknowledging:

Transaction data alone does not tell us enough about merchant risk.

Mastercard also reported that, in a pilot, its Merchant Scam & Risk Indicator identified approximately 80% of issuer-identified risky merchants, with many identified as early as 90 days before the issuer's initial escalation.

Mastercard's broader Merchant Trust Services strategy describes merchant risk management spanning onboarding, ongoing monitoring and remediation, using both proprietary network information and
external intelligence.

That's where the industry is heading.

Earlier.

Broader.

Continuous.

The Best VAMP Strategy May Not Be a VAMP Strategy

This is the part I believe deserves more discussion.

If you're managing VAMP exclusively by watching VAMP ratios, you're effectively managing the outcome.

It's similar to waiting for someone's blood pressure to become dangerously high and then asking what happened.

The better strategy is to understand the conditions that increase the likelihood of the outcome.

For processors, that means asking:

What merchant behaviors tend to precede elevated disputes?

What product changes correlate with future complaints?

What marketing changes precede chargeback increases?

What fulfillment signals appear before dispute ratios rise?

What merchant changes precede fraud?

What combinations of signals matter most?

And eventually:

Can we build an early-warning system around those signals?

That's where Merchant Intelligence becomes more than compliance infrastructure.

It becomes risk-prevention infrastructure.

Think About Risk as a Horizon

The closer we get to the transaction, the stronger some signals become.

But the fewer options we may have.

At the farthest edge of the horizon, we may see:

Merchant changes

Then:

Product changes

Then:

Marketing and customer-experience changes

Then:

Consumer complaints and reputation signals

Then:

Transaction anomalies

Then:

Refunds

Then:

Disputes

Then:

Chargebacks and fraud reports

Then:

VAMP threshold pressure

The farther upstream we detect meaningful risk, the more choices we have.

We can investigate.

Contact the merchant.

Request information.

Correct marketing.

Fix fulfillment.

Require updated policies.

Remove problematic products.

Increase controls.

Adjust reserves when justified.

Restrict certain activity.

Remediate.

Or terminate the relationship if necessary.

Once a merchant is already significantly affecting the portfolio's VAMP performance, those options become much more urgent.

Visibility creates time.

And time creates options.

This Is the Difference Between Monitoring and Intelligence

I've made this distinction before because I believe it is fundamental.

Monitoring tells you something happened.

Intelligence helps you understand what it means.

A website-monitoring system might tell you:

• 37 pages changed.

That's monitoring.

Merchant Intelligence should tell you:

• The merchant added six products, three fall into a newly regulated category, two contain ingredients outside sponsor-bank policy, and one product page introduced a continuity offer and marketing claims associated with elevated consumer-risk concerns.

That's intelligence.

Now add payment data:

• Dispute volume remains normal, but refund requests increased 18% during the same period.

Now we're beginning to understand something.

Not because one signal proves the merchant will generate excessive chargebacks.

It doesn't.

But because multiple independent signals can create a reason to investigate before the payment data reaches a critical threshold.

We Should Be Careful With the Word "Predict"

There is an important distinction here.

Merchant Intelligence cannot predict every chargeback.

Product Intelligence cannot predict every fraud event.

And a website change does not mean a merchant is becoming problematic.

False positives matter.

Context matters.

Human judgment matters.

The objective should not be to build another system that screams:

RISK!

every time something changes.

The objective is to determine which changes are material.

That's why intelligence matters more than raw monitoring.

One new product may mean nothing.

Twenty new products in a completely different regulated category may mean a lot.

A changed sentence may mean nothing.

A new "guaranteed cure" claim may mean something.

A new shipping page may mean nothing.

Changing delivery expectations from five days to six weeks may mean something.

Context turns signals into intelligence.

VAMP Could Ultimately Become a Feedback Loop

This is where the model becomes particularly powerful.

Imagine combining historical VAMP performance with Merchant Intelligence.

Instead of simply asking:

Which merchants exceeded thresholds?

we could ask:

What changed in those merchants before they exceeded thresholds?

Then we learn.

Perhaps certain combinations repeatedly appear:

Rapid product expansion + fulfillment changes.

Aggressive claims + refund-policy changes.

New affiliate traffic + customer complaints.

Product-category changes + elevated disputes.

New domain activity + transaction anomalies.

That historical information can improve future risk models.

Now VAMP data isn't simply an enforcement metric.

It becomes training data for earlier detection.

The system begins learning what the smoke looked like before the fire became visible.

That's a much more interesting use of the information.

Imagine the Portfolio View

Now scale this from one merchant to 50,000 merchants.

Instead of a risk team seeing only:

Merchant A — 87 bps

Merchant B — 112 bps

Merchant C — 141 bps

the organization could potentially see:

Merchant A

Payment risk: Low

Merchant-change risk: Low

Product-change risk: Low

Merchant B

Payment risk: Low

Merchant-change risk: Elevated

Product-change risk: High

Merchant C

Payment risk: Rising

Merchant-change risk: High

Product-change risk: High

Which merchant deserves attention first?

Perhaps Merchant B.

Even though its current VAMP performance looks completely acceptable.

That's the paradigm shift.

Don't only prioritize merchants based on where risk is today.

Prioritize them based on where risk appears to be developing.

This Is What Lifecycle Risk Management Should Eventually Mean

Visa explicitly describes VAMP as an evolution toward lifecycle risk management.

I think the industry should take that phrase seriously.

The merchant lifecycle doesn't begin with the first transaction.

And it doesn't end with a chargeback.

It begins before onboarding.

Discover.

Underwrite.

Understand.

Approve.

Then the merchant begins processing.

Monitor.

Detect change.

Evaluate.

Remediate.

Re-evaluate.

And continue.

Visa's rules allow it to evaluate acquirers, payment facilitators, third-party agents and merchants within the VAMP framework, reinforcing that portfolio oversight is not simply an isolated merchant concern.

The logical extension is clear:

Transaction monitoring should be connected to continuous merchant understanding.

Product Intelligence Becomes Even More Important in Regulated Commerce

This is particularly relevant for CBD, hemp, peptides, nutraceuticals, kratom, mushrooms, vape, nicotine and other regulated-product categories.

Consider a merchant with:

200 products at underwriting.

All reviewed.

All acceptable.

Merchant approved.

Six months later:

247 products.

What happened?

Traditional transaction monitoring doesn't know.

MCC doesn't know.

KYC doesn't know.

KYB doesn't know.

The merchant application doesn't know.

The original underwriting file certainly doesn't know.

Product Intelligence does.

And if those 47 products include items outside bank policy, problematic claims, missing laboratory evidence or products likely to create consumer dissatisfaction, we now have information that could matter long before the first chargeback arrives.

That's why I believe:

Know Your Merchant is no longer enough.

We increasingly need to Know Your Product.

This Doesn't Replace VAMP. It Makes VAMP More Powerful.

This isn't an argument against transaction monitoring.

Quite the opposite.

Transaction intelligence remains indispensable because consumers and fraudsters ultimately interact with the payment system.

But imagine the power of connecting:

Pre-transaction intelligence

with

Transaction intelligence

with

Post-transaction outcomes.

Now the organization sees the complete lifecycle.

Merchant changes → Product changes → Consumer experience → Transaction behavior → Dispute outcomes

That's how we begin identifying causality rather than simply correlation.

And that's how risk management becomes more proactive.

This Is Where Qredible Sees the Opportunity

At Qredible, we've spent years focused on understanding merchants beyond their applications.

That means connecting:

Merchant Intelligence™

Product Intelligence™

Website Intelligence

Regulatory Intelligence

Policy Intelligence

Continuous Monitoring

The opportunity now is to connect those upstream intelligence layers with the payment-performance signals processors already possess.

Not to replace their fraud platforms.

Not to replace Visa.

Not to replace Mastercard.

Not to replace transaction monitoring.

But to give those systems something they historically haven't had:

Context about what changed before the transaction changed.

That's an important distinction.

The Goal Isn't More Alerts

Payment companies already have plenty of alerts.

The last thing risk teams need is another dashboard producing thousands of red flags.

The objective should be better prioritization.

Imagine a merchant has rising disputes.

That's important.

But now imagine the system also knows:

The merchant changed its refund policy 21 days ago.

Added a subscription program 18 days ago.

Added 42 products 16 days ago.

Changed fulfillment language 14 days ago.

Received increased negative consumer sentiment 10 days ago.

And disputes began increasing seven days ago.

Now the analyst isn't simply looking at a ratio.

They're looking at a story.

That's Merchant Intelligence.

The Medical Analogy Matters

If a patient continually develops headaches, a physician can prescribe pain medication every time the headache appears.

Sometimes that's appropriate.

But eventually, good medicine asks:

Why does this keep happening?

Payment risk needs the same mindset.

A chargeback program manages the symptom.

A fraud-monitoring program detects the symptom.

A threshold tells us how severe the symptom has become.

All are necessary.

But Merchant Intelligence asks:

What is causing it?

And Product Intelligence asks:

Did something inside the commerce change that contributed to it?

Those questions won't always produce an answer.

But when they do, they allow us to treat the underlying condition instead of repeatedly treating the symptom.

The Next Evolution of Payment Risk Is Earlier

The industry's risk infrastructure has historically moved closer and closer to real time.

That's been enormously valuable.

But I believe the next competitive advantage isn't simply getting from:

Yesterday → Today → Real Time.

It's moving from:

Real Time → Before.

Before the dispute.

Before the chargeback.

Before the threshold.

Before the portfolio problem.

Before the consumer harm.

Mastercard is already talking publicly about finding risky merchants earlier and combining network activity with external merchant signals. Visa is explicitly moving toward lifecycle risk management.

Those are important signals about where merchant-risk management is heading.

The opportunity for processors and banks is to go even deeper.

Final Thought

VAMP is forcing the payments industry to pay closer attention to fraud and disputes.

That's a good thing.

But we shouldn't stop at measuring them better.

We should use this moment to ask a more fundamental question:

• What causes merchants to begin moving toward those thresholds in the first place?

Some causes will only be visible in transaction data.

Some will be external fraud attacks.

Some will be impossible to anticipate.

But others may have been visible days, weeks or even months earlier.

• The merchant changed.
• The product changed.
• The marketing changed.
• The business model changed.
• The customer experience changed.
• The fulfillment changed.
• The reputation changed.

Something happened.

And the payment system eventually recorded the consequence.

VAMP measures the smoke.

Merchant Intelligence can help us look for the fire.

The future isn't transaction monitoring or Merchant Intelligence.

It's connecting them.

1. Payment Intelligence tells us what happened.
2. Merchant Intelligence tells us what changed.
3. Product Intelligence tells us what is being sold.
4. Policy Intelligence tells us whether it matters.
5. Human judgment determines what we should do about it.

Connect those layers and we move toward something the industry has wanted for decades:

Not simply detecting risk faster.

Preventing more of it before it becomes a transaction problem.

And perhaps the most important VAMP metric of the future won't be how quickly we respond when a merchant crosses a threshold.

It will be how often our intelligence allows us to intervene before they ever get there.

About Qredible

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 →