Several common signs of credit card fraud include damaged or phony cards, agitated shoppers, and avoidant behaviors around signatures and purchases. While these are all definite signs of credit card fraud, many instances are the result of social engineering attacks. Social engineering attacks try to entice unsuspecting victims to divulge personal information or make large purchases. Phishing and vishing email scams are common examples. Text message scams, or smishing, are also common.
There are several strategies a merchant can implement to prevent fraudulent transactions. These strategies may include chargebacks, address verification service, 3D Secure, and Machine learning-based risk decisions. Merchant fraud protection is necessary for business owners. To reduce the risk of fraud, you must understand these strategies and how they can affect your business. After reading this article, you should be better equipped to avoid chargebacks. Let’s start by defining fraud.
Adding Machine Learning, like merchant payments fraud prevention from Accertify, to a merchant’s strategy to prevent fraudulent transactions allows for more efficient fraud detection.
Chargebacks
Fraud is a significant problem for merchants, as it affects their bottom line and their customer experience. According to CMSPI, U.S. retailers will lose $14 billion to fraud by 2021. But there are steps you can take to protect your business from this growing problem. The following are four common challenges to fraud prevention strategies. Understanding them will help you design a strategy that will work best for your business.
One of the most common forms of fraud is account takeover. Fraudsters steal the credit card numbers of innocent customers and then use them to buy products online. The merchant fulfills the fraudulent orders, but the actual cardholder requests a refund from the credit card company. The merchant is then left with a chargeback fee, lost revenue, and stolen merchandise. This situation is costly for both the merchant and the credit card company, that is why merchants should develop cnp fraud solutions to prevent chargebacks.
Address verification service
One of the most effective strategies to prevent fraudulent transactions on your eCommerce site is an Address Verification Service (AVS). This service checks a consumer’s billing address against the information held on file with their issuing bank. If they do not match, the AVS will return an AVS code, which will be helpful for merchants to use to verify the consumer’s identity. In addition, the AVS code can help you avoid chargebacks.
One common mistake that eCommerce merchants make is overpaying for an Address Verification Service (AVS). To avoid overpaying for an AVS, choose a provider that waives its fee or charges a pass-through cost. This extra charge can add up quickly if applied to every transaction. Another way to minimize your risk is by setting an automatic rejection policy through your payment gateway. Finally, remember to balance the cost of fraud against the lost sales.
3D Secure
The 3D Secure process changes the way customers make purchases. Instead of submitting their credit card details, customers are prompted to enter their issuing bank login credentials or biometric authentication before completing their purchase. Once they submit these details, the customer is redirected to the issuing bank’s website, where they are authorized and turned to the merchant’s payment solution. This process reduces the risk of fraudulent transactions and decreases the conversion rate.
Initially, 3-D Secure was designed for use on computers. The updated 3-D Secure 2.0 protocol allows it to work with a broader range of technologies and make the consumer experience seamless. This ensures a secure transaction while preventing fraud. However, it’s important to remember that consumers don’t always use a computer or use their mobile phones instead. Therefore, a merchant’s 3D Secure strategy should consider these factors before implementing this technology.
Machine learning-based risk decisioning
Adding Machine Learning to a merchant’s strategy to prevent fraudulent transactions allows for more efficient fraud detection. Criminals study buying patterns and behaviors, and Machine Learning is an effective way to differentiate between an actual buyer and a fraudster. Advanced anti-piracy software can even prevent regular browsers from recognizing them as pirates. Experienced hackers also create virtual IP addresses and machines to avoid detection.
Machine learning algorithms can differentiate between a genuine customer and a fraudulent one with a trained model. These models can evaluate hundreds of variables in real-time, which speeds up the process and helps analysts spot fraud schemes before they occur. Machine learning-based risk decisions in a merchant’s strategy to prevent fraudulent transactions can increase revenue by 50 percent. Unfortunately, while every online business wants to improve its transaction volume, processing each transaction can be cumbersome and error-prone.
