How do banks use real-time systems to block carding transactions?

[Student]

For educational purposes, I will examine in detail how banks use real-time systems to prevent and block carding-related transactions, focusing on the technical, organizational, and analytical aspects. Carding is a type of fraud in which criminals use stolen bank card data for unauthorized transactions. Real-time systems play a key role in combating this type of fraud, ensuring the immediate detection and prevention of suspicious transactions. A detailed analysis is provided below.

1. General principles of operation of real-time systems​

Real-time fraud detection systems are hardware and software systems that analyze transactions as they occur to identify signs of fraud. These systems must:

• Operate with minimal latency (usually less than 100 milliseconds) to avoid disrupting the user experience.
• Process huge volumes of data, as large banks can process millions of transactions per day.
• Be adaptive to respond to new fraudulent schemes.

The main stages of the system's operation:

1. Data Collection: Obtaining transaction information (amount, time, location, device, merchant, etc.).
2. Data Analysis: Transaction evaluation based on rules, machine learning models and historical data.
3. Decision Making: Approve, reject, or submit the transaction for further review.
4. Action: Block the transaction, request confirmation from the client, or notify the security service.

2. Key components of the systems​

2.1. Transaction Monitoring​

Transaction monitoring systems collect real-time data from various sources:

• Transaction details: Amount, currency, time, location (physical or online), merchant category (MCC code).
• Customer data: Transaction history, geographic preferences, typical purchase amounts.
• Device context: IP address, device type, browser, operating system, device geolocation.
• Network data: Information from payment systems (Visa, Mastercard) and other banks.

This data is collected through API integration with payment gateways, ATMs, POS terminals, and online platforms.

2.2. Rules and Filters​

Banks establish predefined rules (rule-based systems) to automatically identify suspicious transactions. Examples of rules:

• Transactions are blocked if the card is used in several countries within a short period of time (for example, a purchase in Russia and the USA within an hour).
• Reject high-risk transactions if they originate from IP addresses associated with known fraudulent networks.
• Flagging transactions from a card that was previously marked as compromised (e.g. as a result of a data breach).

Rules can be static (programmed) or dynamic (updated based on new data).

2.3. Machine Learning and Artificial Intelligence​

Machine learning (ML) and artificial intelligence (AI) are the foundation of modern anti-carding systems. They enable:

• Detect anomalies: ML algorithms compare the current transaction with the customer's historical profile. For example, if a customer typically spends $500–1,000 at local stores but suddenly makes a $10,000 purchase at an online casino, this raises suspicion.
• Risk scoring: Each transaction is assigned a risk score based on multiple factors. If the score exceeds a certain threshold, the transaction is blocked or submitted for review.
• Clustering and classification: Algorithms group transactions by similar characteristics and classify them as legitimate or suspicious. For example, transactions using stolen cards often have similar patterns (multiple attempts at small purchases to verify the card).

Examples of algorithms:

• Decision Trees and Random Forests: For transaction classification.
• Neural networks: For processing complex patterns and large amounts of data.
• Clustering algorithms (k-means): To identify groups of fraudulent transactions.

2.4 3D-Secure and two-factor authentication​

For online transactions, banks use 3D-Secure protocols (e.g., Verified by Visa, Mastercard SecureCode, Mir Accept). This is an additional level of security that requires:

• Entering a one-time password (OTP) sent via SMS, push notification or mobile app.
• Biometric authentication (fingerprint, facial recognition).
• Confirmations via tokens or codes from applications.

3D-Secure reduces the risk of carding, since even with the card details (number, CVV), the fraudster will not be able to complete the transaction without additional confirmation.

2.5. Integration with external systems​

Banks collaborate with international and local organizations to share fraud data:

• Payment systems: Visa and Mastercard provide access to databases of compromised cards and suspicious merchants.
• IP Blacklists: Integration with databases containing IP addresses associated with fraudulent activity.
• Local databases: For example, in Russia, banks can exchange information through the FinTech Association or the Bank of Russia.

3. Real-time transaction processing​

1. Receiving a transaction: When a customer makes a purchase (in-store, online, or at an ATM), the data is sent through a payment gateway to the bank.
2. Initial check: The system checks basic parameters (e.g. are there sufficient funds, is the card valid).
3. Risk analysis:
   • The system applies rules and ML models to evaluate the transaction.
   • Geolocation, device, client history and other parameters are checked.
   • If a transaction is associated with a blacklisted merchant or an IP address marked as suspicious, it is flagged.
4. Decision making:
   • Approval: If the risk is low, the transaction goes through.
   • Blocking: If the risk is high, the transaction is rejected.
   • Additional verification: If the risk is moderate, the client is sent a confirmation request (e.g. via 3D-Secure or a call from the bank).
5. Notification: The client receives a notification about the transaction status (SMS, push or email).
6. Feedback: If a transaction was incorrectly blocked, the customer can contact the bank, and the system uses this information to improve the models.

4. Technological infrastructure​

To work in real time, banks use:

• High-performance databases: For example, NoSQL databases (MongoDB, Cassandra) or in-memory databases (Redis) for fast data access.
• Cloud technologies: AWS, Google Cloud or Azure for scalability and processing large volumes of transactions.
• Microservice architecture: Dividing the system into modules (e.g. risk analysis module, authentication module) to improve reliability and speed.
• API integration: Quickly exchange data with payment systems, merchants, and external databases.
• Big Data and Stream Processing: Tools like Apache Kafka or Spark Streaming for real-time transaction processing.

5. Example of a carding scenario and system response​

Situation: A fraudster uses stolen card details to purchase electronics from an online store abroad.

1. Anomaly detection:
   • The system notices that the transaction is taking place from a new device (unknown IP, different browser).
   • The geolocation of the transaction (e.g. USA) does not match the location of the client's last activity (Russia).
   • The purchase amount ($1,000) significantly exceeds the average customer bill.
2. Risk assessment:
   • The ML model assigns a high risk score to the transaction (e.g. 95/100).
   • The store is checked to see if it falls into a high-fraud category (electronics).
3. Action:
   • The system requires 3D-Secure authentication. Fraudsters cannot enter the code because they don't have access to the customer's phone.
   • The transaction is declined and the card is temporarily blocked.
   • The client receives a notification about suspicious activity.
4. Consequences:
   • The bank contacts the client to confirm legitimacy.
   • Information about fraud attempts is transferred to a database to prevent future attacks.

6. Problems and Challenges​

1. False Positives:
   • Legitimate transactions can be mistakenly blocked, causing inconvenience to customers. For example, a purchase made while on vacation abroad might be flagged as suspicious.
   • Solution: Banks use adaptive ML models and allow customers to pre-book trips through the app.
2. Balance between security and convenience:
   • Overly strict checks can slow down the process or turn off customers.
   • Solution: Optimize algorithms to minimize checks for low-risk transactions.
3. The evolution of fraud:
   • Fraudsters use sophisticated techniques such as IP spoofing via VPN, device emulation, or social engineering.
   • Solution: Continuously updating ML models and integrating with new data sources.
4. Processing delays:
   • Even minimal delay in real time can impact user experience.
   • Solution: Use high-speed technologies and optimize infrastructure.

7. Examples of technologies and suppliers​

Banks often use ready-made solutions from specialized companies:

• FICO Falcon Fraud Manager: ML-based system for transaction risk assessment.
• SAS Fraud Management: Real-time tool with big data analytics.
• ACI Worldwide Proactive Risk Manager: Risk management and fraud prevention solution.
• Visa Advanced Authorization: A service from Visa for real-time transaction analysis.

Some large banks (for example, Sberbank, JPMorgan) are developing their own systems adapted to their needs.

8. The Future of Real-Time Systems​

• Artificial Intelligence and Deep Learning: More complex neural networks will be better able to predict new fraudulent schemes.
• Biometrics: Increased use of facial recognition, voice recognition, and behavioral biometrics (e.g., analyzing mouse movements or typing style).
• Blockchain: Potential use for secure data exchange between banks and payment systems.
• Quantum computing: It has the potential to speed up data analysis and improve the accuracy of systems.

Conclusion​

Real-time carding prevention systems are a complex set of technologies that incorporate monitoring, rules, machine learning, and integration with external data. They balance security and convenience, minimizing the risk of fraud. However, the constant evolution of carding methods requires banks to continually improve their technologies and adapt to new threats. For educational purposes, it's important to understand that the success of these systems depends on a combination of technical infrastructure, data analytics, and customer engagement.