How banks detect fraudulent transactions (carding)

[chushpan]

Banks use complex methods and technologies to detect fraudulent transactions. These methods are based on data analysis, transaction monitoring, and the use of modern technologies such as artificial intelligence (AI) and machine learning (ML). Let's look at the main methods that banks use to detect suspicious activity:

1. Analysis of customer behavior​

Banks track typical patterns of behavior of each client to identify deviations.

• Examples of analysis:
  • Typical transaction amounts.
  • Frequency of operations.
  • The geographic location of the client (e.g. the region where the map is used).
  • The time of day when the customer usually makes transactions.
• How it works: If a customer who typically makes $50-$100 purchases in their home city suddenly makes a large $5,000 transaction from another country, the system can flag it as suspicious.

2. Fraud Detection Systems​

These systems use algorithms and rules to automatically identify suspicious transactions.

• Main functions:
  • Check for matches against known fraudulent patterns.
  • Analysis of transaction execution speed (e.g. multiple transactions in a short period of time).
  • Detect anomalies in card data (e.g. Billing Address mismatch).
• Examples of systems:
  • FICO Falcon Fraud Manager: A widely used system for transaction analysis.
  • Feedzai, SAS Fraud Framework: Machine learning based tools.

3. Real-time monitoring​

Modern banks analyze transactions in real time to quickly respond to suspicious activity.

• How it works:
  • The transaction is verified immediately after initiation.
  • If the system detects an anomaly, it can:
    • Block the operation.
    • Send notification to the client.
    • Request additional confirmation (e.g. via SMS or push notification).

4. Use of Artificial Intelligence (AI) and Machine Learning (ML)​

AI and ML enable banks to identify complex fraudulent schemes that are difficult to detect using traditional methods.

• Application examples:
  • Data Clustering: Grouping similar transactions to identify suspicious patterns.
  • Link Analysis: Identify links between suspicious accounts or IP addresses.
  • Predictive Analysis: Predicting the likelihood of fraud based on historical data.
• Advantages:
  • AI can adapt to new fraud schemes.
  • Automated analysis of large volumes of data.

5. Checking card details​

Banks use several methods to verify the authenticity of transactions.

• AVS (Address Verification System):
  • Compare Billing Address with the address registered for the card.
• CVV/CVC verification:
  • Make sure you have entered the three-digit code on the back of your card correctly.
• 3D Secure:
  • An additional level of security that requires transaction confirmation via SMS or app.

6. Monitoring IP addresses and devices​

Banks track the devices and IP addresses from which transactions are made.

• Examples of checks:
  • New IP address or country from which there have been no previous transactions.
  • Suspicious devices (eg using TOR or VPN).
  • Simultaneous login from different devices.
• How it helps: If the card is used from a new device or from another country, the system may request additional verification.

7. Big Data Analysis​

Banks collect and analyze huge amounts of data to identify fraudulent patterns.

• Examples of data:
  • Customer transaction history.
  • Data about other clients (e.g. group attacks).
  • Information about suspicious IP addresses and devices.
• How it works: Big Data algorithms reveal hidden connections between fraudulent transactions that are impossible to detect manually.

8. Collaboration with other banks and organizations​

Banks work with each other and with organizations such as Visa, Mastercard, and the PCI Security Standards Council to share information about fraudulent transactions.

• Examples of cooperation:
  • Exchange of data on compromised cards.
  • Joint development of safety standards.

9. Reaction to suspicious transactions​

If the system detects a suspicious transaction, the bank may take the following steps:

1. Transaction blocking:
   • The operation is temporarily suspended for verification.
2. Client Notice:
   • The client receives an SMS, email or push notification with a confirmation request.
3. Suspending a card:
   • If there are many suspicions, the card may be temporarily blocked.
4. Investigation:
   • The bank is conducting an internal investigation to determine whether the transaction was legitimate.

10. Client training​

Banks are also educating customers on how to recognize fraud and take steps to protect their data.

• Examples of recommendations:
  • Do not share card details with third parties.
  • Use two-factor authentication.
  • Report suspicious transactions.

Conclusion​

Banks use a combination of technology, analytics, and human intervention to detect fraudulent transactions. Modern methods such as AI, machine learning, and Big Data significantly improve the efficiency of these systems. However, customer collaboration plays a key role: promptly reporting suspicious transactions helps prevent fraud.

If you have additional questions about fraud detection methods, ask them!

 

[Man]

Banks use a multi-layered security system to detect fraudulent transactions in real time. Here are the key methods:

1. Behavioural analysis (Anti-Fraud systems)​

• Transaction anomalies:
  • Unusual amounts (for example, sudden transfer of all funds).
  • Suspicious locations of operations (yesterday - New York, today - Nigeria).
  • Unusual times (payments at night if the client is usually active during the day).
• Machine learning: Systems analyze 100+ parameters, including CVV typing speed, IP address, and even the angle of the phone when paying.

2. Risk monitoring rules​

Examples of triggers:

• A series of small payments (checking the card before a large transfer).
• Repeated transactions with the same merchant (for example, donations).
• Using VPN/Tor when accessing online banking.

3. Verification technologies​

• 3D Secure 2.0:
  • Device verification (IMEI, geolocation).
  • Biometrics (Face ID, fingerprint) instead of SMS codes.
• Card tokenization: Substitution of real details for one-time tokens in online payments.

4. Joint databases​

• Signal exchange between banks: If a card is detected in fraud in one bank, others block it instantly.
• Lists of compromised merchants (for example, sites where data has been leaked en masse).

5. Investigation of operations​

• Analysis of transfer chains: Identification of "mules" - accounts for cashing out.
• Contacting law enforcement: Banks transfer data to the cyber police in case of suspicious schemes.

What does this look like for the client?​

1. SMS/push notification: "Confirm the transfer of $1,000 to the Caymans?"
2. Temporary block: If a transaction is suspicious, the bank may freeze the payment for verification.
3. Call from security: An employee clarifies the details of the operation.

Important: False positives happen! If your payment is blocked, simply confirm its legitimacy.

For protection:

• Enable all types of 2FA (TOTP is better than SMS).
• Use virtual cards for online purchases.
• Check the list of connected devices in your banking app regularly.

Banks spend billions to combat carders, but customers should also be careful.