What is a Carding Attack: An Educational Introduction

[Student]

A carding attack is a type of payment system fraud in which criminals use stolen credit or debit card information to test their validity. These aren't just random attempts: the attacks are often automated, using bots that conduct thousands of small transactions (for example, $1) on various websites or in stores. The goal is to identify which cards are "live" (active) so they can then be used for larger purchases or black market sales.

Why is this a problem for banks and merchants?

• Financial losses: Every authorization attempt costs money (processor fees), even if the transaction is rejected. Successful transactions result in chargebacks, penalties, and reputational damage.
• Operational costs: Fraud teams spend time investigating, and customers may face card freezes.
• Scale: Attacks can be massive, affecting thousands of cards from one bank (by BIN - Bank Identification Number, the first 6 digits of the card identifying the issuer).

For educational purposes, it's important to understand that carding isn't a random occurrence, but an organized threat. Criminals often purchase "dumps" (lists of stolen cards) on the darknet and use proxies, VPNs, or bots to disguise themselves. Banks in the US (and around the world) combat this through proactive monitoring, using AI and machine learning (ML) to identify anomalies before significant damage occurs. Below, I'll discuss a real-world approach using the example of the Pagos Alerts system, which partners with US financial institutions and payment processors.

How banks detect carding attacks by analyzing transaction patterns​

Banks and payment processors, like those integrated with Pagos, use real-time transaction data to detect patterns. This isn't manual analysis, but automated AI-powered systems that are trained on historical data (both legitimate and fraudulent transactions). The basic idea is that normal transactions are predictable (for example, seasonal peaks during holidays), while fraudulent ones are anomalous.

Key patterns that are analyzed​

Systems like Pagos Alerts scan millions of transactions and focus on anomalies. Here are the main indicators of a carding attack:

1. BIN Spike:
   • The BIN is the issuing bank's identifier. In carding attacks, attackers often test cards from a single source (for example, stolen from a single database), which leads to a sharp increase in authorization attempts from a single BIN.
   • Example: Normal: 100 transactions per day from BIN 123456. Anomaly: a sudden spike to 10,000 per hour. The AI takes into account context, such as seasonal variations (e.g., more transactions on Black Friday), to avoid false positives.
   • Why does this work? Bots generate "unnatural" traffic: quick, repeated attempts with similar parameters (IP, device).
2. Increase in Decline Codes:
   • When a transaction is declined, the issuer (bank) returns a reason code. In carding attacks, the codes are grouped into:
     • Early stages: "Refer to Issuer" - the bank is checking; "CVV Failure" - incorrect security code; "Account Closed" - the card is already blocked.
     • Late stages: "Fraud Lost Card", "Pick Up Card", "Suspected Fraud" - when the bank reacts.
   • Pattern: A sharp increase in such codes (for example, +500% in a day) signals an attack. The system tracks not only the quantity but also the shifts (first CVV errors, then fraud codes).
3. Approval Rate Drops:
   • The approval rate is the percentage of successful transactions. During an attack, it drops because "bad traffic" (bots) overloads the system, and issuers begin to reject more transactions.
   • Example: Normally, 95% of merchant ID (MID) approvals are achieved. An attack reduces this to 70%, especially for specific segments (market, card type).
4. Other behavioral patterns:
   • Device Fingerprinting: Device fingerprints (browser, OS, screen) - bots often use identical or suspicious ones.
   • IP Analysis: Many transactions from proxies or unexpected regions.
   • Temporal patterns: Attacks often occur at night or during off-peak hours.

The AI in Pagos Alerts uses machine learning for dynamic thresholds: the model learns from customer data, adapting to their business (e.g., e-commerce vs. subscription).

Steps to Detect and Stop an Attack: A Step-by-Step Process​

Here's how it works in practice, based on the Pagos Alerts approach. It's a proactive approach—not waiting for damage to occur, but acting on early warning signs.

1. Continuous data monitoring:
   • The system collects transaction data in real time (volume, codes, BIN, MID, etc.). AI analyzes it for anomalies, comparing it to historical benchmarks (daily, weekly, seasonal).
2. Anomaly detection:
   • AI flags deviations: For example, for BIN 12345678, there was a volume spike on June 21 (example from Pagos). The model uses statistics (such as standard deviation) and ML to predict "normal" levels.
3. Alert generation:
   • Automatic notifications: In the Pagos dashboard, via email, or Slack. Alerts include details such as the BIN, spike time, processor, and visualizations (graphs).
4. Deep analysis:
   • The team switches to dashboards (Declines, Approvals, Risks). Segment the data by market, issuer, and card type. Uses BIN data to distinguish fraud from legitimacy (geography, card brand).
5. Stop attack (Response):
   • Blocking: Automatic rejection for suspicious BINs, IP addresses, and devices. Integration with fraud tools: 3D Secure (additional verification), strict CVV rules.
   • A/B testing: Test new rules on a subset of traffic to avoid blocking good customers.
   • Device Fingerprinting and Proxy Piercing: Detect and block bots.
6. Long-term monitoring:
   • They monitor the effectiveness of their rules. If an attack is stopped, the approval rate returns to normal. They reduce fraud by 50-70% overall.

Results and benefits for banks​

• Financial: Reduced fees for declined transactions (each costs ~0.20-0.50 USD). Chargeback prevention (penalties up to 100 USD+).
• Operational: Less time spent on triage (investigation). Early detection provides a "headstart" — hours or days before escalation.
• Reputational: Protecting customers from card blocking, maintaining trust.
• Metrics: Pagos examples include identifying spikes before chargebacks, focusing on single-BIN to minimize noise.

In the US, banks (for example, through partnerships with Pagos) are integrating this with regulations like PCI DSS for security. For education: Study ML models (for example, anomaly detection with Isolation Forest or Autoencoders) – they are key to such systems. If you want to simulate simple analysis in code, provide the data, and I'll show you an example in Python.