Unlike more traditional fraud, where fake names, mismatched addresses or dubious payments provide actionable clues before or during check-out, friendly fraud is more difficult to detect and expensive to remedy. That means merchants need to be mindful of gaps in AI fraud detection when it comes to friendly fraud.

With friendly fraud, the customer info squares up — it’s a real name, valid card, matching shipping and billing addresses, verified email, and often, a long-standing relationship with the retailer. But with friends like these, who needs enemies?

There are two types of malicious friendly fraud and one variety that actually is friendly, or at least not intentionally unfriendly. The two malicious types each employ similar tactics, a customer claiming they never received the item or that the product was not as described. If the fraud goes as planned, they get their money back and keep the merchandise. 

There are different types of first-party fraud

The first type of friendly fraud stems from people with bad intentions. They have a plan – take advantage of a retailer’s return policy to put money and goods in their own pocket. Many will test the waters with a retailer, finding the cracks in their policies and looking for gaps in AI fraud detection in order to more aggressively reap ill-gotten gains. Most are individuals, although some operate in broader criminal organizations, which have increasingly seen retail fraud as a reliable revenue stream. Many of the products find their way to the black market or resale sites. 

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Chargebacks are a costly reality for many merchants, eating into profits and causing headaches for customer service teams. In 2021, chargebacks were estimated to cost merchants a staggering $125 billion globally, with that number expected to rise to $206 billion by 2025 (Juniper Research, 2021). To effectively prevent and manage these disputes, it’s crucial for merchants to understand the difference between fraud and non-fraud chargebacks.

Nobody’s fault but mine: non-fraud chargebacks due to merchant error

While fraud and friendly fraud often take center stage in discussions about chargebacks, it’s crucial not to overlook the role of merchant error or negligence in non-fraud chargebacks. When a merchant fails to fulfill their obligations or provides subpar products or services, it can lead to legitimate chargebacks from dissatisfied customers.

One common scenario is when a merchant never ships out an order or ships an item that is broken or significantly different from what was described. If the merchant then fails to provide adequate customer service to resolve the issue, the frustrated cardholder may resort to filing a chargeback to obtain a refund.

Merchant error can also occur when a business does not clearly communicate its policies, such as return or cancellation procedures, leading to confusion and disputes. Additionally, technical issues on the merchant’s website, such as incorrect product descriptions or pricing errors, can contribute to non-fraud chargebacks.

To minimize chargebacks due to merchant error, businesses should prioritize providing excellent customer service, promptly addressing concerns, and offering fair resolutions. Clear communication about policies, accurate product descriptions, and reliable order fulfillment processes can also help prevent disputes.

Merchants must investigate the root causes of their chargebacks and identify any patterns related to merchant error. By taking responsibility for their mistakes and implementing corrective measures, businesses can reduce the occurrence of non-fraud chargebacks and maintain positive relationships with their customers.

When criminals strike: third-party fraud chargebacks or “true fraud”

Third-party fraud chargebacks, also known as identity theft or true fraud, occur when a criminal uses stolen credit card information to make unauthorized purchases. This can happen through various means, such as data breaches, phishing scams or skimming devices. In these cases, the legitimate cardholder disputes the charge, resulting in a chargeback.

The impact of true fraud on merchants can be severe. In addition to the immediate financial loss from the disputed charge, merchants may face increased chargeback rates, which can lead to higher processing fees and even the risk of losing their merchant account. True fraud can also damage merchant-customer relationships, as customers may lose trust in a business that has allowed fraudulent transactions to occur.

The wolf in sheep’s clothing: first-party fraud chargebacks or “friendly fraud”

Friendly or first-party fraud chargebacks, are disputes initiated by customers for reasons that may or may not involve criminal intent. Let’s take a closer look at the four most common sources of chargebacks categorized as friendly fraud.

Gaming the system: chargeback fraud

Chargeback fraud, in contrast, is a deliberate attempt by a customer to exploit the chargeback process for financial gain. In this scenario, a customer may purchase a product or service, then file a chargeback claiming it was never received or was unsatisfactory, while still keeping the item or benefiting from the service.

Family ties and forgotten transactions: the unexpected sources of chargebacks

Family fraud and forgotten purchases can also contribute to the chargeback chaos. Imagine a teenager going on a secret shopping spree with their parent’s credit card, or a cardholder forgetting about a subscription renewal and disputing the charge in confusion. These scenarios, where the purchase is made by a family member or acquaintance without the primary cardholder’s knowledge or consent, can lead to chargebacks.

Any and all of these kinds of friendly fraud chargebacks can have a significant impact on merchants. Like true fraud, these types of chargebacks lead to revenue loss, increased chargeback rates and strained customer relationships. Friendly fraud, in particular, can be frustrating for merchants, as it sometimes results from unintentional customer behavior rather than malicious intent.

Decoding the differences between fraud and non-fraud chargebacks

Distinguishing between fraud and non-fraud chargebacks is essential for merchants to develop targeted chargeback prevention and management strategies. By identifying patterns and characteristics unique to each type of chargeback, merchants can more accurately categorize disputes and take appropriate action.

Fighting back: an overview of prevention and management strategies

Merchants should consider implementing financial measures such as liability shift coverage, using fraud detection tools and improving communication with customers to reduce confusion and regularly monitoring disputes to gather evidence for representment.

Working with a chargeback management service can also be beneficial, as these providers have the expertise and resources to help merchants navigate the complex chargeback process and minimize losses.

The bottom line: understanding chargebacks is key

Understanding the differences between fraud and non-fraud chargebacks is helpful for merchants looking to protect their bottom line and maintain strong customer relationships. By recognizing the unique characteristics of true fraud, friendly fraud and the subsets of chargeback fraud, merchants can develop targeted strategies to prevent and manage disputes effectively. Implementing a combination of security measures, customer communication and professional support can help merchants reduce the costly impact of chargebacks and focus on growing their business.

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But digital channels also account for more than half (53%) of retailers’ losses due to fraud, and pure online retailers suffer nearly 40% more fraudulent transactions than mostly brick-and-mortar ones, says Nexis/Lexis.

Added to that, the role of the CFO is changing. CFOs today must manage the integration of new technologies, strategically innovate business functions, and stay on top of economic trends and shifting consumer behavior.

And although preventing fraud has traditionally been a retail CFO responsibility and many CFOs have faced the daunting task of predicting the cost of fraud, risk management has not always broken through as a top priority. 

That needs to change. 

Three levers for fighting fraud: Cost, business opportunity and customer experience

The argument for building up large, internal anti-fraud infrastructures and personnel has historically been to curtail the money lost and expenses incurred due to fraud. In other words, avoid costs.

But there are two other aspects of fraud that CFOs must consider. Those are the potential for fraud management to optimize revenue the nexus between smart fraud prevention and improved customer experiences. 

CFOs have long focused on the cost side of the fraud prevention equation — fraud losses and their bottom line cost. According to the Lexis/Nexis study, each instance of retail fraud results in out-of-pocket costs that are three times the face value of the products stolen or lost.

Cost of fraud, sure. But what about leaking revenue? 

But fraud also limits merchants’ opportunities to maximize sales revenues. When a good customer gets mistakenly turned away because of suspected fraud, you suffer a quantifiable loss of revenue because you don’t sell the product — or products — they wanted to purchase.

Not only that, but the cost of acquiring that disappointed customer was squandered — no small issue with the cost of customer acquisition soaring. 

And then there’s customer experience. Today, customer loyalty suffers when good customers are erroneously denied, or when it’s too much bother to prove their authenticity through cumbersome payment gateways, clumsy fraud management processes, or overly intrusive two-factor authentication. 

Fraud protection is a customer-experience driver

Signifyd CFO Jason Eglit says those working to manage ecommerce risk do themselves a disservice when they ground the dialogue in fraud. 

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“They [fraudsters] have the same kind of mindset — and it really is kind of a battle against each other,” says Xavier Sheikrojan, senior risk intelligence manager for Signifyd. “So they’re not better, but I would also not underestimate them.”

Identity theft has reached a new level

An elevated form of AI, generative AI (GenAI), has upped the challenge by giving criminals even better tools: With GenAI, fraudsters can create synthetic identities with deep fakes that include not only images, but voice cloning. Customer service representatives may think they took a phone call or FaceTime order from a regular, loyal client, but did they? Was that really Mr. Miller they spoke with? 

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This, American Express knows. The card network recently instituted a relaxed AMEX CID (card identification number) chargeback policy that means consumers will have an easier time during that perilous checkout stage in which they need to furnish their card details on a digital site.

As a senior chargeback analyst at Signifyd, I live for following these sorts of changes, which card network brands periodically issue to better serve their customers. In my role, I understand the importance of card network brands having rules in place. They’ve devised the rules to help protect their users — both the cardholder and merchant — and the integrity of the card brands in general. Each card brand has devised similar rules which provide fair practices among card users, issuing banks and merchants. 

The Amex CID chargeback policy is meant to ensure fairness

However, while the rules are devised to ensure fair practices, they still face the challenge of customers who use the rules to abuse the system. 

That’s why an important part of my role on the Signifyd Chargeback Team is to stay up to date with the latest rule changes and provide feedback to the card brands when something isn’t working or rules are being abused. That helps the card brands in their effort to keep providing an integral service to their merchants.

At Signifyd we need to always make sure we are using the rules to the best of our ability to fight unfair chargebacks that are raised against our merchants in order to help combat losses. 

CID mismatches led to consumer inconvenience and cart abandonment

So, why this latest change? American Express continually seeks feedback from their customers in order to keep making positive changes to financial products and services.  Many of their business merchants were telling them that their customers were having a negative checkout experience when the Card Identification Number (CID) entered on an order failed to match the CID embossed on the customer’s card and on file with the card issuer. When CID mismatch happens, the customer has to re-enter all their card details again. Some abandon their cart altogether instead. Merchants asked American Express if it could make the checkout experience simpler while also providing some sort of protection in the worst-case scenario: The cardholder raising a false fraud chargeback with their issuing bank. 

On April 12, AMEX unveiled a new CID chargeback policy. It says that if a merchant receives a valid authorization on a card-not-present transaction and that transaction turns out to be fraudulent, the American Express issuing arm will write off the fraud and prevent the chargeback from being sent to the merchant under specific circumstances.  The policy applies to cases in which the merchant obtained a valid authorization and then attempted to validate the CID and the response it received was either “no match,” an “unchecked” or a “no response.”

New CID rules come with a fraud liability shift

In other words, under those circumstances, the fraud liability will be shifted and the chargeback will not count against the merchant’s chargeback rate. 

Merchants can now feel confident submitting approved CID mismatches knowing they now enjoy a liability shift to American Express for orders that turn out to be fraudulent. The hope is that the policy change will help increase merchants’ sales and profitability because fewer customers will be inclined to abandon their carts when the CID does not match. With a smoother checkout process, the customer will be more likely to buy from the merchant in the future. 

At Signifyd, we hope to see less friction when a customer is purchasing using an American Express Card from one of our many merchants if the transaction should fail due to a CID mismatch.

The AMEX chargeback policy now gives merchants peace of mind and consumers added convenience

Now our merchants’ payment processors should automatically put through the transaction without the cardholder having to complete the transaction in full again. With fraud pressures currently on the rise, our merchants will feel at ease knowing that their checkout process has been better for their customers with the added protection of the liability shift should the order result in a fraud chargeback due to CID mismatch. With Signifyd providing a chargeback guarantee on both fraud and consumer abuse chargebacks, merchants can set chargeback worries aside. 

 At Signifyd, we’re mindful of the new Amex CID chargeback policy. We have altered how we detect those chargebacks. Should a chargeback arise out of a CID mismatch for any of our merchants, we are ready to present evidence that our merchants have completed their due diligence by checking the CID at checkout.  We want to make sure that American Express protects our merchants in this one area with the same vigor that we at Signifyd protect them throughout the shopping journey. 

Want maximum peace of mind when it comes to chargebacks? Let’s talk.

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From first-party scams by legitimate customers to full-scale criminal enterprises that stalk and scheme, attack, regroup, then patiently lie in wait to strike again, ecommerce risk prevention experts think it’s time for retailers to update their fraud management strategy with AI-driven solutions backed by human intelligence and domain experts. 

It’s called survival.  

“At Saks we are committed to doing the best that we can but there [are] just things we’re going to have to do to scale this because these fraudsters are, they’re good, they’re really good,” said RJ Cilley, chief operating officer at Saks, speaking about return abuse and challenges at Signifyd’s FLOW summit 2024.

It’s time for heightened fraud awareness 

Merchants lost $41 U.S. billion in ecommerce fraud worldwide in 2022, and an estimated $48 billion in 2023. Equally concerning and quite scary is that fraud losses of $343 billion are predicted to accrue globally from now until 2027.  

“Retailers have to start getting prepared for a little bit of a new world,” said Michael Pezely, Signifyd’s senior director of risk intelligence. “In this environment that we find ourselves in, where inflation is persisting, we’ve got interest rates that are rising, and, of course, geopolitical tensions that are trickling down – in some unexpected ways this environment is actually potentially fueling more fraud.” 

Pezely says there are three pre-indicators of the fraud rise in ecommerce: Fraud pressure has risen on the whole about 50% year over year. First-party fraud has also dramatically increased over the years. In some businesses, 40% to 60% of fraud chargebacks are actually first-party fraud. And account takeovers are up close to 300%. 

On top of all that, and beyond the rise of first-party fraud, the fraud industry is becoming industrialized, spawning sophisticated, global enterprises with specialists in ecommerce, fulfillment and fraud prevention. These rings are illicit businesses that operate in regions where regulation is weak and geopolitical upheaval has resulted in a high degree of lawlessness. 

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Taylor Swift fans know this first-hand. Demand for tickets for concerts on her highly anticipated The Eras Tour far exceeds supply. Fans have reported logging onto Ticketmaster minutes after tickets go on sale to find that they are already sold out, only to find them reappearing on secondary market platforms or reseller sites at much, much higher prices. Some Taylor Swift fans pay 70 times the tickets’ original selling prices, while many can’t get access under any circumstances due to this retailer-versus-reseller battle.

Ticketmaster and other ticketing platforms are attempting to combat this problem using CAPTCHA codes and queueing systems to deflect bots and ensure fair access to tickets for real human beings. But resellers constantly evolve their strategies, creating ongoing challenges for retailer sites.

Reseller abuse is growing as fraud continues its digital transformation

The high-profile incidents of digital scalping and price gouging are simply the best-known cases of a trend that Signifyd data shows has been growing significantly for some time. As organized crime rings industrialize their ecommerce fraud operations, establishing Fortune 500-like enterprises with experts in commerce, fraud, fulfillment and post-purchase returns and refunds, reselling has become a lucrative revenue stream.

In the past year, the increase in orders from unauthorized resellers increased year-over-year by an average of 10%. Month-by-month the increase in attempted reseller orders — identified by a high number of repeated orders bearing the same digital identifiers on the same day — increased from 4% to an April spike of 35%. In no month was the pressure from unauthorized resellers on Signifyd’s Commerce Network lower than in the same month a year before. 

Increase in reseller abuse year over year

Bots power resellers’ business 

Bots are at the root of this problem, though some reselling schemes rely on humans to get the job done. First, what are bots? The term is short for “software robots,” and refers to AI-based applications that automate digital tasks, typically focusing on the repetitive or mundane aspects of computer-dependent desk jobs. They often simulate humans’ interactions with devices, software, websites or online forms. In retail, such bots can be programmed to speedily navigate through even the trickiest online checkout processes, vacuuming up whole inventories of the most in-demand items on a site before genuine customers have the opportunity to make their first click in retailers’ buying processes. 

Resellers also use bots in other ways. For example, they exploit discrepancies in pricing across different online platforms. They scan competitors’ prices and adjust the ones on their reseller sites accordingly. This results in a no-win race to the bottom that can significantly erode retailers’ profits.

Smaller businesses are disproportionately affected by bots, as they don’t possess the money or expertise to invest in the necessary sophisticated anti-bot technologies or business strategies – or to absorb any losses that occur. Market consolidation often ensues, with large reseller sites gaining more and more control and pushing out independent retailers, which inhibits competition and limits consumer choice.

How retailers’ performance is impacted by reseller targeting

The above types of reseller behavior significantly impact retailer performance and brand reputation. Consider, for instance, the great Sony PS5 Christmas disappointment. With the holiday season in full swing, a series of bot attacks cleared the digital shelves of the popular gaming console, leaving parents to pay a premium on the secondary market. Or worse, it left parents explaining to their kids that Santa didn’t see himself clear to drop one of the coveted gizmos at their house. 

Either case leaves consumers frustrated and disappointed or even angry with the retailer that couldn’t keep in stock the PS5 — or in other cases whatever popular item was scooped up.

“I think it’s ultimately about consumer trust and losing that trust has serious repercussions for a business,” said Signifyd Vice President, Strategic Initiatives Gayathri Somanath. “You want to bring consumers to your site and you want to create a trusted experience for them that keeps them coming back to you. These kinds of scalping attacks usually mean that your site has been compromised, a much-desired product is unavailable, and these goods are going to be sold somewhere else. It leaves consumers with a lack of trust in your site. For the retailer, you’re losing good customers and it diminishes your brand value.” 

Both the retailer and the brand — Sony in the PS5 example — lose control of the customer experience and miss the opportunity to build a relationship with the buyer — one of many damaging effects of unauthorized reselling.

Here’s a comprehensive list of how unauthorized resellers damage retailers in the short term and the long term:

• Revenue losses: When unauthorized reseller sites commit resale abuse by buying products in large quantities and selling them at inflated prices on secondary markets, retailers can lose big as customers are diverted from shopping legitimate sites where they often buy additional accessories and items that complement the popular item they were after in the first place. 

• Customer dissatisfaction: Genuine human buyers who fail to purchase products through normal retail channels due to resellers’ shenanigans can grow unhappy with both the retailers and with the manufacturers of the products in question — especially when they have to pay a significant premium elsewhere. This potentially erodes trust and customer loyalty.
• Deteriorating brand reputation: When customers perceive the purchasing process as unfair or manipulated, they can associate their negative experiences with your brand. This can damage your reputation, making it more difficult to attract and retain customers.
• Disrupted retail markets: By creating artificial scarcity and driving up prices on secondary markets, reseller sites can completely discourage customers from purchasing certain types of products altogether, impacting overall sales and revenue for manufacturers and retailers in particular industries.
• Regulatory constraints: The behavior of resellers has drawn regulatory scrutiny and calls for legislative intervention to address fairness and transparency. The onus could be put on retailers to implement more robust – and costly – measures to prevent reseller activities and safeguard consumers.

How AI technology – biometrics and machine learning (ML) – can minimize reseller abuse

But AI can be retailers’ friend, also. Behavioral biometrics and ML models can help prevent reseller abuse with advanced ways to detect and thwart suspicious activity. Here’s how:

• Analyze customer behavior: Behavioral biometrics recognize suspicious patterns in the behavior of online visitors to retailer sites such as how mice and trackpads are used, typing speeds, and certain navigation tendencies to create profiles of individual users. By continuously monitoring these behaviors, the technology can recognize anomalies that indicate bots are at work.
• Separate bot from human visitors: By training ML models on historical online data from multiple retailers’ sites, AI systems can learn to recognize bots. Patterns such as too-swift or repetitive clicking, unusual browsing or high-frequency transactions from a single IP address can indicate potentially malicious, non-human activity.
• Automatically identify unauthorized reseller ecommerce fraud: ML algorithms can be trained to detect fraudulent transactions and combat retail fraud by noting the frequency of transactions,  purchasing history of individual site visitors, information about devices used and geolocation data. By identifying deviations from typical human purchasing behaviors, such models can flag those transactions most likely to come from reseller sites or their bots.
• Use dynamic risk scoring: ML algorithms can be used to build risk-scoring systems that dynamically judge the risk associated with transactions or interactions with site visitors based on a broad array of factors. By continuously updating risk scores in real time, retailers can take action like requiring additional authentication or temporarily blocking suspicious accounts.
• Deploy adaptive integrated security controls: ML models can be integrated into retailers’ existing security systems to adapt controls based on evolving threats. For example, if they detect a new type of bot attack, they can swiftly recognize and communicate to the security technologies already in place to stop it, making retailers’ defenses against reseller abuse more resilient.

Reseller abuse is a multi-layered challenge that requires a multi-faceted defense strategy. Signifyd’s Commerce Protection Platform relies on a Commerce Network of thousands of online merchants and AI-driven models to build an understanding of the identity and intent behind every online order. That puts Signifyd in the unique position to detect the tell-tale signs of evolving bot attacks while also giving it the intelligence to recognize sophisticated actors who develop schemes that avoid relying on bots.

While Signifyd’s AI defends merchants against a vast array of reseller attacks, its policy abuse engine, Decision Center, can be used to create customized and flexible rules using AI features to identify and manage resellers based on a business’ needs.

CurrentBody, which sells innovative beauty and self-care products, turned to Signifyd when its unauthorized reseller program became a growing problem.

“We were very concerned about the effect resellers could have,” said Lyn Carbine, head of trading at CurrentBody. “Controlling the distribution of our products is essential to maintaining successful brand partnerships.”

After setting up policies to deter unauthorized resellers, Carbine said CurrentBody’s unauthorized reseller rate “effectively dropped to zero.” 

“The impact of this on our brand and partnerships can’t be overstated,” she continued. “We’ve regained control of millions of dollars worth of product that would have flowed through the wrong channels.”

Other ways to combat reseller abuse

Technology isn’t the only way to fight back against reseller abuse. Business-model-based strategies can also work well. Of course, not every business-model strategy fits the business model of an existing brand. And while considering whether to adopt a business-model-based approach consider any additional effects, such as adding friction by inconveniencing consumers or causing frustration with limited inventory. That said, some business-model-based strategies include:

• Surprise site visitors with unpredictable product selections and releases: Introduce randomness into your available inventory by changing product release times, limiting the number of products that can be purchased by each customer, or rotating what’s available at any given time to make it harder for reseller sites to easily predict and program their bots to exploit your retail site. You can also create “limited edition” offers that can generate hype and urgency among human customers while making resellers hesitate to invest in building bots for items with limited availability.
• Verify accounts: Use techniques such as email verification or two-factor authentication to make sure that individual purchasers are legitimate human customers and not automated AI bots. 
• Change to a direct-to-consumer (DTC) model: By selling directly to consumers through your own channels – for example, your own online store (as opposed to an aggregated retailer like Amazon) or branded brick-and-mortar locations – you have more control over pricing, inventory management, and customer relationships, reducing the influence of resellers. 
• Offer subscriptions: By asking customers to pay fees for access to exclusive products, discounts, or benefits, you can stop resellers by limiting the availability of sought-after items to privileged – and guaranteed human – members.
• Personalize customer experiences: Offering personalized or customized experiences can discourage resellers, as such products won’t be attractive to the mass market and therefore the revenue potential from reselling them will be low. Personalization also has the advantage of significantly boosting customer loyalty for retailers. 

Stopping reseller market abuse is a retail community effort

Reseller abuse is a serious threat to the health of online retailers. You’ll be more successful if you don’t act alone or in a vacuum. 

By sharing selective data, for example, retailers – even competitors – can collectively strategize to strengthen their defenses against reseller sites. After all, you face common adversaries. You will all be more resilient if you present a united front against the destructive influence of reseller sites and their bots. Signifyd’s vast Commerce Network essentially provides this sort of advantage without sharing data among merchants.

Don’t neglect building strong partnerships with online marketplaces, either. They’re very powerful and working with them to establish strict policies against reseller abuse can help you monitor and enforce fair trade, protecting both them and you—guarding your respective brand reputations as well as customer experiences.

By adopting the above technologies, business models and strategies, retailers can minimize the impact of reseller abuse while fostering stronger relationships with their valued human customers. All of which leads to maintaining better control of their brands.

Photo by Getty Images

Are unauthorized resellers taking your profits? Let’s talk. 

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This erroneous identification of a valid purchase as fraudulent is called a false decline — and can lead not only to the loss of that particular sale but to broader damages.

 Your questions about false declines answered

 What are false declines?

False declines are valid orders that a merchant declines for fear of fraud. The term “false decline” indicates that the order was erroneously flagged as fraudulent. Some merchants call these orders “insults,” an indication of the damage they can do to customer relationships.

 What is the average false decline rate?

It’s tricky to get an accurate false decline rate, partly because only 64% of merchants track false decline rates, according to a merchant survey by the Aite-Novarica Group.

Those that do, use varying strategies for identifying false declines, such as an order that goes through on the second attempt and does not result in a chargeback; or a blocked order that is followed by a complaint to customer service. Some merchants purposely approve a small number of orders that appear fraudulent, as well, to test whether their systems are correctly identifying fraud.

Due to these complications, estimates of the false decline rate vary. For example, in the  Aite-Novarica Group survey about one-third of ecommerce merchants estimated that more than 1% of their total transactions are false declines. Another estimate, based on a survey by the Merchant Risk Council, found that about 6% of all ecommerce transactions are rejected due to fraud suspicions, and of those, false declines are between 2% and 10%.

 What causes false declines?

 False declines arise in ecommerce for a number of reasons. Merchants that depend on manual review of orders, legacy rules-based systems or some combination leave themselves open to problems that newer AI-driven fraud protection systems were designed to avoid.

Those who turn to manual review with human agents to detect fraud leave themselves open to human error. Fraud teams that find themselves confronted with a spike in the volume of orders, for instance, are prone to make errors as they scramble to keep up with demand. Human agents can also mistake a set of conditions that indicate fraud in one order for a sign that a subsequent, similar order is fraudulent, even when there is an innocent explanation for why odd conditions exist.

For instance, while fraudsters might ship an order to an address that does not match the order’s billing address so they can take possession of their ill-gotten gains, a shipping and billing address mismatch might also occur because a customer is shipping a gift directly to a loved one. 

What are the problems with rules-based systems?

Similar problems can crop up when merchants turn to legacy, rules-based systems that use automation to decline an order when a certain pattern of conditions in an order presents itself. Rules systems often grow in complexity as new rules are added to the system when new red flags are discovered. Over time, the systems become more restrictive — declaring fewer orders to be fraud-free and therefore approving fewer orders overall. 

Merchants who rely on manual review and rules-based systems have to constantly adjust how their systems determine what is fraudulent, in an attempt to stay ahead of the perpetrators. When they set the system to be overly cautious about potential fraud, the result can be false declines.

Merchants that use AI-based fraud protection and make ship-or-don’t-ship decisions on vast amounts of data, however, can avoid the leading causes of false declines and substantially reduce their likelihood. The machine-learning models at the heart of such future-focused solutions rely on transaction, behavioral and historic data to separate fraudulent orders from legitimate ones. 

How are AI-driven systems different?

“There are over 1,000 features that the model takes into consideration and thousands of data elements within those features,” Jasal Motiram, manager of enterprise customer success at Signifyd, said of the company’s AI-driven fraud-fighting technology. 

This means there is not just one factor that determines the risk, but rather a combination of factors, including whether the ordered item is often targeted in fraud schemes; a new shipping address; a recently opened account; mismatches involving credit card CVV numbers; or declined orders that are retried within seconds, faster than a human could retype the necessary information. The machine learning models provide flexibility and make their determinations with significantly more context around each order. 

That said, it’s important to understand that fraudsters are aware of some of these factors and constantly evolve their own tactics to try to outsmart the learning model. For that reason the human intelligence and domain expertise that informs the design and education of the machine learning models is a crucial factor in successful fraud protection. 

 What is the cost of false declines?

False declines cause numerous problems — all of which can lead, ultimately, to increased costs or lost revenue. 

In 2021, 451 Research estimated that false declines resulted in merchants losing $16.3 billion a year. And it’s estimated that merchants lose 13 times more money to false declines than they do to true fraud, according to a frequently cited Javelin study. 

What are all these costs? To begin with, of course, the merchant loses out on the revenue from the sale that is erroneously tagged as fraud. In some cases, the customer then calls customer service for help, costing the merchant money in the form of staff time. 

Overall results 2024 YTD

But as Signifyd analysis shows, the costs don’t stop there. Among loyal customers — those who have previously had at least three orders approved — a false decline is followed by a 65% decline in the number of orders placed by that customer and a 16% decline in their average order value. Moreover, 27% do not return to the merchant at all — meaning the merchant loses not just that one sale, but a lifetime of potential sales from a repeat customer. 

And even those shoppers who return are significantly less valuable as customers. Signifyd’s analysis determined that the lifetime value of returning insulted customers drops by 17%, compared to those customers who were not subject to a false decline. 

Why do false declines need to be addressed?

 The main reasons merchants should act to prevent false declines are financial: Declining legitimate orders costs money, both in the immediate term and over time. 

But in addition to the lost dollars, merchants risk damage to their customer relationships and reputation. 

Having a legitimate order declined will make a customer feel — at best — frustrated and likely to shop elsewhere, possibly forever. At worst, customers who feel like they’re being treated as potential criminals may not only give their business to a competitor but may also vent their frustrations to friends or coworkers, or on social media. The damage to the reputation of the brand could be long-lasting – and could translate into even more lost sales, as well as difficulties with employee recruitment and retention. 

Seven steps to avoid false declines 

Analyze your system’s false declines

The first step to preventing false declines is understanding why your system is mistakenly identifying legitimate orders as fraudulent. 

“Categorize your insults, or false declines,” Motiram said. Did the customer call in? Did the risk team review it and decide it was a good order? “Understanding where you’re committing insults can actually allow you to home in on what you need to fix.”

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The impact exceeds just the dollar amount reversed. Each chargeback results in fees from payment processors and card networks like Visa and Mastercard. Administrative costs to respond to disputes along with potential merchandise and shipping costs for unrecoverable items inflate total losses. One study found merchants lose an average $3.75 for every $1 in chargebacks. Every merchant faces this risk to the bottom line.

Dangers extend beyond monetary losses. An excessive chargeback rate damages reputations with banks and processors. This leads to holding funds, higher fees, potential account closures or sanctions such as placement on the Terminated Merchant File. Card networks monitor chargeback ratios and can place merchants in monitoring programs such as the Visa Dispute Monitoring Program (VDMP) when chargebacks cross thresholds.

While chargebacks may never fully be eliminated, merchants can take proactive steps to prevent and mitigate most invalid disputes. The steps merchants can take include understanding the main triggers for chargebacks, utilizing prevention tools, optimizing policies and operations and fighting fraudulent chargebacks.

Understanding the main triggers of chargebacks

There are several reasons why chargebacks occur. Here is a look at the common ones.

Fraudulent transactions

Chargeback fraud comes in several forms. Some fraudulent chargebacks result from outright criminal fraud using stolen credit cards. But “friendly fraud,” also known as first-party fraud, by customers deliberately claiming they didn’t participate in a purchase to get merchandise, goods or services for free is also common.

Product issues

When customers claim they never received the product ordered, it arrived damaged or defective, or it was not as described on the website, they may file a chargeback.

Processing errors/descriptor issues

If the merchant’s name on the credit card statement is unrecognizable or unclear, customers may claim not to recognize the charge and dispute it.

Subscription issues

Customers sometimes dispute recurring subscription charges after an initial free trial if they didn’t intend to purchase or thought they properly canceled. They may also file a dispute explaining that they wanted to cancel a subscription but were unable to.

To reduce chargebacks, merchants must understand the main triggers behind them and implement prevention tactics. Simply writing off chargebacks as a “cost of doing business” leaves significant revenue on the table.

Preventing chargebacks

Have clear return/refund policies and make them visible

Customers are more likely to go through proper refund channels instead of disputing a charge if return policies are clear and easy to find on the website, receipts, etc. Make the process simple to reduce your risk of chargebacks.

Provide excellent customer service

Be responsive when customers have questions or issues. Offer easy ways to contact support, like phone, email, chat and social media. The harder customers must work to reach you, the more likely they are to dispute charges.

Optimize billing descriptors

Use a recognizable business name or website URL in the descriptor on credit card statements. Test to ensure customers see an identifiable name. Unclear descriptors lead to disputes.

Use purchase verification

Require address verification (AVS), CVV codes and other authentication tools to prove customers are authorized users before accepting orders. This deters credit card fraud upfront.

Stay compliant with all payment network rules

Be aware of the time-consuming overall chargeback process. Violations like processing incorrect product codes or delayed transactions can trigger chargebacks. Carefully follow Visa, Mastercard, etc. operating regulations.

Following best practices gives customers fewer reasons to dispute charges and makes operations more chargeback-resistant. But specific prevention tools are also needed.

Chargeback prevention tools & services

Chargeback prevention

Prevention begins with order approvals. A merchant who approves every order is bound to get chargebacks. One who blocks every order with any level of risk will end up with “false positives” and reject lots of good orders. Merchants can conduct manual review to try to prevent bad orders, but this creates a delay for the customer and a headache and high costs for the merchant.

Effective fraud prevention built into the order process helps reduce the risk of chargebacks. Vendors are available who provide manual review and/or automated review using machine learning, or some combination that works for their business. And if the vendor offering chargeback protection also guarantees orders and provides liability shift, merchants don’t need to worry as much about the financial impact of chargebacks.

Chargeback alerts

Chargeback alert services provide advance warning of potential disputes from participating banks, giving merchants 24-72 hours to resolve issues and avoid chargebacks.

Network inquiry tools

These tools connect merchants directly to card networks and issuers to share order details and transaction evidence to resolve potential disputes before chargebacks occur.

Advanced chargeback representment services

Companies like Signifyd help merchants recover losses from chargebacks by resubmitting cases with compelling evidence to get illegitimate chargebacks reversed and funds returned. They handle the entire time-consuming representment process, making sure all deadlines are met and all recovery steps are completed for the merchant. Some will guarantee that chargebacks from approved orders are covered and reimbursed to the merchant.

Chargeback monitoring/management

Chargeback monitoring, analysis tools and prevention advice to help keep chargeback rates under control are available from vendors offering chargeback protection like Signifyd. Some merchants may have access to similar services through their acquiring banks. Acquirers may also receive notifications from Visa or other payment processors when a merchant’s chargeback rates get too high.

Ecommerce merchants cannot rely on manual efforts alone to detect and prevent chargebacks in real time. Automated prevention tools that integrate with operations are essential to a comprehensive strategy. Merchants will also want to be ready for the most common chargeback scenarios.

Handling specific chargeback scenarios

Fraudulent transactions

Leverage fraud detection and fraud prevention tools to stop criminal fraud chargebacks. For friendly fraud, have a robust process to contest invalid disputes with compelling evidence the customer participated in the sale.

Product/service disputes

If customers claim non-delivery, provide tracking numbers and delivery confirmation. For claims of receiving wrong/defective items, have a process to obtain and submit evidence.

Billing descriptor issues

Optimize billing descriptors as covered earlier. Also, send purchase receipts immediately after orders to remind customers what they bought.

Subscription disputes

Improve subscription processes with clear disclaimers about recurring charges and easy cancellation flows. Obtain customer consent and send timely renewal reminders.

While prevention is ideal, the risk of chargebacks will still be present even with excellent practices. Having defined procedures to gather evidence and respond based on the dispute reason code increases the chances of resolving cases successfully.

Advanced chargeback management tactics

With the common scenarios and tools in mind, consider the following tactics in your chargeback prevention strategy.

Understand chargeback reason codes and respond appropriately

Different chargeback reason codes like “product not received” or “canceled recurring transaction” require providing specific evidence. Having a system to automatically take the right response saves time.

Dispute invalid chargebacks through representment

Don’t simply accept all chargebacks. Consistently resubmitting winnable cases with compelling evidence can recover revenue, deter future friendly fraud and protect your financial reputation.

Track key metrics like chargeback ratios and fees

Monitor chargeback rates, total chargeback fees paid and other metrics over time. Set maximum thresholds to trigger improved processes if exceeded.

When to consider chargeback liability shift or reserves

For some larger merchants, having a reserve with funds to cover potential disputes can make sense financially. Merchants of any size might do better to work with a reputable fraud protection vendor providing liability shift, an insurance-like approach to covering losses from chargebacks. The vendor guarantees that any chargebacks that arise from approved orders are covered, shifting liability from the merchant to the vendor.

Just preventing chargebacks upfront is not enough. An advanced strategy also requires monitoring key performance indicators, efficiently resolving disputes when they occur and evaluating revenue protection options.

You can prevent chargebacks

Chargebacks remain a significant source of revenue loss and operational disruption for ecommerce merchants. But they don’t have to be an unavoidable cost of doing business.

By understanding the main triggers like credit card fraud, product issues, processing errors and subscription mishaps, merchants can implement prevention best practices and tools customized to their risks. Making policies clear, prioritizing customer service, optimizing billing descriptors and leveraging services like chargeback alerts and inquiry tools equip merchants to stop many chargebacks before they happen.

When disputes do occur, having defined procedures to provide the right compelling evidence based on reason codes, disputing illegitimate chargebacks through representment and tracking performance metrics allows merchants to resolve chargebacks efficiently and identify areas to improve.

Merchants should also evaluate options like liability shift or chargeback reserves based on their specific risk profile. With a comprehensive prevention and chargeback management strategy in place, merchants can minimize chargebacks, recover more revenue, maintain processing capabilities and preserve customer relationships.

Implementing the strategies in this guide requires an investment of time and resources. But the ROI of a chargeback prevention plan makes it worthwhile for any merchant wanting to eliminate this significant, unpredictable revenue drain.

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Last year, in-store and online returns totaled $743 billion (14.5% of sales), of which $101 billion (13.7%) was lost to return abuse and fraud, according to a survey published by the National Retail Federation. For every $100 in returned merchandise, retailers lose $13.70 to refund abuse.

Tightening up free and easy return policies is fraught with peril

Tightening an abuse return policy to prevent return fraud and lessen losses is an option for ecommerce merchants, but it’s tricky. Making returns difficult or expensive for consumers appears to be a losing proposition. 

For most online shoppers there’s no turning back. They’ve become spoiled with easy returns: In a recent survey by the International Council of Shopping Centers (ICSC), 82% of shoppers said a return policy determines where they purchase.  

And a Signifyd consumer survey found that more than 80% of online consumers polled said they would shop more often with a retailer based on a good return experience. By the way, in the same survey, 75% said they would stop shopping with a retailer based on a bad return experience.

In the ICSC survey cited above, nearly three-fourths of the 1,012 respondents said they’d stop shopping online from a company that charged a fee to ship back a purchase, while more than half said they’d quit shopping online with a retailer if that retailer shortened a free return window. 

Drive profits by knowing your customers 

To successfully minimize refund abuse, retailers need the help of technology to provide insight into the identity and intent behind each return request. Who is returning the item? Is there a history of ecommerce returns? What does that history look like?

Signifyd CEO Raj Ramanand, speaking at the company’s FLOW Summit 2024, stressed the importance of ecommerce focusing on personalization and maximum trust to build customer loyalty and achieve profitable growth. 

“A delightful customer experience consists of two things,” he said. ‘’One, I as the retailer, as the brand, have to trust who that person is. And two, once I can do that, I need a way in real time, at the moment when someone is making a return, when someone is checking out, at that moment, I need to be able to personalize that experience.”

How minimizing future returns can improve revenue

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