Guide from Monoup: How to Spot and Stop Card Payments Fraud
A new client makes payments in a row for the same amount, uses services actively, and you already think you’ve got a loyal user — only they file chargebacks on all payments a couple of days later. Sound familiar?
This is a typical behavior pattern of card fraudsters who seek out websites with weak security to exploit payment gateways for their own gain. Some try to get services for free using their own money, while others use stolen cards.
How to avoid falling for such bad actors and protect your business from card fraudsters we will discussed today.
Main Types of Card Fraud
Before implementing protection mechanisms, it’s important to understand exactly who you’re dealing with, because the motivation and behavior of different types of users vary, and accordingly, the strategies to combat their exploitations will differ.
Friendly fraud is a real person with their own card who intentionally uses a service and then disputes payment with the bank. Since most banks by default take the cardholder’s side, and proving that a digital service was provided is significantly more difficult than it seems, this opens up a goldmine for dishonest users. To the merchant, such a user looks completely normal until the chargeback happens.
The second type is a carder, who operates with other people’s cards, most often stolen or purchased on darknet marketplaces. Their goal is to cash out the card through your platform or obtain a digital product that can be easily sold on free market. Speed is key for them — they act while the card is still active and rush to spend as much as possible from the available balance.
How to Recognize Friendly Fraud
So-called “friendly fraud” from real customers is usually the hardest to identify. This type of fraudster behaves like an ordinary user, and that’s their main advantage — it’s hard to detect them on the spot. Nevertheless, several patterns can give you a clue, depending on the specific service.
Repeated payments of the same amount in a short period are one of the most important signs. The person seems to be in a hurry to use your service urgently to its maximum extent, though a normal user is usually not in such a rush. This is followed by immediate activity after payment: downloaded, watched, used—and then a chargeback a few days later with the claim “the service was not provided.” This often happens with streaming services, educational platforms, and SaaS. Products in these niches are not physical, and proving they were provided to the client can be a little tricky. You need to gather quality screenshots and carefully track user behavior on the platform — and if this your weakness — such users will exploit it.
Another signal that can be noticed in the data is that chargebacks come in clusters — not one, but several at once within a day or two. Such clients often return multiple times if they managed to deceive you initially: the same email or device, with open registration, can “purchase” the service again if you don’t block them first. In 2026, an interesting generational pattern emerged in the industry: Gen Z is more often involved in friendly fraud than any other generation. Some Gen Z users even believe that this is part of the card systems' customer service — a feature that should be available to them by default. If they’re not in the mood, they can say “the service was not as described” and get their money back. Industry players have already noticed this, but it’s currently impossible to combat this phenomenon effectively without the intervention of Visa and Mastercard. If policies toward such events change, there may be fewer of them. Today, many merchants suffer from this type of fraud, and strategies to combat it often overlap with legitimate customers.
Signs of a Carding Attack
If friendly fraud user might initially seem ordinary and not attract much attention, carding attack will immediately draw your attention — they don’t even try to appear natural; they’re in a hurry, and it’s obvious.
Several failed payments attempt in a row from different cards is one of the clearest signals, known as card testing. The person tries different cards or amounts until the first successful transaction. Meanwhile, card details and account data often don’t match geographically: the card is issued in Germany, IP is from Indonesia, name is misspelled or missing entirely. In such cases, a new account immediately goes to payment without any typical behavior on the site — didn’t browse the catalog, didn’t read the description, went straight to the payment page. All of these are not typical signs for a normal customer, who usually selects a service and compares offers.
Pay special attention to IPs and subnets: one address or a nearby subnet generates multiple registrations and payments in a short time, often for small amounts — carders intentionally start with microtransactions to test the card before moving on to larger sums. The use of VPNs, Tor, or non-standard browsers should also raise suspicion — a legitimate user rarely pays for digital services through anonymizers. An open door for such “buyers” almost guarantees problems with acquirers, and a critical mass of such payments can accumulate in just half an hour. Some fraudsters use entire automated systems and bots that allow them to create accounts on websites in seconds rather than manually entering emails.
Carding attacks are usually fast and frequent, coming in waves and adapting to your settings. Basic limits can be quickly bypassed once they figure them out. By the way, both sides use AI in this context: merchants for protection, and fraudsters for attacks.
What to Do Next?
The good news is that both types of fraudsters leave traces — and these traces can be read in the data if you know what to look for. At the same time, note that there is no universal solution: what stops a carder can create friction for a legitimate user, and overly lenient rules leave gaps for friendly fraud.
The basic principle of protection is built on several levels simultaneously — rules at the transaction level, behavioral analysis of accounts, and working with PSP data, which most merchants often don’t read. Each business has its own risk profile: what is a red flag for one model may be completely normal behavior for another. If large payments are normal on a gaming site, regular eSIM service can’t boast the same, where payments often don’t reach $20.
Setting up protection that doesn’t reduce conversions and actually works is a separate expertise, and the cost of mistakes here is the highest. If you recognize your situation in described patterns and want to understand it in detail — this is a reason for consultation with us.
