In a study conducted by Business Wire, it was estimated that businesses lose as much as $448 billion annually from payment fraud, refund abuse, and false declines. Payment fraud is among the highest-cost threats that digital retailers face, and becomes a major issue if left unaddressed. Because of this, understanding payment fraud and how to properly resolve it is paramount to a business’s success.
What e-commerce payment fraud is
E-commerce payment fraud is any fraudulent transaction made online without a physical credit or debit card. Because merchants aren’t able to verify a card in person, they rely entirely on the information that the user enters at checkout. Fraudsters exploit this by supplying stolen or fabricated payment information.
This can be done with stolen card numbers, compromised accounts, false disputes on legitimate orders, or abusing return policies after placing fraudulent purchases. Most of these tactics result in chargebacks. The merchant loses both the transaction value and the item itself (as it’s already shipped), typically with a chargeback processing fee on top.
The scope is significant. According to Javelin Strategy & Research, card-not-present (CNP) fraud accounts for the majority of payment fraud losses in the United States, and losses continue to grow as e-commerce volumes increase. For merchants operating at scale, even a small fraud rate translates to thousands of fraudulent transactions per month.
The real cost of e-commerce payment fraud
Merchants often calculate fraud costs by the value of fraudulent orders. The actual cost is higher. Chargebacks carry processing fees on top of the transaction value, typically ranging from $15 to $100 per dispute depending on the payment network and acquirer. Merchants that exceed card network chargeback thresholds face additional consequences such as increased processing fees, enrollment in mandatory dispute monitoring programs, and in serious cases, loss of their ability to accept card payments altogether.
Operational costs compound the direct losses, and of course, also cost the merchant valuable time and resources. Additionally, sometimes legitimate orders get declined, resulting in loss of real revenue and damaging relationships with trusted users who may not return.
According to LexisNexis Risk Solutions, for every dollar of fraud loss, U.S. merchants absorb approximately $4.61 in total costs when accounting for fees, chargebacks, and operational overhead.
Common types of e-commerce payment fraud
- Card-not-present fraud: The most common form of e-commerce payment fraud. Occurs when fraudsters obtain card numbers, expiration dates, and CVVs from data breaches or underground marketplaces, then use them to make purchases online. The cardholder disputes the charge, and the merchant absorbs the loss.
- Card testing: When fraudsters make small transactions to verify whether a stolen card is active before attempting larger purchases. Fraud teams can spot it through velocity patterns: a high volume of small transactions from the same device or IP address, followed by a significant order.
- BIN attacks: Rather than relying on a single stolen card, fraudsters systematically test combinations of card details using a known bank identification number prefix to generate valid card numbers. These attacks can overwhelm fraud detection queues quickly.
- Account takeover (ATO) fraud: When legitimate customer accounts are targeted through credential stuffing, phishing, or social engineering. Once inside, fraudsters use stored payment methods to place orders, often to newly added shipping addresses. ATO-driven payment fraud is particularly difficult to detect because the session begins with what appears to be a trusted login.
- First-party fraud (friendly fraud): When a legitimate customer disputes a valid transaction, either intentionally to avoid payment or because they do not recognize the charge. It looks identical to a fraud dispute from the merchant’s perspective and is expensive to fight without strong transaction-level evidence.
- Refund and return fraud: Involves placing orders with stolen card credentials, initiating a return, and receiving a refund before the original chargeback arrives. Merchants face the cost twice: first for the fraudulent order and next for the refund already issued.
Why e-commerce payment fraud is difficult to detect
In isolation, most fraud signals are ambiguous. A shipping address that does not match billing is common in legitimate gift purchases. An email domain that looks disposable might belong to a privacy-conscious trusted user. A device flagged for previous fraud might have been sold or shared since. None of these examples are immediate red flags by themselves, so detecting fraud can be a challenge.
Effective detection requires context: account history on the platform, device intelligence across sessions, network-level patterns connecting a given transaction to others, and behavioral signals collected during the checkout flow. Fraud teams need a system that synthesizes all of these signals simultaneously rather than evaluating them in sequence.
How fraud teams prevent e-commerce payment fraud
Modern e-commerce payment fraud prevention works across multiple layers, with risk assessed throughout the full customer journey rather than just the point of transaction.
Pre-transaction signals feed into risk assessment before the authorization request is submitted. Device fingerprinting, IP analysis, and behavioral data collected during browsing and checkout all contribute. Identifying card testing or ATO behavior early means stopping fraud before a transaction is approved rather than disputing it after.
Risk scoring at authorization is where each transaction is evaluated against a comprehensive signal set. High-risk transactions are declined or routed for review. Low-risk transactions proceed without friction. Sift assesses thousands of signals throughout the user journey to produce a Sift Score that reflects the full context of the transaction, not just its surface attributes.
Workflows and dynamic decisioning translate risk signals into automated actions. High-confidence fraud is declined immediately. Edge cases get elevated to manual review or additional verification. Dynamic friction applies challenges selectively, only when risk warrants it, rather than inserting friction into every elevated-risk session.
Post-authorization monitoring extends the prevention window. Fraud that passes authorization is often visible in downstream signals. Events like order modifications, address changes, or unusual fulfillment patterns all warrant additional monitoring. Monitoring post-authorization activity is especially important for platforms with high order values or long fulfillment windows.
Building a prevention program that scales
E-commerce payment fraud is an ongoing and ever-evolving problem for companies. Attack patterns change over time and fraud operations adapt to new controls over time. As a result, fraud detection must remain consistent and ongoing to keep fraudsters at bay.
Teams that rely on static rules and procedures face a recurring rebuild cycle, always one step behind fraudsters as they attempt to add new rules when fraud slips through and tune thresholds to manage false-positive rates. Machine learning models trained on network-wide transaction data adapt continuously, identifying emerging fraud patterns without manual intervention.
The operational side matters too. Review queues need to be sized and prioritized to surface high-value fraud without overwhelming analyst capacity, while escalation queues handle complex cases.
Sift provides the visibility fraud teams need to monitor and investigate fraud cases and respond to emerging threats before they compound into significant losses.
CNP fraud uses stolen card credentials directly without compromising an account. ATO fraud uses access to a legitimate account to place unauthorized orders with payment methods already on file. Both result in chargebacks, but ATO-driven fraud often goes undetected longer because the session starts with a trusted login.
Card testing is typically detected through velocity analysis: a high volume of low-value transactions from the same device, IP address, or email domain in a short period, followed by larger purchases. Machine learning models trained on network-wide data flag testing activity earlier than threshold-based rules because they can see patterns across thousands of platforms simultaneously.
Friendly fraud occurs when a legitimate customer disputes a valid charge, either intentionally to avoid payment or because they do not recognize the transaction. True payment fraud involves a third party using stolen credentials. Both result in a chargeback, but the evidence required to fight them differs. Transaction-level evidence, behavioral data, and delivery confirmation are most effective against friendly fraud disputes.
