Key Takeaways

• Online payment fraud is rising faster than prevention efforts can keep up. Payment fraud losses in the European Economic Area alone reached €4.2 billion in 2024, up from €3.5 billion the year before, driven by faster payment rails, automation, and a shift from stolen credentials into policy abuse through disputes and refunds.
• No single fraud type operates in isolation. Card-not-present fraud, account takeover, APP scams, card testing, skimming, BEC, and chargeback abuse each require different detection logic, and attackers deliberately chain them together to exploit gaps between controls.
• Automation has industrialized payment fraud. Bots, emulators, and anti-detection tooling allow attackers to run credential stuffing, card testing, and account farming continuously at scale. Static rules and signature-based controls cannot keep pace with attack patterns that evolve faster than rule sets can be updated.
• Group-IB’s Fraud Protection stops account takeover, card testing, and automated abuse at the session layer using device intelligence, behavioral biometrics, and bot detection. The Cyber Fraud Intelligence Platform extends that defense network-wide, sharing privacy-safe signals across institutions to uncover mule networks and coordinated fraud rings that no single organization can see alone.

What Is Online Payment Fraud?

Online payment fraud is any attempt to obtain goods, services, or funds by abusing a payment process or manipulating the people and systems in digital channels such as the web and mobile. The fraud includes using stolen card details and account takeovers to move money, tricking someone into approving a payment, and abusing dispute and refund policies.

How Payment Fraud Impacts Businesses

Payment fraud causes direct financial losses from unauthorized transactions and chargebacks. It also drains operational resources, causes false declines that cost you legitimate revenue, and severely damages brand reputation. Organizations processing fast, frictionless payments at scale, such as e-commerce, digital banking, fintechs, and iGaming, are prime targets.

The Payment Fraud Landscape in 2026

Payment fraud is rising even as many organizations are ramping up their prevention efforts. In the European Economic Area (EEA), payment fraud losses hit €4.2 billion in 2024, up from €3.5 billion the year before. Regulators are increasingly highlighting payer manipulation as a key factor behind this rise.

Merchants are also seeing a shift beyond stolen credentials into policy abuse, where disputes, refunds, and returns are used as an easy way to recover money after a legitimate purchase.

Here are the top trends that make online payment fraud harder to contain:  

• Faster payment systems reduce the response window. Instant account-to-account transfers move funds before fraud-prevention teams can review suspicious activity, especially in Authorized push payment (APP) scams, where the customer initiates the transfer. 
• Remote, digital verification expands attack paths. Online journeys often rely on device, session, and behavioral signals rather than physical checks, increasing exposure to credential abuse and automation.
• Automation lowers the cost of fraud. Bots, emulators, and anti-detection tooling let attackers run credential stuffing, card testing, and account farming continuously with minimal human effort.

How Does Online Payment Fraud Happen?

Online payment fraud often starts with attackers obtaining something that allows them to pass as a legitimate customer. This can happen in several ways, such as:

• Payment card data stolen through phishing or malware, 
• Credentials obtained through a data breach and 
• Reused in credential stuffing, 
• Information is bought and traded on underground marketplaces. 

In scam-driven cases, criminals persuade victims to approve a payment by impersonating and creating a sense of urgency. Once attackers have access, they will either resell the information or use it to move money as fast as possible. 

This can mean testing stolen cards with small transactions before making bigger purchases, taking over an account to change payout details and send transfers, or funneling funds through mule accounts to make recovery even harder.

Fraudsters tend to repeat the fraud across many attempts until the payment system challenges or stops them. The common pattern is speed and scale, which is why online payment fraud prevention works best when organizations can catch suspicious access or testing behavior early (before the money moves).

Types of Online Payment Fraud and Prevention Methods

The most common types of online payment fraud are card-not-present fraud, account takeover, authorized push payment scams, and chargeback or refund abuse. Each requires a different prevention approach to avoid unnecessary friction for real customers.

Below, we review each type of payment fraud and the methods that fraud teams can use to detect and prevent it early. 

1. Card-not-present (CNP) fraud 

Card-not-present (CNP) fraud occurs when a fraudster obtains someone’s credit card information and uses it to make an online purchase. It often overlaps with other attacks, such as account takeover or card testing.

Common indicators:

• Unusual combinations of device, location, and purchase behavior for the cardholder
• Shipping to lockers or newly added addresses.
• Short session times from landing to checkout, especially when combined with high-value baskets.

How to prevent CNP fraud:

• Use risk scoring that combines payment data with session signals, not just transaction data.
• Apply step-up verification selectively for high-risk orders. For example, step-up authentication, 3DS challenges, or additional verification for risky delivery methods
• Detect automation indicators (such as headless browsers, emulators, or abnormal velocity) that often accompany scaling attempts.

2. Chargeback fraud and refund abuse

Chargeback fraud, or friendly fraud, occurs when a customer places a legitimate online purchase but later disputes the charges with their bank, claiming they didn’t receive the item or authorize the purchase. The bank will then reverse the charge and refund the customer, resulting in lost revenue and a chargeback fee.

Common indicators:

• Disputes clustered around specific products, delivery methods, or customer segments.
• Repeated claims tied to the same identity, device, or delivery address across multiple accounts.
• High dispute rates for instant fulfillment products, such as digital goods and subscriptions.

How to prevent it: 

• Make transactions easy to recognize through clear billing descriptors and receipts.
• Collect evidence during the transaction, such as timestamps, device information, and delivery confirmation, in case a dispute arises later.
• Flag repeat dispute behavior across identities and accounts using device and linkage signals.

3. Card testing attacks

Card testing is the high-volume validation of stolen card data using small authorization attempts, often followed by larger purchases once a subset of cards works.

Common indicators:

• High velocity of low-value authorizations across many cards.
• Repeated declines from the same device, IP range, or automation cluster.
• Short sessions, minimal browsing, and repeated payment form submissions.

How can you prevent card testing fraud?

• Detect high-frequency payment attempts across devices, IPs, accounts, BIN ranges, or card fingerprints.
• Rate-limit payment retries and enforce smart throttling that targets bot-like behavior without punishing genuine customers.
• Use consistent instrumentation across web and mobile to prevent channel hopping when one surface gets blocked.

4. Skimming and formjacking

Skimming in online payment fraud refers to malicious scripts injected into checkout pages to capture card data. It can also include the compromise of third-party libraries that touch payment forms.

Common indicators:

• Unauthorized changes to payment pages or third-party scripts.
• Unexpected external connections from checkout pages.
• Spikes in customer reports of unauthorized card use after legitimate purchases.

How to prevent it:

• Apply strict controls for third-party scripts and dependencies on payment pages.
• Monitor for unexpected JavaScript behavior and changes in the integrity of checkout assets.
• Restrict who can modify payment page resources and validate release pipelines.

5. Business Email Compromise (BEC) 

Business email compromise (BEC) and invoice fraud commonly target B2B payments and payout workflows. Attackers impersonate executives, suppliers, or finance teams to redirect payments.

Common indicators:

• Urgent requests to change bank account details for an existing supplier.
• Payment instructions that differ from prior patterns or arrive through new communication channels.
• Last-minute changes shortly before a scheduled payout.

How to prevent BEC payment fraud:

• Require out-of-band verification for any changes to beneficiary or supplier bank details.
• Implement approval workflows for changes to payment instructions, especially for high-value transfers.
• Monitor for indicators of impersonation, including domain lookalikes, unusual sender patterns, and abnormal timing.

6. Authorized push payment (APP) fraud

Authorized push payment (APP) happens when a payer is tricked into approving a transfer, often through impersonation, a sense of urgency, or false claims. Unlike traditional unauthorized fraud, the customer initiates the transaction.

Common indicators:

• A first-time payee or a newly created beneficiary receiving an unusually large transfer.
• Behavioral pressure signals, such as the rapid creation of a beneficiary account, followed immediately by a transfer. 
• Transfers to accounts that exhibit mule-like behavior, characterized by many inbound sources and rapid outbound movements.

How to reduce APP fraud:

• Set up additional verification steps for risky beneficiary changes and for initial payments to new payees that seem high-risk. 
• Try to use indicators from the recipient side when assessing risk, since mule behavior often shows up there.
• Keep an eye on transaction details (such as new devices, unusual times, unexpected amounts, and mismatched geolocations) and apply holds or extra checks only when the overall risk justifies it.

7. Account takeover (ATO) fraud 

Account takeover occurs when someone gains access to a legitimate customer account. Once they are in, they can misuse saved payment methods, change payout details, or redeem loyalty rewards.

Common indicators:

• A new device or browser is being used, followed swiftly by attempts to access sensitive settings.
• Rapid changes to account details, like email addresses, phone numbers, passwords, delivery addresses, or payout information.
• A sudden switch from regular browsing to actions that involve monetary transactions.

How to prevent it:

• Keep an eye on how users behave during their sessions, not just successful logins, since a successful login can still mean trouble.
• Set up additional security measures around high-risk actions, such as changing payment methods, updating beneficiary details, resetting passwords, or modifying shipping addresses.
• Use tools to detect credential stuffing and brute-force attacks early, such as monitoring login patterns, implementing bot detection, and checking for unusual login activity.

Strategies to Prevent Fraud Across the Payment Journey

Effective online payment fraud prevention requires strict controls at every stage of the user journey, not just at checkout. Each stage serves as a vulnerable attack path and requires tailored detection signals, which we’ll discuss below.

Stage 1: Account creation and onboarding

Cybercriminals may create new accounts using fake identities or stolen credentials to appear legitimate before executing attacks. These accounts age naturally, creating transaction histories that help them evade monitoring when real-time payment fraud is triggered weeks or months later.

Signals to monitor: Predictable email patterns (e.g., user1@, user2@), identical device fingerprints across multiple new accounts, and rapid automated form completion.

Prevention: Block known bot traffic, verify identity documents, and flag linked attributes (shared IPs or devices) across new accounts for manual review.

Stage 2: Login and authentication

Most payment losses involve some form of identity compromise or manipulation. Detecting abnormal sessions early reduces the likelihood that fraud reaches the authorization stage.

Signals to monitor: Credential stuffing and brute-force patterns at the edge, login success followed by unusual navigation or rapid security changes, or device and location anomalies relative to the user’s established behavior.

Prevention: Require step-up authentication (e.g., SMS or biometrics) for logins on new devices. Place temporary holds on withdrawals immediately following a password or email change.

Stage 3: Checkout and payment authorization

Payment systems need to score transactions within milliseconds to prevent fraud without impacting conversion, especially since delays may cause legitimate users to abandon the transaction.

Signals to monitor: High-velocity transactions, mismatch between the billing address and IP location, or routing to known high-risk destinations.

Prevention: Combine payment signals with session intelligence to detect fraud earlier in the user journey. Apply silent approval for known devices and standard amounts, reserving friction only for high-risk signals. 

Technologies for Real-Time Fraud Prevention

The most effective fraud prevention tools for online payments rely on invisible, session-level signals to stop attacks without disrupting the user experience.

• Device intelligence uses device fingerprinting to examine hardware features like screen resolution, sensor availability, and WebGL rendering to recognize returning users. It can also spot emulators and spoofing attempts by identifying oddities, such as missing hardware sensors on a mobile device.
• Behavioral biometrics analyzes how a user interacts with their device, such as typing rhythm, mouse movements, and navigation patterns. It establishes a behavioral baseline for each user, enabling it to quickly detect automated bots (which fail these checks) and raise alerts if behavior strays from the norm. Case study: A cryptocurrency marketplace blocked over 220,000 bad bot requests and detected 1,200 Trojan-infected devices within two months of deploying Group-IB’s Fraud Protection platform.

• Rules-based systems can quickly block clear cases of fraud, such as transactions from sanctioned countries. On the other hand, machine learning models tackle more complicated situations. They assess borderline cases by weighing various weak signals against historical behavior patterns.

Case study: According to the Forrester Total Economic Impact™ Study commissioned by Group-IB in 2021, a financial services firm with 10 million online customers and over 6 million monthly transactions replaced its legacy anti-fraud solution with Group-IB Fraud Protection (then called Fraud Hunting Platform). In the first year, false positives dropped by 20%, scaling to 30% by Year 3.

The firm blocked 10 to 20% more fraud attempts than the legacy tool, with the block rate rising from 60% to 80% over three years. The Forrester model delivered 130% ROI over three years and a payback period of under six months. Fraud analysts and call center operators also spent less time chasing false alarms.

Stopping Fraud Early with Shared Intelligence

Fraudsters often exploit gaps created by organizational silos to launch attacks that span multiple financial institutions. Traditional prevention methods tend to analyze data in isolation, making it easy for institutions to miss coordinated attacks.

Cross-institution intelligence sharing helps to close this visibility gap. When banks share anonymized fraud signals via a consortium, they detect patterns that span multiple organizations.

Uncover mule accounts and coordinated rings 

Mule account networks complicate the tracking of illicit funds by spreading them across seemingly unrelated accounts. When only one institution looks at this flow, it’s easy to see only a small piece of the puzzle. The Cyber Fraud Intelligence Platform closes this gap by sharing tokenized signals in real time across participating institutions, without any raw personal data leaving each organization’s environment. 

This approach allows fraud-prevention teams to identify shared infrastructure, patterns of sequential account creation, and overlaps in fund flows across participating banks. When multiple banks flag the same device fingerprint, the consortium can reach the level of confidence needed to block transactions before any funds are moved.

Group-IB Fraud Protection detects money mule accounts by identifying behavioral, technical, and relationship anomalies, such as multiple accounts accessed from one device, GPS spoofing, or rapid, low-value transactions. It provides early detection during the account setup phase, allowing banks to block transactions and, through the Cyber Fraud Intelligence Platform, securely share intelligence on suspicious, connected accounts.

Privacy-safe intelligence sharing

Effective fraud prevention requires collaboration, but regulations like GDPR strictly limit data sharing. The solution is privacy-preserving intelligence.

Cyber Fraud Intelligence Platform accomplishes this through Distributed Tokenization. With this system, personal data never leaves your institution. Instead of sharing sensitive information directly, the platform irreversibly tokenizes card numbers, account identifiers, and device fingerprints using Distributed Tokenization. This technology has been independently validated by Bureau Veritas for GDPR compliance. 

Cyber Fraud Intelligence Platform compares tokenized signals across the network in milliseconds, delivering intelligence that the institution’s existing fraud management or risk scoring systems can incorporate into real-time decisioning before authorization. 

Defend Your Payment Channels with Group-IB

Fraud teams are always trying to strike a balance between stopping complex attacks and keeping the checkout process smooth for real customers. Since cybercriminals are leaning more on automation, credential stuffing, and organized mule networks, relying solely on basic security measures or static rules at checkout no longer cuts it.

To properly secure online payments, it’s vital to understand what’s happening during a user’s session and the broader financial network. The best way to prevent fraud is to check user behavior in real time before a transaction and link that data with information from other institutions to detect coordinated threats like APP scams.

Here’s how organizations can set up these protections without adding friction for genuine users:

• Fraud Protection analyzes real-time sessions using device intelligence, bot detection, and behavioral biometrics to detect unusual activity and block ATO or card testing early in the process. 
• Cyber Fraud Intelligence Platform delivers network-level defense. Using privacy-safe Distributed Tokenization, it analyzes cross-institution data to uncover coordinated fraud rings and mule accounts that isolated systems might miss.

Talk to Group-IB experts today to see how our solutions can work with your existing infrastructure to approve more genuine transactions and reduce losses from fraud.

FAQs

What is the difference between payment fraud and payment scams?

Payment fraud is a broad category that includes both unauthorized fraud (a criminal initiates the payment) and payment scams where the victim authorizes a payment under deception. Fast payment systems are especially vulnerable to authorized push payment (APP) scams because funds can become available almost instantly and are often difficult to reverse.

What matters most for online payment fraud prevention in a low-friction checkout?

Look at what’s happening in the session (device consistency, behavior, velocity, and context) and only introduce friction when those signals suggest that something is off. This approach can help reduce false declines because the decision is based on the full session context. 

How should organizations balance privacy and fraud prevention?

The goal is to use the minimum data needed to make high-quality decisions, keep it for the shortest time that still supports investigations and dispute resolution, and demonstrate that the approach is fair and accountable under privacy law.

Which types of fraud require shared intelligence rather than single-institution controls?

Mule networks, coordinated rings, and many APP scam patterns benefit most from cross-institutional correlation because no single organization can see the full chain.

Do the best fraud prevention tools for online payments always require more step-up authentication?

No. The best fraud prevention tools rely on high-quality signals to reduce unnecessary friction, then selectively apply step-up verification to high-risk sessions and actions.