How to Prevent Payment Fraud and Chargebacks in Ecommerce

Payment fraud and chargebacks used to be two separate line items on a fraud team’s dashboard. Now they are converging. Sift’s Q2 2026 Digital Trust Index found that overall chargebacks rose 19% year-over-year, and merchants lose $4.61 for every dollar of fraud once fees, fines, and lost merchandise are added up.

This guide walks through what is driving the increase in payment fraud chargebacks and the concrete steps trust and safety professionals can take to prevent it.

Why payment fraud and chargebacks are rising together

While card testing, stolen payment credentials, and account takeover still contribute to real financial losses, the bigger shift over the past two years has been towards ecommerce chargebacks.

The Merchant Risk Council’s 2026 Global eCommerce Payments and Fraud Report found that 62% of merchants reported an increase in first-party misuse disputes, and 57% cited a rise in refund and policy abuse over the past year. Sift’s own data shows first-party fraud made up 36% of all reported fraud in 2024, up from just 15% a few years earlier, and it carries an estimated $132 billion risk to ecommerce merchants globally.

That means a prevention strategy built only to catch stolen cards will miss the fastest-growing source of chargebacks. Fraud teams need controls that stop cybercriminals at checkout and evidence that holds up when a legitimate customer disputes a legitimate order.

Separate true fraud from first-party fraud before you build your strategy

Not every chargeback has the same root cause, and treating them identically wastes resources. True fraud happens when a fraudster uses stolen payment details or a compromised account to make an unauthorized, illegal purchase. Friendly fraud, now often called first-party misuse, happens when the genuine cardholder makes the purchase, then disputes it anyway, whether from confusion, dissatisfaction, or intent to get a free item.

The distinction changes what “prevention” looks like. True fraud is best stopped upstream, before the transaction is authorized, through signal-based detection. First-party misuse cannot always be blocked at checkout because the buyer is who they claim to be. It has to be managed through clear evidence, transparent policies, and smarter dispute response. A mature fraud prevention program budgets time and tooling for both problems separately instead of funneling everything into one fraud queue.

Build a layered defense at checkout

Card-not-present transactions still carry a materially higher chargeback rate than card-present ones, so the checkout flow deserves the most scrutiny in any prevention plan. A layered defense combines several signal types rather than relying on any single rule:

• Device and network signals that flag emulators, proxies, or devices linked to prior fraud
• Behavioral signals, such as typing patterns and navigation speed, that distinguish a rushed automated script from a real shopper
• Velocity checks across the user journey that catch unusual patterns in how many cards, addresses, or accounts touch a single device in a short window

Sift assesses thousands of these signals across the full user journey and aggregates them into a Sift Score, a 1 to 100 rating where 1 indicates a trustworthy transaction and 100 indicates likely fraud. Feeding that score into Payment Protection lets a fraud team set thresholds by risk tier instead of applying the same authentication step to every order, which keeps friction proportional to actual risk.

Apply risk-based friction instead of blanket verification

One of the fastest ways to trade fraud losses for cart abandonment is to add step-up verification or 3D Secure to every transaction. While it reduces fraud, it also reduces conversion, and high-value legitimate customers notice the extra friction first. Risk-based friction solves this by matching the intervention to the risk score in real time. Low-risk orders move through checkout without interruption. Orders that land in a middle risk band get a lightweight challenge, such as a one-time passcode. Only the highest-risk transactions trigger full Verification or a hold for manual review.

Routing those highest-risk orders into Queues, with the supporting signal data attached, lets analysts make a faster and more accurate call than a rule-based system working alone. The goal is not to catch every fraudulent order before authorization, but to simply catch the ones with the clearest signal and let genuine customers through without a fight.

Fight disputes with stronger evidence, not more paperwork

When a chargeback does land, the merchants who win representment are the ones who show up with specific, transaction-level evidence rather than a generic rebuttal letter. Card network programs, including Visa’s Compelling Evidence 3.0 and Mastercard’s dispute rules, reward merchants who can document device data, delivery confirmation, login history, and prior order patterns tied to the disputed transaction. A recent report found that merchants win 45% of re-presented chargebacks, but net only an 18% recovery rate once fees and time are factored in, which shows how much value is lost when evidence is incomplete or arrives late.

Clear billing descriptors, proactive delivery notifications, and an accessible refund process reduce the volume of disputes that reach this stage at all. Many first-party misuse cases start because a cardholder does not recognize a charge or cannot get a fast answer from customer service. Closing that gap before the customer calls their bank is often cheaper than winning the dispute later.

Track the metrics that predict chargeback risk before it shows up on a statement

Card networks monitor merchants against chargeback rate thresholds; they’re commonly cited around 0.65% for Visa’s standard monitoring program, bringing higher fees and closer scrutiny. Waiting for that letter means the prevention strategy has already failed. Track these figures monthly instead:

• Chargeback rate by transaction volume and by dollar value
• Approve rate alongside fraud rate, since a rising approval rate paired with flat fraud losses signals real improvement
• False positive rate, since declining legitimate customers creates its own revenue loss
• Dispute win rate and average time to respond

Reviewing these numbers in Insights inside the Sift Console, and adjusting Workflows as patterns shift, keeps a prevention program responsive instead of static. Fraud patterns change every quarter. A Workflow tuned for last year’s attack pattern will not catch this year’s.

If your fraud team is having trouble with payment fraud and chargebacks, Sift can help. Sift uses advanced machine learning technology to scan for patterns, and stops fraudsters before causing monetary harm to your business. Learn more about it by scheduling a free consultation with our team today.

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