By Elie Y. Katz
National Retail Solutions (NRS)
The COVID-19 pandemic impacted credit card fraud trends by fueling explosive growth in card fraud activity. Fraud involving existing credit cards jumped 69 percent in 2021 from the year before, resulting in $9 billion in costs. During the pandemic, a significant amount of fraud shifted from physical stores to ecommerce sites, with criminals focusing on stealing personal information to make fraudulent purchases online.
Trending types of credit card fraud include card-not-present (CNP) fraud, phishing, smishing and vishing schemes, and account takeover (ATO). Phishing uses emails and links, smishing uses text messages or common messaging apps, and vishing uses voice calls and voicemails to obtain sensitive information. In an ATO attack, a fraudster gains unauthorized access to a user's online account credentials.
Credit card skimming involves using a device to steal credit card information, such as the card number and expiration date, from a card's magnetic stripe. Criminals use this information to make unauthorized purchases or create counterfeit cards. Skimming devices are placed on ATMs, gas pumps or other payment terminals and are often difficult to detect.
Regarding credit card fraud, store owners must understand their liability in accepting online orders versus in-store purchases. In a card-present (CP) transaction, where the cardholder is physically present at the POS, the merchant can verify the card's authenticity through identification checks and chip-enabled cards. A merchant that follows proper security procedures is not liable for fraudulent purchases; the responsibility lies with the issuing bank.
However, in CNP transactions, where the cardholder is not physically present, the liability shifts to the merchant. Without the ability to physically inspect the card or confirm its legitimacy, online merchants must bear the responsibility for fraudulent orders and provide refunds to customers.
This puts them at risk of financial loss, including the costs of refunding scammed customers and potential fees imposed by acquiring banks for chargebacks. It's imperative for merchant service providers to help store owners understand these distinctions and implement effective fraud prevention measures to protect themselves and their customers.
Preventing fraud and reducing chargeback rates require specific measures. While EMV chip technology reduced card-present fraud in the retail industry, retailers remain susceptible to various fraudulent schemes and attacks. ATOs are prevalent, where fraudsters exploit customer accounts for unauthorized rewards or payment usage.
First-party misuse, or friendly fraud, involves customers disputing legitimate purchases for false reasons. Delivery, curbside pickup, and BOPIS (buy online, pickup in store) orders are particularly vulnerable to first-party misuse due to the difficulty in proving them false. Retailers also face the risk of CNP fraud, such as card testing, wherein fraudsters attempt small purchases to see if cards are still valid.
Ignoring the risks of fraud and chargebacks can lead to escalating attacks, higher rates of fraud and chargebacks, and costly fees. Even a tiny fraudulent transaction can result in substantial revenue loss and fees. Additionally, card networks monitor fraud and chargeback rates, potentially resulting in fines, restrictions or loss of merchant accounts. Thus, retailers must develop comprehensive strategies to combat fraud and minimize chargebacks.
Combatting credit card fraud in retail environments requires a layered approach incorporating multiple preventive measures. Retailers can adopt five best practices to prevent credit card fraud effectively and thereby significantly reduce credit card fraud and protect their businesses:
Credit card companies are also taking measures to protect merchants from credit card fraud. For example, Visa uses Visa Advanced Authorization to detect and prevent fraudulent purchases and offers a variety of materials to support fraud prevention, security and risk management efforts; Mastercard has a fraud detection system to protect merchants from fraudulent transactions; and American Express uses fraud protection enhanced by machine learning to analyze numerous real-time risk variables.
Merchants can reduce unauthorized credit card transactions by using EMV-enabled card readers. Merchants can also take steps to limit the risks of credit card fraud, such as training staff about fraud, halting suspicious purchases, and using PCI-compliant payment systems.
Selecting the right POS system is critical in the battle against store credit card fraud. It's like having a reliable security guard at the entrance of your business, ensuring that only legitimate transactions get through. A top-notch POS system comes equipped with advanced security features that shield customer data during payment processing, like encryption and tokenization.
The POS system should proudly display industry certifications, such as PCI DSS compliance, which means it has undergone rigorous testing to meet the highest security standards. By making an intelligent choice regarding a POS system, a merchant is fortifying a store's defenses, safeguarding precious customer information, and keeping the business running smoothly and securely.
Several innovative technologies are being used to prevent credit card fraud. Recent innovations include universal credit cards, credit cards with push buttons, and finger/hand detection methods to protect from fraud. Major credit card networks use fraud protection enhanced by machine learning that analyzes numerous variables for risk in real time. Additionally, several proactive security measures can be taken to increase digital security, such as card security features like AVS, 3DS, and CVV that verify the cardholder's identity.
Machine learning enables real-time insights and identifies fraudulent transactions in vast volumes of data (see https://tinyurl.com/4x45zbvu). Artificial neural networks trained using a simulated annealing algorithm effectively identify fraudulent credit card transactions.
A machine learning-based credit card fraud detection engine has been proposed using the genetic algorithm for feature selection. Machine learning models are among the most prominent techniques in detecting illicit transactions. Autonomous credit card fraud detection using machine learning approaches has also been developed.
CP and CNP fraud come with different liabilities for merchants and issuing banks. To combat this problem effectively, retailers must implement strong security measures, verify customer identities, educate their employees, retain transaction records, and stay updated on industry trends.By using advanced tools like AI-powered fraud filters and authentication protocols, retailers can enhance the security of online transactions and identify suspicious activities. Implementing verification processes for delivery and pickup orders helps ensure that products reach the right customers and minimizes the risk of first-party misuse.
Training employees to recognize signs of fraud and retaining transaction records as evidence are crucial for defending against fraudulent chargebacks. And staying informed about industry trends and adhering to card network guidelines are essential for compliance and minimizing vulnerabilities.
Credit card issuers also play a role in fraud prevention by employing machine learning and real-time risk analysis. And retailers should already be using EMV-enabled card readers and PCI-compliant POS systems. Combining these measures with continuous monitoring and adaptation will help retailers stay ahead of evolving fraud and safeguard their businesses. As merchant service providers, you can help retailers take proactive steps to reduce the financial losses and reputational damage associated with credit card fraud while ensuring the safety and trust of their customers. 
Elie Y. Katz is founder, president and CEO at National Retail Solutions (NRS), https://nrsplus.com. Contact him by phone at 201-715-5179 or by email at ekatz@nrsplus.com.
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