Payment fraud has evolved. It’s faster, more sophisticated, and harder to spot—especially when your business is juggling multiple markets, payment methods, and digital touchpoints.
Protecting revenue today means thinking beyond basic fraud filters. It’s about building smart systems that work in real time, integrating the right tools into your stack, and making sure everyone knows who’s responsible when something goes wrong. So where are the risks shifting—and how do you stay one step ahead without putting conversion rates on the line?
Common types of payment fraud
Online payment fraud is now embedded within global commerce at a scale that’s hard to ignore. According to Juniper Research, online payment fraud is projected to surpass US$362 billion globally between 2023 and 2028, with a staggering $91 billion in losses expected in 2028 alone. Credit card fraud alone is on track to hit $43 billion by 2026. In parallel, nearly 1,200 scam domains linked to fraudulent merchant accounts have been traced—many registered in the UK and Hong Kong.
While these types of fraud vary widely in method and intent, many begin with the same entry point: phishing. As a tactic, phishing involves tricking individuals into revealing sensitive details like login credentials, card information, or bank account numbers. These stolen details are then used to fuel broader attacks such as account takeover or wire transfer fraud. In 2024, phishing remained the most common initial attack vector in payment fraud globally, and its low cost and high success rate make it a favourite among fraudsters.
Check out the top fraud trends global brands need to watch out for in 2025.
Below is an overview of the common types of payment fraud, how they work, what signs to look for, and their typical impact on merchants and customers:
|
Fraud type |
Description |
Signs |
Effect |
|
Stolen credit card fraud |
Use of stolen card details to make purchases |
Multiple failed attempts, mismatched billing/shipping info |
Merchants lose goods and funds; customers face unauthorised charges |
|
Account takeover (ATO) |
Fraudster gains access to a legitimate user account |
Unusual login locations/devices, changes to account or payment info |
Orders placed without customer knowledge; damage to brand trust |
|
Wire transfer fraud |
Fraudster impersonates a vendor to divert funds |
Unusual payment requests, pressure to act quickly |
Irreversible fund loss for the business; no product delivered to customer |
|
Friendly fraud |
Legitimate customer disputes a valid transaction |
Frequent chargebacks from same buyer, vague dispute reasons |
Increased chargeback fees, potential penalties for high dispute rates |
|
Synthetic identity fraud |
Use of fake credentials combining real and fake info |
Inconsistent identity details, multiple small transactions |
Build-up to large fraudulent order; loss of goods and trust in verification systems |
|
Triangulation fraud |
Fraudster uses stolen data to fulfil orders made by real customers |
Mismatched order details, orders placed unusually quickly |
Customer receives item; merchant loses goods and payment; customer unaware of the fraud |
How payment fraud detection systems work
Rule-based vs. machine learning systems
Rule-based systems rely on pre-set conditions: block transactions from certain IPs, flag high-value orders, etc. They’re simple to implement but can’t adapt quickly. Machine learning models, by contrast, learn from historical data and adjust their predictions to catch previously unseen fraud patterns.
Real-time transaction fraud prevention with behavioural analytics
Speed matters. Real-time fraud detection uses behavioural analytics to spot anomalies in how a customer moves through the checkout process. Repeated failed payment attempts? Mismatched billing and shipping details? These small signals become big warnings when combined.
Device fingerprinting and velocity checks
Every device has a unique combination of attributes—browser type, language settings, operating system. Device fingerprinting helps detect fraudsters who use the same device for multiple suspicious transactions. Velocity checks monitor how fast and how often transactions occur from the same IP, email, or account.
Antom’s integrated fraud monitoring engine
Antom offers an intelligent risk engine with real-time fraud scoring, velocity thresholds, and rule customisation. Merchants can adjust their tolerance for risk based on order value, location, and payment method—all within a single interface. It's particularly useful for businesses operating in markets like Brazil and Mexico, where local checkout preferences vary widely.
Payment fraud prevention strategies that work
Identity verification: 2FA, biometrics & document scanning
Layered verification methods—such as two-factor authentication, biometric login, or document checks—add friction for fraudsters, not genuine buyers. For high-value orders or voucher payment methods, it’s a practical step.
Chargeback management best practices
Quick response matters. Always track your payment confirmation data, maintain clear billing descriptors, and document every customer interaction. A disputed payment, if not handled quickly, becomes a chargeback that affects your risk profile.
Blacklists, whitelists and dynamic risk scoring
Use blacklists to block repeat offenders and whitelists to streamline low-risk regulars. Dynamic risk scoring lets you score each transaction based on real-time data, adjusting the checkout process dynamically.
KYC and AML compliance: a regulatory imperative
For companies operating globally or accepting alternative payment solutions, KYC and AML controls are essential. These checks help verify customer identities and detect suspicious fund flows, especially for high-risk payment methods like wire transfers.
Employee awareness and internal fraud risks
Not all threats come from outside. Train employees to recognise fraud indicators and restrict access to sensitive systems. A single error in verifying an order or processing a refund can result in significant fund leakage.
Machine learning’s role in modern fraud detection
Training and evaluation of fraud models
The quality of the model depends on the quality of the data. Use anonymised transaction logs to teach fraud models how to identify patterns of misuse across customer segments, payment methods, and locations.
Risk scoring and real-time decisioning
Each transaction receives a score based on hundreds of data points—amount, currency, order history, checkout behaviour. Depending on the score, the system can allow, block, or route the transaction for manual review.
Adaptive learning from historical patterns
Machine learning models don't just detect fraud—they improve over time. By continually learning from both successful and blocked transactions, your fraud prevention setup becomes more accurate and less prone to false positives.
Why payment fraud analytics are a game-changer
Actionable insights for fraud teams
Dashboards that combine payment method performance, fraud attempts, and refund anomalies allow teams to spot irregularities faster. For instance, are orders using GrabPay payment in Indonesia showing an unusual failure rate? That’s worth a closer look.
Optimising approvals without compromising security
Striking the right balance between blocking fraud and approving genuine transactions is tricky. Fraud analytics help reduce friction by tailoring security controls based on risk—keeping checkout smooth for real buyers and effective against fraudsters.
How Antom helps merchants detect and prevent payment fraud
Real-time monitoring with intelligent risk control
Antom Shield evaluates each transaction using advanced risk scoring. For checkout environments with high voucher or cash payment usage, Antom automatically adjusts controls to maintain a low risk of fraud.
Integrated analytics dashboard and unified reporting
Track your entire transaction lifecycle—from payment method used to payment confirmation—in one view. Whether you're analysing in-store orders or online purchases, Antom centralises the data.
PCI-DSS compliant architecture with global fraud controls
Every transaction—whether it's a cash payment or a debit card purchase—is protected by Antom’s globally compliant infrastructure.
Seamless integration with online and in-store solutions
Antom’s modular APIs and SDKs make it easy to integrate fraud prevention into any checkout process, including bills and online purchases in-store. Whether the customer pays via voucher, bank account, or alternative payment method, fraud controls activate automatically.
