Introduction to carding and the role of anomaly detection systems

[Student]

Carding is a form of financial fraud in which criminals use stolen credit or debit card information (card number, CVV, expiration date, and sometimes PIN) to conduct unauthorized transactions. This can include online purchases, fund transfers, or even testing cards with small amounts to ensure they work before committing to larger purchases. Research estimates that global losses from credit fraud, including carding, will exceed $43 billion by 2026, growing by 7-10% annually due to the increase in online transactions. Early detection is critical, as it allows banks to block a card or transaction before damage occurs, minimizing financial losses and maintaining customer trust.

Anomaly Detection Systems (ADS) are artificial intelligence (AI) and machine learning (ML)-powered tools that analyze transaction data in real time, identifying deviations from "normal" behavior. Unlike rule-based systems, which rely on fixed rules (e.g., "block transactions over $1,000"), ADS are adaptive and can detect new, unknown threats. They are particularly effective against carding, where fraudsters attempt to mimic legitimate activity but leave subtle anomalies, such as unusual transaction speeds or geolocation inconsistencies. For educational purposes, we'll cover this topic step by step: from basic concepts to advanced algorithms, with examples and practical considerations.

Basic principles of anomaly detection​

Anomaly detection is a subset of ML that focuses on identifying rare events or patterns that deviate from the expected norm. In the banking context, the "norm" is based on historical customer data: typical amounts, frequency, locations, time of day, and even device type. Anomalies can be:

• Point anomalies: A single transaction that deviates from the norm (e.g., a $500 purchase in another country when the customer typically spends $50 locally).
• Contextual anomalies: A normal transaction in the wrong context (e.g., a purchase at 3am when the customer is active during the day).
• Collective anomalies: A series of transactions that together look suspicious (for example, 10 small purchases in 5 minutes is a typical "test" for carders).

ADS are classified by training type:

1. Unsupervised: No labeled fraud data. Algorithms build a norm model based on the majority of the data and flag outliers. Ideal for new threats, such as evolving carding.
2. Supervised: Uses labeled datasets (fraud/non-fraud). More accurate, but requires historical fraud data.
3. Semi-supervised: Combines learning from normal data and identifying outliers.

Banks integrate ADS with systems like FICO Falcon or Feedzai, processing millions of transactions per second.

Key ML Algorithms for Carding Detection​

ADSs use a variety of algorithms. Here's a comparison of popular ones, with examples of their application to carding:

Algorithm	Type	Description	Application to carding	Advantages	Flaws
Isolation Forest	Unsupervised	Isolates anomalies by randomly splitting data into trees. Anomalies are isolated faster.	Identifying series of small test transactions (velocity anomalies).	Fast, scalable, does not require normal data distribution.	May generate false positives in noisy data.
Autoencoders (neural networks)	Unsupervised	Compress data into latent space and reconstruct; high reconstruction error = anomaly.	Detecting unusual patterns, such as purchases from suspicious merchants on the darknet.	Handles multidimensional data well (geo, device, sum).	Requires a lot of data for training.
Random Forest / XGBoost	Supervised	Ensembles of decision trees classifying transactions as fraudulent/not fraudulent.	Flagging geolocation mismatches (map from Russia used in the USA).	High accuracy (up to 99%), interpretability.	Depends on the quality of the labeled data.
Support Vector Machines (SVM)	Supervised	Constructs a hyperplane to separate classes; one-class SVM for unsupervised.	Identification of high-value transactions that deviate from the profile.	Effective for imbalanced datasets (fraud is rare).	Slow at high volumes.
Graph Neural Networks (GNN)	Semi-supervised	Analyze graphs of relationships between transactions/accounts.	Detection of fraud rings – networks of carders using multiple cards.	Catches collective anomalies in networks.	Difficult to implement.
Logistic Regression	Supervised	A simple model for binary classification based on probabilities.	Basic risk scoring for simple patterns, such as unusual times.	Easy interpretation, fast.	Less effective for complex nonlinear patterns.

These algorithms are trained on datasets like Kaggle's Credit Card Fraud (which contains 284,000 transactions, with a fraud rate of 0.17%). In practice, banks combine them into hybrid systems to reduce false positives that annoy customers (for example, blocking a legitimate purchase while on vacation).

How ADS Detects Carding Early: Steps and Signs​

ADS workflow in a bank:

1. Data collection: Transactions are enriched with metadata - IP address, device fingerprint (browser, OS), geolocation, time, amount, merchant.
2. Profile building: The ML model creates a baseline of customer behavior (e.g. average amount $100, 80% of transactions in the home region).
3. Real-time analysis: Each transaction is assessed using a risk score (0-1, where >0.8 is a flag). If an anomaly occurs, the system generates an alert.
4. Intervention: Automatic blocking, 2FA (two-factor authentication) request, or customer notification.
5. Feedback and retraining: Confirmed fraud updates the model for future detections.

Specific signs of carding that ADS detects in the early stages:

• Velocity checks: High transaction frequency (e.g. 5+ per minute) – carders test cards with small amounts ($1-5) to avoid immediate blocking.
• Geolocation anomalies: Transaction from a distant country or with a VPN (IP and billing address mismatch).
• Behavioral deviations: Unusual merchants (high-risk sites), sudden high-value purchases, or changes in patterns (e.g., from clothing to electronics).
• Device mismatches: Using a new device or browser, often with signs of bots.
• Collective patterns: Connection with other suspicious accounts via shared IPs or merchants.

A practical example: In systems like Stripe or Mastercard, AI flags a transaction if it deviates from the norm by 3+ standard deviations (a statistical method). Research shows that ADSs reduce fraud losses by 20-50%, with an accuracy of up to 95%.

Benefits, Challenges, and Best Practices​

Advantages:

• Proactivity: Early intervention prevents damage; for example, blocking at the carder test stage.
• Scalability: Process petabytes of data without human intervention.
• Adaptability: Automatically learns from new threats, including AI-generated fraud.
• Integration: With 3D Secure, tokenization (replacing data with tokens) and biometrics (typing speed, gestures).

Challenges:

• Data imbalance: Fraud is rare (0.1-1% of transactions), which leads to overfitting.
• False positives: 5-10% of alerts are false and annoying to customers.
• Evolution of threats: Carders use proxies, bots and stolen identities.
• Privacy: GDPR/PCI DSS compliance in data processing.

Best practices for banks:

• Use hybrid models (ML + rules).
• Update datasets regularly (for example, with BIS frameworks).
• Monitor metrics: Precision, Recall, F1-score.
• Partner with networks like Visa/Mastercard to share fraud data.
• Train staff and customers (suspicious activity notifications).

Conclusion​

Anomaly detection systems are a key tool in banks' anti-carding arsenal, enabling them to move from reactive to predictive protection. They not only reduce financial risks but also improve operational efficiency. For in-depth analysis, I recommend experimenting with open datasets on Kaggle or reading reviews in journals like ScienceDirect. In the future, with the development of quantum ML and edge computing, ADS will become even more accurate, but always remember: technology is a tool, not a panacea; the human factor and ethics remain important.