The order was accepted. The merchandise shipped. The customer received the product. Then, days or weeks later, the cardholder disputes the transaction.

At that point, the merchant is trying to recover revenue rather than protect it.

That is why effective chargeback prevention starts much earlier: before a risky order leaves the warehouse.

The distinction is becoming increasingly important. Mastercard projects global chargebacks will reach approximately 324 million annually by 2028, up 24% from 2025 levels. Merchants in its research identified 45% of their chargebacks as fraudulent.

At the same time, chargeback risk is expanding beyond traditional stolen-card fraud. The Merchant Risk Council’s 2026 Global eCommerce Payments & Fraud Report found that 62% of merchants reported an increase in first-party misuse disputes, while refund and policy abuse remains a significant post-purchase problem.

For Shopify merchants, reducing chargebacks requires understanding what is causing them, identifying risky behavior early and deciding which orders should actually be fulfilled.

What is a chargeback?

A chargeback occurs when a cardholder disputes a transaction with their bank and the payment is reversed through the card network.

Unlike a normal refund, the customer does not necessarily resolve the issue directly with the merchant.

Chargebacks can occur for many reasons, including:

• A stolen credit card was used

• The cardholder does not recognize the transaction

• A customer claims an order never arrived

• A customer says an item was damaged or not as described

• A recurring payment was not canceled as expected

• A customer disputes a legitimate purchase

• The merchant made a billing, fulfillment or customer-service error

Shopify recommends that merchants review their chargebacks by reason because different causes require different prevention strategies.

That is an important point: not every chargeback is the same type of fraud, and not every chargeback is fraud at all.

True fraud vs. friendly fraud vs. refund abuse

Understanding the source of the dispute is the first step toward preventing it.

True fraud

True fraud generally involves an unauthorized transaction.

For example, a fraudster obtains stolen payment credentials, purchases a product and ships it to themselves or another destination. When the legitimate cardholder discovers the transaction, they dispute it.

If the merchandise has already shipped, the merchant can potentially lose both the product and the payment.

Shopify specifically recommends reviewing suspicious and high-risk orders before fulfillment to reduce this exposure.

Friendly fraud or first-party misuse

Friendly fraud occurs when the actual cardholder disputes a transaction that they, or someone they authorized, made.

Sometimes the dispute is accidental. A customer might:

• Forget making the purchase

• Fail to recognize the merchant descriptor

• Have a spouse or family member make the purchase

• Contact their bank instead of the merchant

Other cases are intentional.

A customer might receive a product and then claim the transaction was unauthorized or that the merchandise never arrived.

The industry increasingly refers to intentional forms of this behavior as first-party misuse.

This is becoming a larger merchant problem. The Merchant Risk Council reported that a majority of ecommerce merchants are seeing increased first-party misuse disputes.

Refund and policy abuse

Not every costly form of customer abuse becomes a traditional payment chargeback.

Customers can also exploit merchant refund, return and promotional policies.

Examples include:

• Claiming an item never arrived when it did

• Returning a used product

• Returning a different product

• Manipulating shipment or tracking information

• Repeatedly requesting refunds

• Creating multiple accounts to abuse promotions

• Circumventing purchase or quantity limits

The 2026 MRC report found refund and policy abuse was the most commonly reported fraud type, affecting 41% of surveyed merchants. Among merchants experiencing this abuse, false claims that goods were not received were the most frequently cited pattern.

These behaviors matter because chargeback prevention should not be isolated from broader customer-abuse detection.

The same customer behavior that leads to repeated refunds today can become a payment dispute tomorrow.

Merchant error

Some chargebacks are preventable without sophisticated fraud detection.

A customer might dispute a transaction because:

• The billing descriptor is confusing

• Shipping took longer than expected

• Tracking information was inaccurate

• The product was substantially different from its description

• A subscription was not canceled properly

• The customer could not reach support

Good fraud prevention therefore works alongside clear policies, reliable fulfillment, recognizable billing descriptors and responsive customer service.

Shopify recommends all of these practices as part of reducing chargebacks.

Fighting a chargeback is not the same as preventing one

Once a chargeback is opened, a merchant can submit evidence.

That evidence might include:

• Order details

• Customer communications

• Billing information

• IP information

• Shipping records

• Tracking and delivery confirmation

• Proof that the customer previously used the account

• Documentation showing the merchant followed its policies

Strong evidence can improve the chances of successfully challenging a dispute.

But that is chargeback management, not chargeback prevention.

The merchant has already spent time fulfilling the order and now must spend additional time investigating and responding to the dispute.

And winning is never guaranteed. Shopify notes that although merchants can submit evidence, the decision to reverse the payment ultimately rests with the cardholder’s issuing bank.

Preventing a bad transaction from reaching fulfillment changes the economics completely.

Instead of asking:

How do we recover this transaction?

The merchant asks:

Should we ship this order in the first place?

The real cost of a chargeback goes beyond the transaction

A $200 fraudulent order does not necessarily cost a merchant only $200.

The merchant might also lose:

• The merchandise

• Shipping and fulfillment costs

• Payment processing costs

• Chargeback or dispute fees

• Employee time spent investigating

• Customer-service time

• Inventory availability

• Marketing acquisition spend associated with the order

There can also be broader consequences if chargeback rates become too high.

Shopify warns that fulfilling high-risk orders can contribute to elevated chargeback levels, which can ultimately lead to payment-processing issues or removal from Shopify Payments.

The broader economics of fraud reinforce this point.

The 2026 LexisNexis True Cost of Fraud Study found that U.S. retail and ecommerce businesses incur approximately $5.13 in total costs for every $1 of direct fraud loss. More than half of U.S. merchants surveyed also reported increased customer churn associated with anti-fraud measures, illustrating why merchants need to reduce fraud without creating unnecessary friction for legitimate buyers.

The objective is therefore not simply to block more transactions.

It is to make better decisions about which transactions to trust.

Warning signs to review before shipping an order

No single signal automatically means an order is fraudulent.

A legitimate customer can ship a gift to another address. Someone traveling can place an order from an unusual location. A loyal customer can suddenly make a much larger purchase.

Risk becomes more meaningful when multiple signals begin to tell the same story.

Before fulfilling a suspicious order, merchants should consider factors such as:

Billing and shipping inconsistencies

Does the shipping information make sense in the context of the buyer?

A mismatch is not proof of fraud, but it can become more significant when combined with other unusual behavior.

IP and geographic inconsistencies

Does the location from which the order was placed align with the billing, shipping or customer information?

Multiple payment attempts

Repeated attempts using different cards can indicate card testing or an attempt to find valid stolen payment credentials.

Unusual order velocity

Has the buyer placed multiple orders within a short period?

Are multiple accounts placing orders to the same address, device or identity?

New or suspicious identity information

Look at the broader consistency of the email, phone, address and other information associated with the buyer.

Unusual purchase behavior

A customer’s basket can provide useful context.

A suddenly large order, unusually high quantities of easily resold products or purchasing behavior inconsistent with normal customers may warrant additional review.

Previous refunds, disputes or returns

A transaction can look normal when viewed in isolation.

The buyer’s history might tell a different story.

Repeated refunds, returns, delivery claims, disputes or attempts to circumvent merchant policies can indicate a broader abuse pattern.

Shopify’s fraud analysis already provides merchants with information such as AVS results, CVV checks, IP details and unusual purchasing activity. Third-party fraud tools can add additional identity, behavioral and relationship context to the order.

Approve, review, hold or cancel

Chargeback prevention does not mean automatically canceling anything that looks unusual.

A better approach is to create a clear decision workflow.

Approve

The evidence indicates the customer and transaction are likely legitimate.

The order proceeds to fulfillment.

Review

Something about the transaction is unusual, but there is not enough evidence to conclude that the order is fraudulent.

The merchant reviews the supporting risk information or verifies the customer.

Hold

The order presents enough risk that it should not proceed to fulfillment until the merchant has completed additional verification.

For merchants using appropriate payment configurations, manual payment capture can also provide additional time to review suspicious orders before capturing funds.

Cancel

The available evidence indicates the transaction presents an unacceptable level of fraud or abuse risk.

The order is canceled before the merchant ships the merchandise.

Shopify Flow can also automate portions of this process, including holding or canceling high-risk orders and capturing payments on lower-risk transactions.

The key is making the decision before fulfillment whenever possible.

Why automatically canceling every high-risk order can backfire

Stopping fraud matters.

So does approving legitimate revenue.

An aggressive prevention strategy that automatically cancels every unusual order can create another expensive problem: false positives.

A high-value order is not inherently fraudulent.

Neither is an international customer, an address mismatch or a new device.

The more useful question is whether the combination of identity, payment, device, location, historical and behavioral information supports the transaction.

This is why contextual fraud detection can provide an advantage over relying only on individual rules.

Merchants need enough evidence to confidently stop risky transactions without unnecessarily rejecting good customers.

Can chargeback protection or insurance solve the problem?

Chargeback protection can be useful, but merchants should understand what is being protected.

Programs such as Shopify Protect can protect certain eligible Shop Pay transactions against qualifying fraudulent and unrecognized chargebacks when program requirements are met.

Other providers may offer chargeback guarantees or similar financial protections.

These services can reduce the merchant’s financial exposure to covered disputes.

But coverage and prevention solve different problems.

A guarantee may absorb the financial loss associated with an eligible chargeback. It does not necessarily identify:

• Refund abuse

• Return abuse

• Promotion abuse

• Reseller activity

• Account relationships

• Suspicious customer behavior

• Other transactions that fall outside the protection criteria

The underlying customer or fraud pattern can therefore continue even when an individual chargeback is financially covered.

For merchants trying to understand who they should trust, prevention remains important.

Chargeback alerts and dispute management are still valuable

Prevention will never eliminate every chargeback.

Merchants should still have processes for:

• Receiving dispute notifications

• Responding quickly

• Collecting supporting evidence

• Tracking delivery

• Maintaining customer communications

• Understanding dispute reason codes

• Monitoring their overall chargeback rate

Real-time dispute alerts can sometimes allow merchants and issuers to resolve a problem before it progresses further through the chargeback process.

Mastercard reported in 2026 that approximately 60% of payment disputes escalate into chargebacks, highlighting the opportunity to resolve disputes earlier when possible.

But dispute tools operate after the transaction has occurred.

They are the second line of defense.

Order-level fraud prevention is the first.

A better chargeback-prevention strategy for Shopify

An effective Shopify chargeback strategy should operate across the entire order lifecycle.

Before fulfillment

Evaluate the transaction, identity and buyer behavior.

Review suspicious orders and decide whether to approve, hold or cancel them.

During fulfillment

Use reliable shipping methods, accurate tracking and delivery confirmation.

Keep customers informed about delays.

After delivery

Watch for unusual refund, return and customer-service patterns.

Make it easy for legitimate customers to resolve problems directly with your store.

When a dispute occurs

Respond with clear, organized evidence and learn from the result.

Then feed that information back into future order decisions.

The goal is a continuous process:

Detect → Decide → Act → Learn

Each chargeback, refund, return and review decision should make the merchant better at identifying the next risky transaction.

How Alogram helps prevent chargebacks before fulfillment

Alogram AI Payment Fraud Blocker is designed to help Shopify merchants identify risky behavior before an order ships.

Alogram evaluates multiple types of intelligence around the transaction, including:

• Buyer behavior

• Identity information

• Device and location intelligence

• Transaction characteristics

• Billing and shipping inconsistencies

• Suspicious relationships and patterns

• Refund and return behavior

• Reseller activity

• Promotion abuse

Instead of simply telling a merchant that something looks unusual, Alogram provides explainable order-level evidence to help determine whether an order should be:

Approved. Reviewed. Held. Or canceled.

This can work alongside Shopify’s existing fraud analysis and merchant workflows rather than requiring merchants to replace the tools they already use.

The objective is straightforward:

Identify risky behavior before inventory leaves the warehouse.

Final thoughts

Chargebacks are often treated as a problem that begins when the merchant receives a dispute notification.

By then, the merchant may already have lost the most important opportunity to prevent the loss.

The better question happens earlier:

Should we fulfill this order?

For Shopify merchants, effective chargeback prevention means combining fraud analysis, behavioral and identity intelligence, targeted automation, strong fulfillment practices and post-purchase monitoring.

Some chargebacks will still happen.

Some should be fought with strong evidence.

Some may be covered by chargeback-protection programs.

But when fraud or abuse can be identified before fulfillment, preventing the risky order in the first place is usually better than trying to recover the loss afterward.

Sources

Shopify, Fraud analysis

Shopify, Preventing fraud

Shopify, Managing high-risk orders with Shopify Flow

Shopify, Shopify Protect

Merchant Risk Council, 2026 Global eCommerce Payments & Fraud Report

LexisNexis Risk Solutions, 2026 True Cost of Fraud

Mastercard, updated global chargeback outlook

Mastercard, dispute resolution and chargeback alerts

Shopify App Store, Alogram Payment Fraud Blocker

The Fraud Brief explores fraud, payments, identity and risk in a world increasingly shaped by AI.

Written by d’Artagnan Osborne, Founder of Alogram, where we are building real-time fraud and payment risk decisioning for modern commerce.

A suspicious order might have a billing and shipping mismatch. A buyer might try several cards before completing a purchase. A merchant may encounter first-party misuse or refund and policy abuse after the sale. Other buyers may attempt to exploit promotions, accounts or store policies.

A Shopify fraud filter can help identify or block some of this activity. But “fraud filter” can mean several different things today.

Shopify’s former Fraud Filter app is no longer available. In its place, merchants now have several layers of fraud prevention: Shopify’s built-in fraud analysis, Fraud Control checkout rules, Shopify Flow and third-party fraud apps.

Those tools do not all work the same way.

Fraud Control applies merchant-defined rules. Shopify Flow automates actions. And Shopify’s own fraud recommendations are powered by machine-learning algorithms.

That distinction matters as merchants face a broader mix of payment fraud and customer abuse. The 2026 Global eCommerce Payments & Fraud Report, based on responses from 1,278 merchant professionals across 37 countries, found that respondents reported losing an average of 3.2% of annual ecommerce revenue to payment fraud globally. It also found that 62% reported an increase in first-party misuse disputes, while 57% reported an increase in refund or policy abuse.

This guide explains how Shopify’s current fraud tools work, where merchant-defined rules are useful, where they have limitations and how contextual fraud detection can add another layer of information before an order is fulfilled.

What is a Shopify fraud filter?

Today, “Shopify fraud filter” is best understood as a broad description rather than the name of one current Shopify product.

It can describe tools that help merchants:

• Block a known email address or IP address

• Reject checkouts that match specific conditions

• Flag unusual payment activity

• Hold high-risk orders before fulfillment

• Automate actions based on fraud risk

• Analyze identity, behavior, location and transaction information

Some controls operate during checkout and can prevent a checkout from becoming an order.

Others analyze an order after checkout and provide information that helps a merchant decide whether to fulfill, review, hold or cancel it.

That difference is important.

A rule that blocks a known IP address solves a different problem from a fraud system that evaluates the overall risk of an order using multiple pieces of context.

What happened to Shopify’s original Fraud Filter app?

Shopify’s former Fraud Filter app is no longer available in the Shopify App Store.

If you encounter older articles referring to it, Shopify’s former Fraud Filter Help Center URL now redirects to its guidance for managing high-risk orders with Shopify Flow.

There is not one direct replacement.

Today, Shopify merchants can use a combination of:

• Shopify Fraud Analysis

• Fraud Control

• Shopify Flow

• Manual payment and fulfillment controls

• Third-party fraud prevention apps

Each serves a different purpose.

Shopify’s built-in fraud analysis

Shopify’s built-in fraud analysis is designed to evaluate eligible online credit-card orders when Shopify can verify the payment.

The analysis provides fraud indicators that merchants can use when investigating an order.

Depending on the transaction, those indicators can include:

• Address Verification System, or AVS, results

• Whether the correct card security code was provided

• Information about the IP address used to place the order

• Whether the customer attempted to use more than one credit card

Where fraud recommendations are available, Shopify can classify an order as having a low, medium or high risk of a chargeback due to fraud.

There is an important eligibility detail. Shopify states that stores on Grow or higher, or stores using Shopify Payments on any plan, can receive Shopify fraud recommendations. Basic stores that do not use Shopify Payments receive fraud indicators and support for third-party fraud apps, but not Shopify’s fraud recommendations.

And Shopify’s fraud recommendations are not simply a collection of static rules.

Shopify says its recommendations are powered by machine-learning algorithms trained on historical transactions across Shopify stores, and that those algorithms are continuously improved.

That means the useful comparison is not “Shopify versus AI.”

Shopify already uses machine learning.

The more useful distinction is between merchant-defined rules, workflow automation, and contextual or model-based fraud detection.

Shopify advises merchants to review high-risk orders before fulfillment because fulfilling them can increase chargeback exposure and, in serious cases, affect payment processing.

A high-risk recommendation also does not automatically require cancellation. Depending on the circumstances, a merchant might verify the order, hold it, cancel it or refund it.

Shopify Fraud Control

Fraud Control is Shopify’s fraud analytics and checkout-rules app.

Its dashboard provides fraud-related metrics, while its checkout rules let merchants create controls using information such as:

• Email addresses

• IP addresses

• Address attributes

• Combinations of checkout conditions

There is an important restriction: Fraud Control checkout rules are only available to merchants using Shopify Payments.

Unlike Shopify’s machine-learning fraud recommendation, Fraud Control checkout rules act on conditions defined by the merchant.

For example, a merchant can create a rule around a known IP address or combine multiple checkout conditions. If a checkout matches a blocking rule, Fraud Control can prevent it from becoming an order.

That makes rules useful for known and repeatable activity.

But Shopify also warns merchants to configure Fraud Control carefully because broad rules can prevent legitimate customers from checking out. Shopify also makes clear that checkout rules do not guarantee that chargebacks will not occur.

Shopify Flow

Shopify Flow is an automation platform.

It allows merchants to build workflows using triggers, conditions and actions.

For fraud-related use cases, Shopify documents workflows that can:

• Capture payment when an order is not high risk

• Cancel and restock a high-risk order

• Send an internal notification

• React to known bad email addresses

• Take action on customers with frequent return activity

Flow can reduce repetitive manual work and help merchants apply a consistent response.

But Flow and fraud detection are not the same thing.

Flow acts on the triggers, conditions and risk information available to the workflow. In other words, it can automate the response to a risk signal without necessarily being the system that generated that signal.

How rules-based fraud filters work

A rules-based fraud filter follows a simple model:

If a defined condition occurs, take a defined action.

A merchant could create logic such as:

• If an email address matches a confirmed blocklist, block the checkout.

• If a known abusive customer attempts another purchase, send the order for review.

• If specific checkout conditions occur together, hold the order.

• If a customer meets a defined return-abuse threshold, trigger a workflow.

Rules are valuable because they are predictable.

The merchant knows what condition was matched and what action should follow.

They work especially well for known situations such as:

• Confirmed fraudulent email addresses

• Known abusive customers

• Specific IP addresses

• Restricted destinations

• Store-specific purchasing restrictions

• Previously identified fraud patterns

But fraud does not always repeat in exactly the same form.

Where static fraud filters fall short

A rule can only respond to the conditions it was designed to recognize.

If a merchant blocks one fraudulent email address, the buyer can use another. IP addresses can change, and Shopify itself advises merchants to consider the possibility of proxy-service IP addresses.

At the same time, a rule that is too broad can create a different problem: blocking legitimate customers.

The financial tradeoff is significant.

The 2026 LexisNexis True Cost of Fraud Study found that U.S. merchants incur approximately $5.13 in total cost for every $1 of direct fraud loss. The study also reported that 54% of U.S. ecommerce merchants experienced increased customer churn linked to anti-fraud measures.

The objective, then, is not simply to block more transactions.

It is to stop meaningful risk while minimizing unnecessary friction for legitimate buyers.

1. Fraudsters can change individual details

Emails, devices, IP addresses and other transaction information can change from one attempt to another.

Blocking a single identifier can stop one attempt without necessarily identifying a broader pattern.

2. A single signal can be misleading

A billing and shipping mismatch might indicate elevated risk.

It might also be a gift.

A high-value order can be unusual without being fraudulent. An international IP address might warrant additional review without independently proving anything about the buyer.

The better question is not simply:

Is one part of this order unusual?

It is:

What does the combination of available information tell us about the risk of this order?

3. Rules need periodic review

Merchant-defined rules should be revisited as fraud patterns, customer behavior and store policies change.

Otherwise, rules can become outdated, overlap with one another or continue blocking activity that no longer represents the same level of risk.

4. Broad rules can reject good customers

A rule is deterministic.

If the condition matches, the action happens.

That is useful when the condition is highly reliable. It becomes more problematic when the signal has multiple legitimate explanations.

How contextual AI fraud detection is different

Machine-learning and AI-based fraud systems can evaluate patterns across multiple signals instead of relying entirely on a single merchant-defined condition.

The exact information analyzed varies by product.

Depending on the system, relevant signals can include categories such as:

• Buyer identity

• Device or network context

• Geographic information

• Purchasing behavior

• Transaction information

• Historical activity

• Relationships between pieces of transaction data

Consider an order with different billing and shipping addresses.

A static rule could be configured to block the order immediately.

A contextual fraud system can potentially evaluate additional questions:

• Is the buyer information internally consistent?

• Are other transaction signals unusual?

• Has the buyer attempted multiple payment cards?

• Does the location information make sense?

• Is the behavior consistent with legitimate transactions?

• Are several otherwise small risk indicators appearing together?

The objective is not to assume every unusual transaction is fraudulent.

It is to determine whether unusual activity is supported by enough additional context to represent meaningful risk.

Rules-based filters vs. contextual fraud detection

Should every high-risk Shopify order be automatically canceled?

Not necessarily.

Shopify itself presents several possible responses to a high-risk order rather than instructing merchants to automatically cancel every one.

When evidence is conclusive, automatic action can make sense.

When the evidence is less certain, holding an order for review may be preferable to either fulfilling it immediately or automatically rejecting a legitimate customer.

This becomes increasingly important as dispute volume grows.

In a June 2026 update, Mastercard projected that global chargeback volume will grow 37% from 2025 to 2029, reaching approximately 359 million transactions annually. Mastercard also reports that its 2025 data showed fraudulent chargebacks accounting for about 45% of merchant chargeback volume globally.

A practical risk workflow might include four outcomes:

Approve

The available evidence supports fulfilling the order.

Review

Something is unusual and the merchant needs additional information before deciding.

Hold

Fulfillment is temporarily paused while the order is investigated.

Cancel

The available evidence indicates that the fraud or abuse risk is unacceptable.

Merchants can also use manual payment capture to create additional review time before the payment is captured. The distinction matters because payment authorization can occur before capture.

A better Shopify fraud-filter strategy

1. Start with Shopify’s fraud information

Review Shopify’s recommendation where available, but also examine the individual indicators associated with the order.

A low, medium or high label is useful. The evidence behind it is useful too.

2. Use rules for known bad activity

Rules are well suited for highly specific conditions that you already understand.

Keep them targeted.

The broader the condition, the greater the chance that legitimate buyers will match it.

3. Evaluate multiple signals together

An address mismatch or unusual order value should not automatically determine the outcome.

Look at how the available information relates.

Risk is often more meaningful in combination than in isolation.

4. Make the decision before fulfillment

Shopify specifically recommends reviewing high-risk orders before fulfillment.

Once a disputed order has already been fulfilled, the merchant can face lost merchandise or service value in addition to dispute-related costs.

5. Track what happened

Record whether reviewed orders ultimately resulted in fulfillment, cancellation, refunds, disputes or confirmed abuse.

That gives merchants better information for deciding whether individual rules and review processes remain useful.

Going beyond a basic Shopify fraud filter

Alogram Payment Fraud Blocker is designed to help Shopify merchants evaluate risky orders before they ship.

Alogram analyzes behavioral, identity, geographic and transaction signals and presents order-level insights inside Shopify Admin.

The goal is to give merchants more context for deciding whether an order should be:

• Approved

• Reviewed

• Held

• Canceled

That additional context can also help merchants investigate activity that extends beyond traditional stolen-card fraud, including refund abuse, promotion abuse, reseller activity and suspicious buyer behavior.

This broader view matters because ecommerce risk increasingly continues beyond the original payment.

The National Retail Federation’s 2025 Retail Returns Landscape estimated that 19.3% of online sales would be returned in 2025 and found that 9% of all returns were fraudulent. It also reported that 45% of surveyed shoppers considered bending the rules when making a return acceptable.

Alogram is designed to help merchants add context to these decisions rather than automatically treating every unusual customer or transaction as fraud.

Final thoughts

Shopify merchants no longer have one product called Fraud Filter that represents the entire fraud-prevention stack.

Instead, Shopify provides several layers.

Fraud Control can apply merchant-defined checkout rules.

Shopify Fraud Analysis can provide indicators and machine-learning-based fraud recommendations for eligible orders.

Shopify Flow can automate actions.

And third-party fraud apps can add additional indicators, contextual analysis and actions.

Rules-based filtering answers a straightforward question:

Did this order match a condition we already defined?

Contextual fraud detection addresses a different question:

What does the available identity, behavior and transaction context tell us about the risk of this order?

For many Shopify merchants, the better strategy is not choosing one or the other.

It is using targeted rules for known problems, Shopify’s native fraud capabilities for the signals they provide and additional contextual analysis where the business needs a more complete view before fulfillment.

Frequently asked questions

Does Shopify still have a Fraud Filter app?

No. Shopify’s former Fraud Filter app is currently unavailable in the Shopify App Store.

Its former Help Center URL now redirects to Shopify’s guidance for managing high-risk orders with Shopify Flow.

What replaced Shopify Fraud Filter?

There is no single one-for-one replacement.

Relevant functionality is now distributed across Shopify Fraud Analysis, Fraud Control, Shopify Flow, manual payment and fulfillment controls, and third-party fraud apps.

Does Shopify use AI or machine learning for fraud detection?

Yes.

Shopify states that its fraud recommendations are powered by machine-learning algorithms trained on historical transactions across Shopify stores and that those algorithms are continuously improved.

That is separate from merchant-defined Fraud Control rules and Shopify Flow automation.

How does Shopify identify a high-risk order?

Shopify’s fraud analysis can consider indicators such as AVS results, CVV correctness, IP information and whether the buyer attempted to use more than one credit card.

For eligible merchants, Shopify can also provide a low, medium or high fraud-risk recommendation.

Should I automatically cancel every Shopify order marked high risk?

Not necessarily.

Shopify documents multiple possible responses to high-risk orders, including additional verification, cancellation and refunding. Merchants can also use fulfillment holds, Shopify Flow and manual payment capture to create additional review steps.

The appropriate action depends on the evidence available and the merchant’s risk tolerance.

Sources

Shopify, Fraud analysis

Shopify, Fraud Control app

Shopify, Managing high-risk orders with Shopify Flow

Shopify App Store, Fraud Filter

Merchant Risk Council, 2026 Global eCommerce Payments & Fraud Report

LexisNexis Risk Solutions, 2026 True Cost of Fraud

Mastercard, updated global chargeback outlook

National Retail Federation, 2025 Retail Returns Landscape

Shopify App Store, Alogram Payment Fraud Blocker

The Fraud Brief explores fraud, payments, identity and risk in a world increasingly shaped by AI.

Written by d’Artagnan Osborne, Founder of Alogram, where we’re building real-time fraud and payment risk decisioning for modern commerce. Learn more about Alogram for Shopify.

Many attacks are not fundamentally new.

What has changed is how quickly attackers can repeat them, alter one data point, observe the result, and try again.

A different email. A new device. A slightly modified address. Another card. Another identity.

Each transaction may look unrelated on its own. Together, they reveal an attacker rapidly testing the boundaries of the fraud system.

Fast scoring is not fast learning!

Commercial fraud platforms often describe themselves as real time because they can return a risk score in milliseconds.

But the model behind that score may still take days or weeks to learn from a new attack.

It may depend on confirmed fraud outcomes, chargebacks, analyst investigations, new rules, retraining, validation, and deployment.

Those controls exist for good reasons. Fraud models should not rewrite themselves after every suspicious event.

But the mismatch is becoming difficult to ignore:

The AI attacker can adapt after every attempt. The defensive AI model often cannot.

The real fraud-speed gap

Attackers are increasingly operating a rapid optimization loop:

• Change one variable

• Submit another transaction

• Observe the response

• Repeat until something works

Meanwhile, many fraud models are still learning from yesterday’s confirmed outcomes.

The transaction is evaluated in milliseconds. The lesson from that transaction may reach the model much later.

That delay is the fraud-speed gap.

AI must help train the fraud AI

The answer is not uncontrolled autonomous retraining.

The answer is using AI to compress the path between a new attack being observed and the defense being safely updated.

AI should help connect related attempts, identify which variables are changing, recognize emerging patterns, propose new signals, and test improvements before controlled deployment.

The goal is simple:

Keep human oversight and model governance, but move the learning loop much closer to the speed of the attack.

Learning speed is the next advantage

Fraud systems should be measured across two dimensions:

Decision speed: How quickly can the system score the current transaction?

Learning speed: How quickly can it understand a new attack and improve the next decision?

The industry has spent years optimizing the first.

The next generation of fraud defense will be defined by the second.

The smartest model may not win. The fastest safe learner might.

Where does your fraud operation lose the most time today: confirming outcomes, investigating patterns, retraining models, or deploying new controls?

At Alogram, we are focused on helping Shopify merchants move from isolated order review to explainable, lifecycle-aware fraud decisioning. For merchants seeing suspicious checkouts, failed payment attempts, risky orders, chargebacks, refund abuse, or repeat fraud patterns, our Shopify app is built to help identify those patterns earlier and support faster, clearer action.

Learn more about Alogram for Shopify.

Written by d’Artagnan Osborne, Founder of Alogram, where we are building real-time fraud and payment risk decisioning for modern commerce.

The fraud merchants are dealing with today often starts before an order exists. It shows up as fake abandoned checkouts, failed payment attempts, card-testing bots, fake customer accounts, polluted analytics, damaged email flows, refund abuse, and chargebacks that arrive long after the package has shipped.

In other words, fraud has moved beyond the order.

That shift matters because many ecommerce fraud tools were built around a single moment: the transaction. A customer places an order, a system scores the risk, and the merchant decides whether to approve, review, cancel, or fulfill.

But modern fraud is increasingly attacking the full commerce workflow.

It is not just the payment. It is the checkout path. It is the abandoned cart flow. It is the email system. It is the return policy. It is the refund process. It is the dispute evidence. It is the repeated behavior that only becomes obvious when you connect the dots across time.

That is the new fraud surface.

The old model was order-centric

Traditional ecommerce fraud prevention was built around the order.

A customer checks out. The fraud system looks at signals like billing address, shipping address, IP address, order value, velocity, payment method, and prior chargeback history. Then the merchant receives a recommendation.

Approve.

Review.

Decline.

That model still has value. Merchants still need to identify risky orders before they ship. They still need to avoid chargebacks. They still need to protect revenue and reduce manual review.

But the order-centric model assumes there is an order to evaluate.

That assumption is becoming a problem.

A growing portion of merchant pain now happens before a legitimate order exists. Fraudsters are not always trying to buy the product. Sometimes they are using the store as infrastructure.

They may be testing stolen cards. They may be creating fake customer accounts. They may be generating abandoned checkouts. They may be probing payment flows. They may be rotating names, emails, addresses, and IPs to see what gets through.

The merchant is left with the mess.

No sale.

No real customer.

No clean data.

Just failed payments, fake carts, bad analytics, support confusion, and a store that now has to separate real buyer activity from automated abuse.

The new fraud surface starts before the order

One of the clearest patterns merchants are discussing today is checkout abuse.

A store sees a sudden wave of abandoned checkouts. The names look fake. The emails bounce. The payment attempts fail. The addresses repeat with small changes. The behavior does not look like normal shopping.

That is often not a marketing problem. It is not a conversion problem. It is not necessarily a product problem.

It can be card testing.

In a card-testing attack, fraudsters use automated scripts to test stolen card numbers against checkout pages. They are not interested in the merchant’s products. They are interested in whether the card authorizes.

That distinction is important.

If the attacker is using checkout as a testing endpoint, then product-page behavior may not tell the full story. Storefront traffic tools may miss part of the activity. Traditional order review may come too late because many of the attempts never become successful orders.

But the damage still happens.

Abandoned checkout data becomes unreliable.

Email recovery flows get triggered for fake customers.

Marketing analytics become harder to trust.

Payment attempts create noise.

Staff waste time investigating activity that was never a real buyer.

Good customers can become harder to see because the signal is buried under bot behavior.

This is why merchants are frustrated. They are not only asking, “How do I stop a fraudulent order?”

They are asking, “How do I stop my store from being used this way in the first place?”

That is a very different problem.

The damage is not only chargebacks

Chargebacks are still painful. They are direct, measurable, and easy to understand.

The merchant loses the sale. The product may already be gone. Shipping may be gone. Fees may apply. The dispute process takes time. The bank may side with the customer even when the merchant believes the evidence is strong.

But modern ecommerce fraud creates damage before the chargeback ever appears.

It damages the operating system of the store.

If bots create thousands of abandoned checkouts, the merchant’s abandoned cart metrics become less useful. If fake emails enter recovery flows, sender reputation can suffer. If fraudulent customers create accounts, customer data becomes polluted. If fake payment attempts spike, the merchant has to figure out whether the issue is fraud, payment configuration, checkout friction, or something else.

This is why I think the industry needs to stop treating fraud as only a transaction problem.

Fraud is now an operational problem.

It affects analytics.

It affects customer communication.

It affects fulfillment decisions.

It affects refund policies.

It affects dispute handling.

It affects staff time.

It affects how much friction a merchant feels forced to add for everyone else.

When merchants cannot trust the activity moving through their store, every workflow becomes harder.

Fraud continues after the order ships

The other side of the problem happens after checkout.

An order can look acceptable at payment and still become risky later.

The customer may request a refund with suspicious timing. A return may come back damaged, empty, or inconsistent with the claim. A buyer may say the package never arrived even when tracking says it did. A customer may receive a refund and then file a chargeback. A repeat buyer may show a pattern of refund requests, return abuse, delivery claims, or disputes across multiple orders.

This is where fraud prevention often falls short.

A checkout score is a snapshot. It tells the merchant something about the transaction at that moment.

But fraud is not always visible at that moment.

Sometimes the risk appears in the sequence.

The timing.

The repetition.

The mismatch between behavior and claim.

The history across refunds, returns, support tickets, and disputes.

That is why post-purchase fraud is becoming such an important part of the conversation. Refund abuse, return abuse, friendly fraud, and chargebacks are not separate from payment fraud. They are part of the same lifecycle.

If a merchant only evaluates risk at checkout, they may miss the pattern that develops after the order.

AI and automation make this harder

AI did not create fraud. Automation did not create fraud.

But both make fraud easier to scale.

Attackers can generate fake names, rotate emails, script checkout attempts, test cards faster, create more convincing messages, and manipulate evidence with less effort than before.

That changes the economics.

A fraudster does not need every attempt to work. They can run many attempts, learn from failures, and keep adjusting.

Merchants, on the other hand, have to protect conversion. They cannot simply block everything. They cannot add endless friction. They cannot manually review every strange cart, every failed payment, every refund request, and every chargeback.

That imbalance is the real issue.

Modern fraud is becoming faster, cheaper, and more automated.

Merchant response is still too often manual, fragmented, and reactive.

That gap is where the losses happen.

What merchants need now

The next generation of ecommerce fraud prevention needs to move beyond isolated order scoring.

It needs to understand the full commerce lifecycle.

That means looking at signals across:

• Checkout behavior

• Failed payment attempts

• Account creation patterns

• Abandoned cart anomalies

• Email and identity signals

• Shipping and address repetition

• Order timing and velocity

• Refund behavior

• Return patterns

• Delivery claims

• Chargeback history

• Merchant-specific risk policies

The goal is not to block more customers.

The goal is to make better decisions earlier.

Good fraud prevention should help merchants understand what is happening, why it is risky, and what action makes sense. Sometimes the right action is to block. Sometimes it is to hold fulfillment. Sometimes it is to request verification. Sometimes it is to monitor a pattern. Sometimes it is to approve the order and avoid unnecessary friction.

The key is explainability.

Merchants do not just need a score. They need to know what the score means.

They need to know whether the issue is payment risk, identity risk, address repetition, refund behavior, velocity, or a pattern that has appeared across multiple transactions.

A black-box score is not enough when the fraud surface has expanded across the entire store.

The practical question for Shopify merchants

For Shopify merchants, the practical question is no longer only:

Which orders are high risk?

The better question is:

Where is fraud showing up across my store?

Is it happening before checkout?

Is it happening through failed payment attempts?

Is it creating fake abandoned carts?

Is it tied to repeat addresses, repeat customers, or repeat refund behavior?

Is it showing up after fulfillment?

Is it becoming chargebacks?

Is it affecting analytics, email flows, staff time, or payment risk?

That broader view matters because fraud rarely stays in one lane.

A card-testing pattern may start as failed payments and abandoned carts. A refund abuse pattern may later become chargebacks. A repeat address pattern may look harmless order by order, but obvious when viewed across time.

Merchants need tools that can connect those signals.

Not just to stop fraud, but to operate with more confidence.

The modern solution

Shopify merchants need to move from isolated order review to explainable, lifecycle-aware fraud decisioning.

The goal is not to create more friction for good customers.

The goal is to help merchants identify risky patterns earlier, understand why something looks suspicious, and take action before fraud spreads across payments, fulfillment, refunds, returns, disputes, analytics, and customer communication.

For modern merchants, fraud prevention cannot be limited to one transaction at one moment.

It has to follow the behavior.

It has to explain the risk.

It has to support the workflow.

The next wave of ecommerce fraud is not only about stolen cards or suspicious orders. It is about automated checkout abuse, fake accounts, polluted abandoned-cart data, refund abuse, return patterns, delivery claims, friendly fraud, and chargebacks that appear after the merchant has already done the work.

The merchants who understand this shift will be better prepared.

The tools that win in this next era will not simply score orders. They will help merchants see the pattern earlier, explain the risk clearly, and protect the full commerce workflow.

That is where ecommerce fraud prevention needs to go next.

At Alogram, we are focused on helping Shopify merchants move from isolated order review to explainable, lifecycle-aware fraud decisioning. For merchants seeing suspicious checkouts, failed payment attempts, risky orders, chargebacks, refund abuse, or repeat fraud patterns, our Shopify app is built to help identify those patterns earlier and support faster, clearer action.

Learn more about Alogram for Shopify.

Written by d’Artagnan Osborne, Founder of Alogram, where we are building real-time fraud and payment risk decisioning for modern commerce.

I’ve been thinking a lot about agentic payments lately, not from the perspective of the underlying technology, but from the perspective of how this actually works in daily life.

Most of the conversation around agentic commerce assumes that the future is simply faster money movement. AI agents will shop, compare, reorder, subscribe, cancel, book, negotiate, and pay on our behalf. That part is probably true. Agents will make intent move faster, and in many cases they will reduce the friction between a decision and a payment.

But I think that is only half of the story.

For agentic payments to work safely at scale, the industry will need to accept a somewhat counterintuitive idea: authorization may speed up, but settlement may need to become more controlled.

Once agents are allowed to act on behalf of consumers and businesses, they will need more than a payment credential. They will need a defined operating range. That operating range will likely live in the wallet, or at least be enforced through the wallet, and it will include much more than a dollar limit.

A consumer might allow an agent to reorder household items, renew subscriptions below a certain amount, or book travel only within approved parameters. A business might allow an agent to prepare vendor payments, reconcile invoices, or route payments for approval, but not release funds without a human sign-off.

That is the shift.

The wallet is no longer just a place to store credentials. It becomes a programmable control layer for delegated authority.

Wallets need to become programmable

When people talk about programmable wallets, the conversation often starts with spend limits. That makes sense, but spend limits are only the beginning.

Agentic payments will require controls around behavior, not just amount.

What merchant categories is the agent allowed to use? How often can it transact? Can it purchase internationally? Can it renew subscriptions? Can it select a new vendor? Can it act without approval during certain hours? Can it authorize a payment but delay settlement? When does it need a stronger identity signal from the user?

These are not edge cases. They are the everyday operating rules that will make agentic payments practical.

Over time, we will likely see default profiles for different use cases. A consumer wallet may have one profile for household purchases, another for subscriptions, another for travel, and another for family spending. A business wallet may have profiles for employee purchasing, vendor payments, recurring invoices, procurement approvals, and treasury-controlled payments.

A consumer agent buying household items should not operate under the same policy as a business agent preparing vendor payments.

The important question becomes less, “Can this agent pay?” and more, “Is this agent acting within the approved policy?”

Authorization and settlement need to separate

This may be one of the most important changes.

In traditional consumer payments, we often think about authorization and settlement as part of one payment motion. A purchase is approved, and the system moves toward finality.

In agentic payments, that may not always be the right model.

Agents will make mistakes. That is not a criticism of agents. It is just part of giving software more responsibility. An agent may misunderstand instructions, optimize for the wrong outcome, choose the wrong merchant, renew the wrong subscription, or complete a purchase before the consumer realizes the implications.

The transaction may be technically authorized, but still not reflect the user’s true intent.

That creates a very different operating environment for fraud, disputes, and risk. Today, “friendly fraud” often implies a consumer disputes a purchase they authorized or benefited from. In an agentic environment, the line becomes more complicated. A consumer may have authorized the agent generally, but not that specific action, merchant, timing, or outcome.

That means the payment system needs more nuance.

Fast intent does not always mean instant settlement. There may need to be reversible windows, risk-based holds, approval workflows, or identity step-up before funds fully move.

The future may be faster authorization, but more controlled settlement.

Business payments will stay more human-in-the-loop

Business payments will evolve differently from consumer payments.

Agents will be very useful in business workflows. They can prepare payments, review invoices, reconcile data, route approvals, flag anomalies, and recommend the next action. In many cases, they will remove a lot of manual work from finance, procurement, and operations teams.

But that does not mean businesses will hand over final payment authority without controls.

For higher-value payments, new vendor relationships, unusual invoice patterns, treasury movement, or regulated workflows, human approval will remain important. The agent may do the work, but a person or defined approval chain may still own the release.

That is not a limitation. It is how trust will be designed into the process.

The practical future of business payments is not fully autonomous money movement everywhere. It is agent-assisted preparation, policy-based routing, and controlled approval.

The phone becomes the practical trust checkpoint

For consumers, the mobile device becomes especially important.

The phone already sits at the intersection of device identity, wallet credentials, biometric unlock, passkeys, location context, and user behavior. That makes it a natural place to perform a stronger trust check when an agent action needs confirmation.

If an agent is acting within a low-risk profile, the transaction may move with very little friction. But if the agent steps outside the expected pattern, chooses a new merchant, crosses a threshold, changes geography, or triggers a risk signal, the phone becomes the moment where the system asks for a stronger proof of intent.

Not every transaction needs more friction.

But some transactions will need a clear way to ask: is this really you, and do you really want this to happen?

That is where device-bound identity, biometrics, wallet approval, and passkeys become practical, not theoretical.

A real-time decision layer becomes necessary

The more agentic payments become part of everyday commerce, the more risk decisions need to happen inside the flow.

Static rules will not be enough. One-time authorization will not be enough. A basic fraud score at checkout will not be enough.

Every agent action may need to be evaluated against permission, policy, identity, behavior, merchant risk, transaction context, approval status, and settlement risk.

This is where I think the real infrastructure opportunity sits.

Not in making every payment faster, but in deciding which payments should move quickly, which should pause, which should require approval, and which should require stronger proof of intent.

At Alogram, this is how we think about the next layer of payment risk. Agentic payments will need a real-time risk and decision layer that can evaluate not only whether a payment is fraudulent, but whether the agent is acting within the right policy and whether the transaction should move, pause, or step up for approval.

That is a different problem than traditional payment fraud.

It is a trust and decisioning problem.

The real shift is programmable trust

Agentic payments are coming, but they will not work simply because agents can initiate payments.

They will work when consumers and businesses can define the boundaries of delegated authority, when wallets can enforce those boundaries, when approval workflows are programmable, and when settlement can be controlled based on risk and intent.

The future of payments is not simply faster payments.

It is knowing when money should move fast, when it should pause, and when the system needs a stronger signal of trust.

That is the foundation agentic payments will need before they become part of daily life.

Written by d’Artagnan Osborne, Founder of Alogram, where we are building real-time fraud and payment risk decisioning for modern commerce.