The Difference Between Perception and Reality in Credit Card Security
The vast majority of users feel secure when they see “https,” yet 70% of cyber attacks occur through these secure connections. According to a study conducted in the US, 43% of users perceive the visual aesthetics of a webpage as a security indicator when entering their credit card information. Some fraudulent websites can create exact copies of real bank websites using copied SSL certificates; users may share their card information without realizing it.
Some e-commerce sites place fake “secure payment” logos on their pages; whether these logos are clickable or not can indicate whether the site has a real certificate.
Noteworthy Cyber Attack Methods
One of the most common methods for stealing credit card information is “formjacking”; in this method, while the shopping site appears to be functioning normally, data is sent directly to the hacker's server in the background. Some hacker groups have even begun collecting credit card information through live chat bots integrated into websites; at the end of the conversation, a seemingly innocent offer like “Can we help you with payment?” is presented.
The “skimming” method is no longer physical but can also be used in virtual POS systems; even the site owner may be unaware that malicious code has been loaded onto their system. In a cybersecurity test conducted in the US, live background code that stole credit card information from users was detected on 15 of 100 popular e-commerce sites.
User Behavior and Risky Habits
Most users do not pay attention to browser warnings when they reach the payment screen; especially on mobile devices, warnings are displayed in smaller sizes and are easily overlooked. Many people agree to save their credit card information on a shopping site when creating an account, which creates a chain reaction risk in the event of a data breach.
The percentage of users who click on fake “your payment failed” messages in their email inbox and re-enter their card details reaches 12% in some regions. A study conducted in the US found that the most frequently stolen card details were obtained during purchases made between 9 PM and 1 AM, as users tend to be less vigilant during these hours.
Mobile Shopping and App-Based Threats
Some mobile shopping apps can access the clipboard on the device without the user's knowledge and retrieve copied credit card numbers. Fake shopping apps can reach the top of the App Store and Google Play during discount periods, especially with keywords such as “Black Friday” or “Cyber Monday.”
Many users grant shopping apps access not only to their credit cards, but also to their device's location, microphone, and even their contact list, which increases the risk of indirect data breaches. Some mobile browsers cache screenshots, making information about past purchases accessible outside the app.
Fake Websites and Social Engineering Games
Scammers are now operating in a much more sophisticated manner than the classic “fake site” approach; they convince users by presenting links disguised as Instagram ads or recommendations from well-known blogs. Social engineering tactics that trigger the urge to shop, such as messages like “90% discount valid only today,” prompt users to act without thinking.
Many people mistake shopping ads on social media platforms for genuine brands and enter their card details on fake websites. Scams involving messages such as “Re-enter your card details to receive an instant refund” are most commonly used for product groups with high refund rates.
Are Security Recommendations as Effective as They Seem?
While using virtual cards may seem like a serious security measure, many scammers can now bypass these cards by using methods such as limit updates or installment inquiries. Two-factor authentication (2FA) is generally secure; however, scammers can obtain live verification codes from users through fake bank calls.
While card information saved in browsers may appear to be “password-protected,” many people use simple passwords like “1234” to secure their browser passwords. Even in developed countries, many users do not use a VPN when shopping over public Wi-Fi, which can make their data easily accessible to third parties.
Striking Examples from Real Life
In 2020, a major US-based e-commerce site was attacked, and the credit card information of over 200,000 users was leaked through a comment plugin on the site. A university student in Canada lost their entire credit limit after making a purchase through a fake Apple Store; the site perfectly replicated the original Apple interface. In the UK, a user clicked on a “digital market” ad on social media and made a 30-pound purchase; two days later, 900 pounds worth of different purchases were detected in their account.
In a case in the US, scammers sent a user an SMS message stating, “Your payment was not approved; click the link and try again.” The link was fake, but the page closely resembled the user's bank. In cyberattacks carried out through fake Amazon-looking sites, not only payments but also users' home addresses, phone numbers, and device information are stolen.
Defenses and Vulnerabilities in Banking Systems
Some banks have begun testing “dynamic CVV” (constantly changing security code) systems to combat e-commerce fraud, but this system does not work on every website. Some credit card companies in the US automatically reject transactions when they detect behavior inconsistent with the customer's shopping habits, but this can sometimes block legitimate purchases. Some banks match the IP address of a purchase with the user's geographic location; transactions made from a different country may be deemed risky and blocked.
Some large financial institutions use AI-based “user behavior maps” to detect anomalies based on shopping patterns; for example, someone who spends $300 on a wine site at 3 a.m. is flagged by the system. Some bank apps flag card information copied outside the app with a “may have been compromised” warning, but most users do not take this warning seriously.
AI-Enabled Fraud Methods
AI-powered “deepfake calls” are now a new threat in banking; fraudsters use AI voices that sound like bankers to request card information from users. Some fraudsters create fake store profiles on social media and use AI-powered messages that function like chatbots to convince individuals to click on fake payment links.
AI systems can generate personalized fraud texts based on compromised email addresses; for example, by mimicking a brand the user has previously ordered from to build trust. A method that is becoming widespread in developed countries is “AI voice phishing”: calling the bank with a voice artificially generated from voice recordings found on the internet and requesting a password. Some AI fraud software can analyze user behavior on the payment page and even estimate the person's wallet limit and set a fraud limit accordingly.
Human Psychology and Security Vulnerabilities
In an experiment conducted in the UK, 68% of users clicked on a fake security logo and stated that they trusted the site; this logo had no real connection to the site. The rise of “buy now, pay later” systems has further distracted users' attention from credit card security; in these systems, fraud can take weeks to be detected. Some users prefer to keep their main cards registered for “convenience” rather than using single-use virtual cards; as a result, they become the first targets when system vulnerabilities arise.
The fact that the same people use the same card information on both desktop and mobile devices gives hackers an advantage in terms of data tracking in cross-device attacks. During discount periods, users are more likely to complete transactions without checking security indicators due to “fear of missing out.”
Real-Time Fraud Scenarios
In the US, a user received a fake “payment declined” notification and clicked on the link. Unwittingly, they canceled the actual payment transaction and approved the scammer's transaction instead. Some fake shopping sites appear to process the real order but only redirect users to a confirmation page, where the system processes the card but the product never arrives.
During online payments, the page appears to freeze for 1-2 seconds, during which time the data is sent to another server in the background. Some fraud networks display a fake “payment failed” screen and ask the user to make a second payment, resulting in the same user being charged twice. Sometimes, users who purchase a fake product write reviews as if it were a real product because the site sends them a small gift; this turns into a social engineering tactic to mask the fraud.