What is card testing and how is it detected?

[Mutt]

Card testing in the context of carding is a process where fraudsters (carders) check the validity of stolen or skimmed bank card data (card number, expiration date, CVV code and sometimes the owner's name) to determine whether it can be used for further fraudulent transactions, such as large purchases, cash withdrawals or selling the data on the black market. This process is an important part of carding, as stolen data does not always guarantee successful transactions due to blocking, restrictions or lack of funds on the card. For educational purposes, the mechanisms of card testing, carder methods, and approaches of anti-fraud systems (including Visa TC40 and MasterCard SAFE) to detect and prevent such attacks are described in detail below.

1. What is card testing and why is it important for carders?​

Card testing is a preliminary stage of fraud, the purpose of which is to confirm that the stolen card data is valid and can be used. Carders obtain card data through various methods:

• Skimming: Using devices to read data from a card's magnetic stripe or chip at ATMs, POS terminals, or other payment points.
• Phishing: Deceiving users through fake websites, emails or messages to obtain card details.
• Hacking databases: Stealing card data from online store databases, payment systems or other services.
• Black Market Purchase: Purchasing dumps (full card data) on darknet sites.

Since not all stolen data is valid (for example, the card may be blocked or have a zero balance), carders conduct testing to:

• Make sure the card is active.
• Check if it is being tracked by the bank or the owner.
• Determine card limits to plan for large transactions.

Without testing, carders risk using invalid data, which can lead to transaction rejections, increased attention from anti-fraud systems, and wasted time.

2. How do carders conduct card testing?​

Carders use thoughtful techniques to minimize the risk of detection and maximize the effectiveness of testing. Key approaches include:

2.1. Small transactions ($1–$5)​

• Carders make small payments to avoid attracting the attention of the cardholder or anti-fraud systems. Such amounts rarely raise suspicions, as they may look like regular micropayments (for example, for subscriptions or trial purchases).
• Examples of testing platforms:
  • Online shopping: Buy cheap digital goods (such as music, apps, or gift cards).
  • Subscription platforms: Attempts to sign up for trial subscriptions (Netflix, Spotify) with automatic deduction of a small amount.
  • Charity sites: Donations for small amounts, as such transactions often have minimal verification.
  • Payment Gateways: Using services like PayPal or Stripe to test through test payments.

2.2. Automation with Bots​

• Carders use scripts or specialized programs (bots) that automatically send requests to conduct transactions with hundreds or thousands of cards. Such programs can:
  • Enter card details on websites.
  • Check transaction status (successful/rejected).
  • Save results for future use.
• Automation allows you to test maps in large volumes in a short time, which is especially important when working with large data dumps.

2.3. Masking activity​

• Proxies and VPNs: Carders use proxy servers, VPNs, or Tor networks to hide their IP address and geographic location. This helps them simulate transactions from the region that matches the card owner.
• Device emulation: Tools are used to replace device data (browser, operating system) so that transactions appear to be normal.
• Switching merchants: Testing is done across different platforms to avoid detection of duplicate transactions with the same merchant.

2.4. Using "mules"​

• Carders may use "money mules" - people who provide their bank accounts or cards to conduct test transactions. This reduces the risk of the carder being directly detected.

2.5. Failure Testing​

• Carders may intentionally enter incorrect data (for example, incorrect CVV or expiration date) to test the system's response. If the transaction is rejected for "insufficient funds" or "successful authorization", this indicates that the card is active.

2.6. Selecting Weakly Protected Platforms​

• Carders prefer sites with a low level of protection, where there is no 3D-Secure (additional authentication) or other anti-fraud measures. These can be small online stores, donation platforms or services with minimal verification.

3. How do antifraud systems detect card testing?​

Anti-fraud systems such as Visa TC40 (Transaction Control) and MasterCard SAFE (System to Avoid Fraud Effectively), as well as other platforms (FICO Falcon, Kount, Riskified), use a combination of rules, analytics and machine learning to detect and prevent card testing. The main approaches are:

3.1. Analysis of transaction patterns​

Anti-fraud systems monitor the following signs indicating that cards are being tested:

• High transaction frequency: Multiple attempts to process transactions from the same card in a short period of time (e.g. 10 transactions in 5 minutes).
• Small Amounts: Recurring transactions of fixed amounts ($1, $2, $5) that do not fit the cardholder's normal purchasing profile.
• Multiple failures: Attempts to process transactions with invalid data (CVV, expiration date) often signal testing.
• Geographic anomalies: Transactions from regions not associated with the cardholder's usual location (e.g. a purchase in the US when the card is usually used in Russia).
• Single-Type Merchants: Multiple transactions at a single merchant or in a single category (e.g. donations only or digital goods only).

3.2. Behavioural analysis​

• Anti-fraud systems create a cardholder profilebased on transaction history:
  • Typical shopping categories (eg groceries, clothing).
  • Average transaction amount.
  • Geography of map use.
  • Transaction frequency and time.
• If new transactions deviate sharply from this profile (for example, small transactions at 2:00 am in another country), the system marks them as suspicious.

3.3. Real-time monitoring​

• Visa TC40: Collects transaction data including authorization codes, reasons for refusal, and types of transactions. For example, a refusal code of "05" (general refusal) or "51" (insufficient funds) may indicate testing if there are many such attempts.
• MasterCard SAFE: Analyzes global fraud data by comparing current transactions to known patterns. If a card is used in a suspicious environment (such as a site linked to previous attacks), it receives a high risk rating.
• The systems use risk scoring, assigning each transaction a score based on the likelihood of fraud. If the score exceeds a threshold, the transaction is rejected or sent for additional verification.

3.4. Device and IP Analysis​

• Anti-fraud systems check:
  • IP Address: Multiple transactions from the same IP but with different cards indicate testing.
  • Device fingerprint: Unique characteristics of the device (browser, screen resolution, operating system). If one device uses different cards, it is suspicious.
  • Proxy/VPN: Using known proxy servers or mismatch between IP and map geolocation.

3.5 Using 3D-Secure​

• Protocols such as Verified by Visa and MasterCard SecureCode require additional authentication (such as entering a one-time password sent to the owner's phone). This makes testing more difficult, as carders rarely have access to the owner's phone.
• If 3D-Secure is not used, anti-fraud systems may increase the risk rating of the transaction.

3.6 Machine learning and artificial intelligence​

• Machine learning algorithms analyze large amounts of data to identify complex patterns that are not covered by static rules. For example:
  • Detect clusters of transactions associated with a single device or IP.
  • Identifying new testing methods based on historical fraud data.
• Machine learning models are trained on data from confirmed fraud cases, allowing them to adapt to new carder tactics.

3.7. Cooperation with merchants​

• Merchants provide transaction data to anti-fraud systems. For example:
  • If a store records 50 transactions for $1 from different cards, but from one IP, this signals testing.
  • Merchants can implement CAPTCHAs or limit the number of attempts to enter card details to prevent automated attacks.
• Payment gateways (Stripe, PayPal) also use their own anti-fraud algorithms that integrate with Visa and MasterCard systems.

3.8. Reaction to suspicious transactions​

• Temporary Card Blocking: If the system detects a testing pattern, the card may be blocked until confirmed by the owner.
• Cardholder notifications: The bank sends SMS, email or push notification asking to confirm transactions.
• Transaction Rejection: Suspicious transactions are rejected automatically, forcing carders to look for other cards.

4. The Role of Visa TC40 and MasterCard SAFE​

Visa TC40 (Transaction Control)​

• This is Visa's system for monitoring and analyzing transactions. It collects data from issuing banks, acquirers, and merchants, including:
  • Authorization code (success/failure).
  • Reason for refusal (e.g. incorrect CVV, insufficient funds).
  • Geography and transaction category.
• TC40 helps detect anomalies such as:
  • Multiple attempts with incorrect data.
  • High frequency of small transactions.
  • Using the card in suspicious merchants.
• If testing is detected, TC40 may recommend that the card issuer temporarily block the card or request additional authentication.

MasterCard SAFE (System to Avoid Fraud Effectively)​

• SAFE collects transaction data from around the world and uses it to build a global picture of fraud. The system analyzes:
  • Card transaction history.
  • The merchant's behavior (for example, whether he has previously participated in fraudulent schemes).
  • Linking current transactions to known fraud patterns.
• SAFE assigns a risk rating to transactions and communicates recommendations to banks and merchants. For example, if a card is used for small transactions on a carding site, SAFE may signal for testing.

Similarities and differences​

• Both systems use real transaction data and work closely with banks and merchants.
• Visa TC40 focuses more on the analysis of reasons for refusals and authorizations, while MasterCard SAFE focuses on the global fraud database.
• Both systems integrate with 3D-Secure and support EMV standards for enhanced security.

5. How to minimize card testing risks?​

For banks and payment systems:​

• Strengthening Authentication: Mandatory use of 3D-Secure for all online transactions.
• Updating anti-fraud algorithms: Regularly training machine learning models on new fraud data.
• Real-time monitoring: Invest in systems such as FICO Falcon or SAS Fraud Management to analyze transactions instantly.
• Attempt Limit: Block cards after several unsuccessful attempts to enter data.

For merchants:​

• Implementing CAPTCHA: To prevent automated bot attacks.
• Transaction Limit: Set limits on the number of transactions from one IP or device.
• Data Checking: Using services like Kount or Riskified to analyze transactions for fraud.
• Filtering merchants: Avoiding cooperation with platforms that do not comply with security standards.

For users:​

• Transaction Monitoring: Regularly check your card statements and enable transaction notifications.
• Use of virtual cards: For online purchases to restrict access to the main account.
• Timely response: Immediate notification to the bank upon detection of suspicious transactions.

6. Consequences of card testing and how to combat it​

Consequences for victims:

• Financial losses if small transactions go undetected.
• The card is blocked by the bank, which may cause inconvenience.
• Potential data leakage for other fraudulent activities.

Consequences for carders:

• Detecting and blocking cards reduces their value on the black market.
• Strengthening anti-fraud measures forces carders to develop new, more complex testing methods.

Evolution of Wrestling:

• As technology advances, carders are moving to more sophisticated methods, such as using artificial intelligence to fake behavior patterns or bypass 3D-Secure.
• Anti-fraud systems, in turn, are being improved by introducing biometric authentication (fingerprints, facial recognition) and more complex machine learning algorithms.

Summary​

Card testing is a key stage in carding, allowing fraudsters to check the validity of stolen cards through small transactions ($1–$5), often using automation and disguise. Anti-fraud systems such as Visa TC40 and MasterCard SAFE effectively combat this by analyzing transaction patterns, user behavior, geography, and device data. The use of machine learning, 3D-Secure, and cooperation with merchants allows for the rapid identification and blocking of suspicious cards, minimizing damage. For educational purposes, it is important to understand that the fight against card testing is a constant race between fraudsters and security systems, where technology and the vigilance of all participants in the payment process play a key role.