Recognize Fraud in e-Commerce Business and Protect Your Customers


Fraud in e-commerce business is a pressing issue, involving deceptive practices that exploit online platforms for financial gain. This encompasses activities like identity theft, chargeback scams, phishing, and counterfeit sales. E-commerce fraud undermines consumer trust, leads to monetary losses, and tarnishes the reputation of businesses. To counteract this, companies deploy advanced security protocols, Artificial intelligence-driven fraud detection, and encryption techniques. Customer awareness about potential risks and adopting cautious online behaviors also play pivotal roles in curbing e-commerce fraud. Collaborative efforts between industry players, law enforcement, and consumers are crucial to establish a safer digital shopping landscape.

There are many ways scammers trick ecommerce algorithms and trick business owners into shipping products with no intention of paying for them using a verified account.

As scammers improve their ability to deceive merchants, eCommerce business owners need to have a strategy to fight fraud. This includes: verifying the identity of users, using backend tools to prevent spam and phishing, and protecting your assets with a comprehensive insurance policy.

Types of fraud in Ecommerce

These situations have evolved over the years, from exploiting cardholders, to full-blown fraudulent campaigns.

Let’s talk about the top six types of e-commerce fraud so you can develop a better perspective from which to execute your prevention strategy.

Card not present fraud

Card not present fraud or “CNP fraud” (card not present fraud) occurs when someone makes a purchase with a card that is not in their possession. E-commerce, with its digital nature, is a hotbed of “CNP” fraud cases.

Although almost all e-commerce stores require cardholders to verify the CVV code to prove ownership of the account, scammers know how to access this type of information through phishing scams and hacker marketplaces.

Chargeback Fraud

Chargeback fraud means that a customer initiates a chargeback after they have received their item, so they can keep their money and the product they received. But chargebacks don’t always happen on purpose. 

Friendly fraud is when a chargeback occurs by accident. Although the intent is not malicious, friendly fraud still accounts for 40-80% of e-commerce fraud losses.

Account Takeover Fraud

This type of fraud occurs when someone hijacks a customer’s account and uses it to purchase items, use reward points, and more.

Fraudsters often steal customer login credentials through phishing scams, in which they pose as legitimate businesses to gain the trust of their customers and trick them into divulging relevant account information. 

Account enrollment fraud

This type of fraud is similar to account takeover fraud, except that instead of taking over an existing user’s account, the scammer uses the stolen credit card information to create an account at an e-commerce store.

Mail Interception Fraud

When a scammer steals a credit card and uses it to send purchases to the cardholder’s real address. But before the package arrives, the scammer contacts customer support to change the shipping address.

This sneaky method bypasses fraud detection systems, stealing from the cardholder and the online business.

Brand impersonation

Brand spoofing fraud tricks customers into giving up crucial financial and personal information.

Scammers use logos and other insignia to make their fake social media pages or phishing emails look real so unsuspecting victims trust the attacker and fall for it.

Identify fraud in ecommerce

The impact of wire fraud can have far-reaching effects, so learning to identify these situations can help your online business avoid further damage.

Here are some tell-tale signs that could mean ecommerce fraud:

  • You receive a new device sign-in email notification, when you haven’t signed in to any new device.
  • You notice unusual login locations.
  • You notice that a user has purchased large quantities of the same product in a single transaction.
  • You get an influx of orders all at once.
  • Multiple orders are shipped to the same address with different cards (or vice versa).
  • You have multiple clients who use similar email addresses.

Fraud detection in e-commerce using machine learning


Fraud detection in e-commerce has become a critical application of machine learning, playing a pivotal role in safeguarding online transactions and protecting both consumers and businesses from fraudulent activities. As the e-commerce landscape continues to grow, so does the sophistication of fraudsters, making it imperative to deploy advanced techniques to identify and prevent fraudulent transactions.


Machine learning techniques have revolutionized fraud detection by enabling the automated analysis of large volumes of transactional data and the identification of patterns indicative of fraudulent behavior. These techniques leverage historical transaction data to train models that can distinguish between legitimate and fraudulent activities. Here’s how machine learning is utilized in fraud detection within the realm of e-commerce:

1. Data Collection and Preprocessing 


A diverse range of data is collected for analysis, including transaction amounts, locations, device information, user behavior, and more. This data is often noisy and unstructured, requiring preprocessing steps like normalization, feature extraction, and data cleaning to ensure accurate model training.

2. Feature Engineering 


Creating informative features is crucial for training effective fraud detection models. Features might include the time of day, location, IP address, purchase history, and more. Feature engineering aims to capture the nuanced patterns associated with fraudulent transactions.

3. Supervised Learning 


In supervised learning, historical data with labeled instances of fraud and legitimate transactions is used to train algorithms. Commonly used techniques include logistic regression, decision trees, random forests, and support vector machines. These models learn from past data to make predictions on new, unseen transactions.

4. Unsupervised Learning


Unsupervised techniques, such as clustering and anomaly detection, are valuable for detecting novel and previously unseen fraud patterns. Clustering algorithms group similar transactions together, while anomaly detection algorithms identify deviations from normal behavior.

5. Neural Networks and Deep Learning

Deep learning techniques, like neural networks, have shown promise in fraud detection due to their ability to automatically learn intricate patterns from data. Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks are used to capture sequential patterns in user behavior.

6. Ensemble Methods


Combining multiple models into an ensemble can enhance overall accuracy. Techniques like bagging and boosting aggregate the predictions of multiple models, mitigating the weaknesses of individual algorithms.

7. Real-time Analysis


Fraud detection in e-commerce often requires real-time processing to prevent fraudulent transactions before they are completed. Stream processing and online learning techniques allow models to adapt to changing fraud patterns quickly.

8. Behavioral Analysis


Machine learning algorithms analyze user behavior over time to establish normal patterns. Deviations from these patterns can trigger alerts for potential fraud. For instance, if a user suddenly makes a high-value transaction from an unfamiliar location, it might raise suspicion.

9. Graph Analysis


Fraudsters often collaborate and share resources. Graph analysis helps identify connections between seemingly unrelated accounts by creating networks of users, transactions, and other relevant entities.

10. Continuous Learning


Fraud patterns evolve rapidly. Continuous learning techniques allow models to adapt to new trends and tactics used by fraudsters. This involves retraining models periodically to ensure their effectiveness.

What type of machine learning is used for fraud detection?

Fraud detection heavily relies on supervised and unsupervised machine learning techniques. Supervised methods involve training models on labeled datasets with both legitimate and fraudulent transactions, allowing algorithms to learn patterns and make predictions on new data. Common algorithms include logistic regression, decision trees, and support vector machines. Unsupervised methods focus on anomaly detection, identifying unusual patterns that could indicate fraud. Clustering algorithms like k-means and density-based methods like DBSCAN help spot deviations from normal behavior. Additionally, advanced techniques like neural networks and deep learning enable the detection of intricate fraud patterns. A combination of these approaches enhances accuracy in identifying fraudulent activities.

Prevent fraud in e-commerce

There are no single solutions to prevent electronic fraud. However, a strategic approach to protection, using one or all of these tips, can prevent fraud on the best Ecommerce platforms:

Implement multi-factor authentication

Multi-factor authentication uses multiple authentication methods for customers to access their accounts. While adding additional friction, the added security with multi-factor authentication provides an extra layer of protection for your eCommerce business and your customers’ financial data.

Follow payment card industry standards

Payment Card Industry Standards, or PCI compliance, help merchants identify weaknesses and prevent credit card fraud by enforcing data security best practices and other legal requirements for e-commerce business owners.

Increase the security of your email

Since phishing emails are responsible for 91% of all fraudulent attacks, business owners need to develop protocols that protect their business email.

New email marketing regulations require businesses to take advantage of digital tools that prevent spam, phishing emails, and other fraudulent email activity.

Take advantage of SSL encryption

An SSL certificate helps keep e-commerce sites secure by encrypting sensitive data to hide it from prying hackers. 

SSL encryption increases customer confidence as they know your e-commerce site is committed to data protection and fraud prevention.

Fraud Monitoring Software

If you have an ecommerce business, you must be able to detect suspicious orders to stop fraud. There is software that provides a full risk analysis including transaction details, geolocation, device tracking and much more.

Leave a Reply