6 eCommerce Fraud Tactics Every Retailer Must Know

As global eCommerce continues its rapid growth, so does the risk of digital fraud. The eCommerce fraud detection market is projected to hit $102.28 billion within the next two years, reflecting the rising threat landscape online retailers face. In 2024 alone, businesses lost $103 billion to fraudulent returns and claims—about 15% of the $685 billion in total returns.

Fraud is no longer just a risk—it's a daily challenge. In this issue, we explore the six most common types of eCommerce fraud threatening merchants today and provide practical guidance on how to detect and defend against them.

🔍 What Is eCommerce Fraud?

eCommerce fraud involves criminal tactics designed to exploit online retailers, from stolen credit card transactions to more advanced schemes like triangulation or account takeovers. Fraudsters often impersonate real customers using fake identities or compromised data, resulting in chargebacks, financial losses, and a damaged reputation.

The 6 Most Common Types of eCommerce Fraud

1. Transaction Fraud

Also known as credit card fraud, this occurs when stolen payment details are used to make unauthorized purchases. Businesses not only lose revenue but also face chargeback penalties and may be labeled high-risk by payment processors.

💡 How to prevent it:
Implement real-time transaction monitoring, device intelligence, and velocity rules to detect anomalies early.

2. Chargeback Fraud (Friendly Fraud)

This type of fraud happens when customers dispute legitimate transactions, claiming:

• They didn’t receive the item

• The product wasn't as described

• They don't recognize the charge

Some customers act opportunistically, while others plan maliciously to exploit store policies.

💡 How to prevent it:
Keep detailed transaction records, use delivery confirmation tools, and employ chargeback mitigation services to dispute false claims.

3. Return Fraud

Fraudsters manipulate return policies by:

• Swapping genuine items with damaged ones (Switch Fraud)

• Removing parts from electronics before returning (Bricking)

• Exploiting open-box discounts

• Buying in bulk to create false scarcity, then returning items

💡 How to prevent it:
Strengthen your returns policy, use serial number tracking, and employ image/video verification for high-value returns.

4. Wardrobing

Customers wear or use products (especially clothing) and return them as “unused.” This practice is widespread and particularly difficult to manage in fashion and lifestyle eCommerce.

💡 How to prevent it:
Apply return condition checks, limit free return shipping, and flag users with frequent returns.

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5. Triangulation Fraud

This multi-layered scheme involves three parties:

1. A real customer buys from a scam seller (on platforms like Amazon or eBay).

2. The scammer uses stolen card data to order from your legitimate store.

3. The real customer receives the item, while you face the chargeback.

💡 How to prevent it:
Use AI tools to detect abnormal buying patterns, BIN lookups, and flag orders with suspicious mismatches in billing/shipping.

6. Account Takeover (ATO)

Hackers gain access to admin or customer accounts via phishing, credential stuffing, or malware. They can lock out rightful owners, steal data, and reroute payments to their own accounts.

💡 How to prevent it:

• Enforce strong authentication (e.g., 2FA)

• Monitor login behavior

• Use device fingerprinting and IP intelligence

Detecting Fraud

To stay ahead, focus your fraud detection strategy on three key touchpoints:
Signups, logins, and transactions.

Here are key techniques to bolster your defenses:

• Data analytics: Spot red flags in user behavior and payment data

• BIN lookups: Identify high-risk card types and geographies

• Digital footprint analysis: Use email, phone, and social data to verify identities

• Velocity checks: Monitor rapid purchase or login attempts

• Shipping pattern analysis: Watch for inconsistencies or reshipping tactics

🛠️ Must-Have Tools for 2025

Fraud prevention in 2025 hinges on two core technologies:

1. Digital Footprint Analysis

• Analyze the online presence behind user data—email, phone, IP address—to validate if a user is legitimate or fraudulent. Great for flagging synthetic or stolen identities.

2.Device Intelligence

• Detect emulators, VPNs, and suspicious behavior from devices in real time. By understanding the device profile, merchants can differentiate between fraudsters and genuine users.

• Static rules like “block orders over $1,000 from X country” are easy for fraudsters to outsmart. In contrast, machine learning models evolve with every transaction, identifying subtle trends, relationships, and fraud patterns in real time.

Fraudsters never rest—and neither should your fraud prevention strategy. With the right combination of technology, strategy, and vigilance, your business can stay ahead of evolving threats while delivering a safe, seamless customer experience.