Electronic commerce, popularly known as e-commerce refers to the buying and selling of goods or services over the Internet or other electronic platforms. It involves the use of websites, mobile applications, and other digital channels to facilitate transactions between buyers and sellers.
 

E-commerce can involve various types of transactions, including business-to-consumer (B2C), business-to-business (B2B), consumer-to-consumer (C2C), and consumer-to-business (C2B) transactions. Some common examples of e-commerce platforms include online marketplaces like Amazon and eBay, online retailers like Zappos and ASOS, and online service providers like Airbnb and Uber.
 

E-commerce has become increasingly popular in recent years due to its convenience and accessibility, and it has transformed the way many businesses operate. The COVID-19 pandemic and its resultant worldwide lockdown in 2020 contributed in no small way to the growth and popularity of e-commerce, which has created new opportunities for entrepreneurs and small businesses to reach a global market and access global customers which were only accessible by big companies.

 

What Is E-commerce Fraud Detection?

 

E-commerce fraud detection is the process of identifying and preventing fraudulent activities that occur during online transactions. E-commerce fraud can take many forms, including credit card fraud, identity theft, and phishing scams.
 

E-commerce fraud detection typically involves using various techniques and technologies to identify potentially fraudulent transactions. These may include machine learning algorithms, statistical models, and rule-based systems that analyze transaction data in real-time to detect patterns and anomalies that may indicate fraud.
 

Some common indicators of e-commerce fraud include unusually large purchases, purchases made from high-risk locations or IP addresses, and the use of multiple credit cards to make purchases. Once potential fraud is identified, e-commerce fraud prevention measures may be taken, such as blocking transactions, flagging suspicious accounts, or requiring additional verification steps before allowing a transaction to proceed.
 

Effective e-commerce fraud detection and prevention are critical for protecting both consumers and merchants from financial losses and reputational damage caused by fraudulent activities.
 

Related Article: Digital Solutions for B2B Cross-Border Payments

 

Types Of E-commerce Fraud

 

There are several types of e-commerce fraud. But in this article, a few have been highlighted and briefly discussed below:

 

a. Card-not-present (CNP) fraud

 

This is the most common type of e-commerce fraud, in which a fraudster uses stolen or counterfeit debit or credit card information to make purchases online. The fraudster does not need to be in physical possession of the card in order to perpetrate this type of fraud. 

 

b. Identity theft

 

In this type of fraud, a criminal steals a person’s personal information, such as name, address, and identity numbers such as National Identity Number (NIN) and Social Security Number (for Nigeria and the United States respectively), to make fraudulent purchases or apply for loans and credit cards. Identity Spoofing is another commonly applied method.

 

c. Friendly fraud

 

Also known as chargeback fraud, this occurs when a customer makes a purchase and then disputes the charge with their payments processing company, claiming that they never received the product or that it was defective. The fraudulent customer here would typically request a refund.
 

d. Affiliate fraud

 

This type of fraud occurs when an affiliate marketer or partner engages in fraudulent activities to earn commissions or benefits, such as inflating the number of clicks or transactions to boost their earnings.
 

e. Phishing and phishing scams

 

These are fraudulent emails or messages that trick the recipient into revealing their personal or financial information.

 

f. Account takeover (ATO) fraud

 

This occurs when a fraudster gains access to a customer’s account through a stolen password or other means, and then makes unauthorized purchases or transfers.
 

g. Triangulation fraud

 

In this type of fraud, a criminal creates a fake online storefront, where they advertise products at a low price. Once a customer makes a purchase, the fraudster uses stolen credit card information to buy the product from a legitimate retailer and has it shipped directly to the customer, while keeping the difference in price as profit.
 

To avoid e-commerce fraud, it is important for merchants to implement robust security measures, such as SSL encryption, two-factor authentication, and fraud detection software, and for customers to be vigilant and take steps to protect their personal and financial information.

 

See more common types of ecommerce fraud and how to prevent them

 

What Are The Major Ways Of E-commerce Fraud Detection?

 

The major ways of e-commerce fraud detection include:

 

a. Address Verification System (AVS)

 

The Address Verification System is a fraud detection tool that verifies the billing address of the cardholder against the address on file with the debit or credit card issuer. If the addresses do not match, the transaction can be flagged for further review.

 

b. Card Verification Value (CVV) Check

 

This is another fraud detection tool that requires the cardholder to enter a three or four-digit code on the back of their debit or credit card. If the code is incorrect or missing, the transaction is delayed, flagged for further review or not processed at all.

 

c. Geolocation

 

This is a fraud detection tool that uses the location of the device used for the transaction to determine whether the transaction is legitimate or not. Over time, certain areas are marked as high-risk areas. If the device used for the transaction is located in a high-risk area, the transaction can be marked for additional scrutiny.

 

d. Fraud Scoring

 

This is a system that assigns a score to each transaction based on various factors such as the amount of the transaction, the location of the buyer, the shipping address, and other factors. If the score is above a certain threshold, the transaction can be identified as suspicious and designated for further evaluation.

 

e. Machine Learning

 

This is a fraud detection tool that uses algorithms to identify patterns in data that can indicate fraudulent activity. Machine learning algorithms can be trained on historical data to improve their accuracy over time. Machine learning algorithms can analyze hundreds or even thousands of data points associated with a transaction, including customer behaviour, device used, location, purchase history, and more. 

 

By identifying patterns and anomalies in this data, machine learning algorithms can predict with a high degree of accuracy whether a transaction is likely to be fraudulent or legitimate.

 

f. Manual Review

 

This is a fraud detection method where transactions are manually reviewed by a human analyst to identify any suspicious activity that may have been missed by automated fraud detection tools. The human eyes have been known to detect things that machines have missed picking.
 

By using a combination of these e-commerce fraud detection methods, online merchants can reduce the risk of fraudulent transactions and protect their businesses and customers from financial losses.

 

Conclusion

 

Having seen the types of e-commerce fraud and the different ways of detecting them, it behaviours on e-commerce merchants and customers to take precautionary measures to ensure they do not fall victim to any of the numerous forms of e-commerce fraud. 
 

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