Fraud is a persistent threat for businesses of all sizes. Fraudsters take advantage of the speed and convenience of online shopping to exploit vulnerabilities. For merchants, this translates into financial losses, reputational damage, and operational strain. With global fraud losses climbing each year, prevention is no longer optional. Businesses need clear strategies to spot risks early and protect both themselves and their customers.
Growing risk of e-commerce fraud
Online fraud is growing rapidly worldwide. According to Mastercard, global e-commerce fraud losses reached $41 billion in 2022 and $48 billion in 2023, with cumulative losses projected to exceed $343 billion between 2023 and 2027. This scale highlights how costly inaction can be.
Different regions experience varying levels of risk. In Latin America, e-commerce fraud accounts for 4.1% of online revenue, the highest globally. The Asia-Pacific region follows at 2.6%, while the United States accounts for 40% of all global fraud attacks. For businesses expanding internationally, these differences matter when assessing exposure.
Fraud tactics are evolving as well. In 2024, refund and policy abuse became the most common type of attack, affecting nearly half of merchants worldwide. Real-time payment fraud and phishing also ranked high, showing the diversity of threats that businesses must prevent.
Types of e-commerce fraud
Fraudsters deploy many different schemes. Understanding them helps businesses spot patterns before damage occurs.
|
Type |
Description |
Example |
Risk to merchant/customer |
|
Payment fraud |
Use of stolen credit card details in card-not-present transactions |
Fraudster buys high-value electronics with stolen card |
Merchant loses goods and pays chargeback fees |
|
Account takeover (ATO) |
Fraudster gains access to a customer’s account |
Compromised login used to make unauthorised orders |
Loss of trust, customer disputes |
|
Friendly fraud |
Genuine customer disputes a legitimate charge |
Customer claims non-receipt of goods |
Merchant absorbs loss and fees |
|
Return abuse / triangulation |
Exploiting returns or using fake intermediaries |
Fraudster runs a fake site to pass orders through |
Inventory and revenue losses |
|
Promo code abuse |
Exploiting discount systems |
Multiple accounts using one-time codes |
Erodes margins and distorts campaigns |
|
Bot-driven fraud |
Automated scripts simulate user activity |
Card testing via thousands of rapid-fire attempts |
System overload, stolen data testing |
|
Phishing & social engineering |
Deceiving staff or customers to share details |
Fake emails imitating retailer support |
Leads to account breaches or stolen funds |
|
Synthetic identity fraud |
Use of fabricated identities |
Fraudster combines stolen and fake data |
Hard-to-detect long-term losses |
How fraud affects businesses
Fraud has more than one impact. The most direct is financial loss: chargebacks, lost goods, refund abuse, and fees all reduce margins. For smaller businesses, even a few chargebacks can damage cash flow.
The operational burden is significant too. Fraud detection often involves manual reviews, which tie up staff resources and slow down legitimate transactions. As fraud attempts increase, this workload grows.
Then there is reputation. Customers expect safe online shopping. When fraud affects them, trust is eroded, and recovery is slow. Larger businesses may have tools to absorb and mitigate losses, but smaller merchants often lack advanced fraud detection systems, making them more vulnerable.
Fraud detection methods
Fraud detection relies on a mix of technology and process. Businesses can prevent fraudsters from succeeding by adopting multiple techniques:
Behavioural analysis
Monitoring user actions—like unusual navigation, rapid clicks, or abnormal cart additions—helps flag suspicious activity. Machine learning models build baselines and spot anomalies quickly.
Device fingerprinting
Every device has unique traits such as browser type, operating system, and screen resolution. Tracking these helps identify when the same device is used across multiple suspicious accounts or payment failures.
Geolocation and IP analysis
Comparing IP addresses with billing and shipping details can highlight mismatches. Fraud from high-risk regions, or through VPNs and proxies, is often detectable this way.
Velocity checks
Multiple orders from the same card or IP address in a short time are red flags. These checks are effective against card testing and bot-driven fraud.
Address Verification Service (AVS)
AVS compares the billing address provided with the one held by the card issuer. Mismatches signal possible stolen card use.
Card Verification Value (CVV) checks
Requiring CVV ensures that the buyer has physical access to the card. This is a standard step against card-not-present fraud.
3D Secure (3DS2) authentication
Adding an extra authentication step, such as one-time passwords or biometrics, shifts liability to the issuer and reduces fraud risk.
Machine learning and AI-based scoring
Advanced fraud detection systems, such as Antom Shield, use big data and AI to assign risk scores in real time. High-risk scores can trigger manual reviews or automatic rejection.
Bot detection tools
Bots can be detected using CAPTCHAs, traffic pattern monitoring, and browser analysis. Blocking them prevents card testing and automated account breaches.
Manual review processes
Human oversight remains important for edge cases. High-value or unusual orders can be reviewed before approval to minimise false positives.
Warning signs of fraud
- Mismatched billing and shipping addresses: Often an indication that a fraudster is attempting to send goods to an address different from the legitimate cardholder’s.
- High-velocity transactions: Multiple rapid orders from the same account or card can signal card testing or bot activity.
- IP addresses from unusual locations or proxies: Transactions from regions with high fraud rates, or where VPNs are in use, may indicate risk.
- Multiple failed payment attempts: Suggests trial-and-error attempts with stolen card details.
- Suspicious email addresses: Random strings or temporary email accounts are frequently used by fraudsters.
- Large or bulk orders without history: A sudden purchase of high-value items from a new or inactive account is a red flag.
- Device or browser inconsistencies: Frequent switching between devices or unusual browser settings can signal fraud.
- Unusual shopping behaviour: Odd purchase patterns, such as random items in a cart, may indicate non-genuine intent.
- Frequent returns or repeated chargebacks: Customers engaging in this behaviour may be abusing refund policies.
- Customer service requests that bypass normal checks: Requests to redirect shipments or skip verification processes can point to fraud attempts.
Strategies to prevent online fraud
- Use multi-layered fraud detection: Combine AVS, CVV, velocity checks, and behavioural analysis.
- Adopt strong authentication: Require 3DS2 where applicable, especially for high-value transactions.
- Educate staff and customers: Train employees to spot social engineering attempts and encourage customers to use strong passwords.
- Automate where possible: AI-powered scoring tools improve speed and accuracy while reducing manual workload.
- Balance prevention with experience: Avoid measures that frustrate genuine buyers. Fine-tune rules to reduce false positives.
- Stay updated on regional risks: Fraud tactics differ across markets. Tailor your fraud detection strategy accordingly.
- Partner with trusted providers: Services like Antom Shield provide real-time risk assessment and fraud prevention tools designed for merchants operating globally.
Conclusion
E-commerce fraud presents a growing challenge, but it is one that can be managed with the right strategies. By learning to recognise warning signs, applying effective fraud detection methods, and adopting best practices, businesses can significantly reduce their exposure. Working with partners like Antom gives merchants access to advanced fraud prevention tools, helping protect revenue while ensuring safe and smooth online shopping experiences for customers.
