Introduction to the context of carding and the role of fraud detection systems

[Student]

Carding is a type of financial fraud in which criminals use stolen credit card information to test their validity (card testing) and make unauthorized purchases, often from online merchants. This involves automated attacks using bots to validate large numbers of cards, resulting in losses for banks, merchants, and consumers. For educational purposes, it's important to understand how modern AI and machine learning (ML)-based systems prevent such schemes by analyzing thousands of signals, such as IP addresses, device fingerprints, behavioral patterns, and transaction data. These systems don't provide step-by-step instructions for committing fraud, but rather focus on identifying anomalies to protect legitimate users. Below, I'll examine the mechanics of each system in more detail, emphasizing their application against carding, drawing on ML principles such as pattern analysis, consortium data, and real-time processing.

How FICO Falcon Works in the Context of Carding Detection​

FICO Falcon is a leading payment fraud detection platform that uses a combination of AI, ML, and a global data network (Falcon Intelligence Network) to monitor transactions in real time. The system processes billions of transactions annually, covering more than two-thirds of all card transactions globally, and integrates data from over 10,000 financial institutions. In the context of carding education, Falcon functions as a multi-layered defense: it builds user behavior profiles based on historical data, identifying anomalies such as sudden purchases from new devices or IP addresses, which are typical of carding.

Key mechanisms:

• Consortium ML models: Falcon uses shared data from multiple banks to train models that recognize global fraud patterns. For example, if a card is used in a series of small test transactions (card testing), the system compares this to normal patterns and assigns a fraud score. This improves card-not-present (CNP) fraud detection by 30%, where carding often occurs without a physical card.
• Signal analysis (over 1,000): The platform collects data on IP addresses (geolocation, proxy servers), device fingerprints (device type, browser, OS), behavior (input speed, sequence of actions), and transactions (amounts, frequency). In carding, this helps identify bot attacks: if multiple transactions come from the same IP address but different cards, the model flags an anomaly. Falcon also uses self-learning (self-calibrating) algorithms that adapt to new patterns, reducing false positives.
• Real-time and multi-layered analytics: Transactions are evaluated in milliseconds using neural networks (introduced in 1992 but have evolved). For carding, this means blocking suspicious transactions before they are completed, with the integration of phone data to prevent related scams.
• Educational aspect: Falcon's machine learning illustrates how algorithms like neural networks learn from big data: they construct "latent variables" (hidden factors) to distinguish genuine behavior from fraudulent activity. This highlights the importance of data in preventing losses estimated at billions of dollars annually from carding.

Sift's Mechanisms for Carding Detection​

Sift is a cloud-based digital fraud prevention platform focused on e-commerce and online services. It uses machine learning to analyze data from the global network (over 6,000 websites and apps), updating models in real time. In the context of card fraud, Sift focuses on identifying card testing and hopping, where fraudsters test stolen data through small purchases or automated scripts.

Key mechanisms:

• Deep learning and network effects: Sift uses deep neural networks to recognize patterns of abuse, such as payment fraud. Models are trained on historical data to predict risk based on "threat clusters"—groups of related threats. For carding, this means detecting series of failed transactions from similar devices, indicating bot attacks.
• Signal analysis (over 16,000): The system collects data on IP addresses (connections, locations, VPNs), device/browser fingerprints (unique identifiers, like canvas fingerprints), behavior (browsing patterns, unusual logins, click rate), and interactions. In carding, Sift distinguishes legitimate purchases from suspicious ones, reducing false positives and blocking automated card testing at scale.
• Automated workflows and backtesting: The platform allows you to configure rules without coding, testing them on historical data for optimization. This helps adapt to evolving carding schemes, such as the use of proxies for IP masking.
• Educational aspect: Sift demonstrates the advantages of machine learning over rules: algorithms automatically learn from new data, minimizing manual effort. This highlights how machine learning reduces the human factor in fraud detection, making systems more scalable to combat global threats like carding.

Riskified's Mechanisms for Carding Detection​

Riskified is an e-commerce risk management platform that uses AI to identify users across online interactions. It analyzes billions of transactions from the Merchant Network, providing approval guarantees and focusing on a frictionless experience. In the context of carding, Riskified is effective against gift card fraud and identity theft, where fraudsters use stolen cards to purchase digital goods that are easily converted into cash.

Key mechanisms:

• Unsupervised machine learning and scoring models: The system builds fraud scores based on historical trends and attributes, identifying suspicious patterns such as rapid, sequential purchases. For carding, this includes recognizing fraud rings (groups of linked accounts) and session anomalies.
• Signal analysis (hundreds of attributes): Evaluates device fingerprinting, session tracking, browsing patterns, IP geodata, and user interactions. In carding, this helps block attacks where cards are tested through bots, with an emphasis on chargeback prevention (chargebacks from carding come faster than from other fraudulent activities).
• Automated engine and 24/7 monitoring: Explainable AI provides context for decisions. The platform becomes more accurate with each order, integrating products from login to post-purchase.
• Educational aspect: Riskified illustrates how machine learning analyzes big data for proactive protection: algorithms identify patterns, such as increased risk in gift card segments (with up to 99.9% accuracy). This teaches why e-commerce needs dynamic models to combat evolving carding.

Hawk:AI's Mechanisms for Carding Detection​

Hawk:AI is a financial crime (AML, fraud) detection platform that uses explainable AI to reduce false positives (by 70%) and improve detection rates (by 3-5x). It integrates transaction monitoring, screening, and FRAML in a single interface, with models for specific fraud types, including account takeovers and merchant fraud.

Key mechanisms:

• Anomaly detection and behavioral profiling: AI builds customer profiles based on transaction patterns, identifying deviations without fixed thresholds. For carding, this means recognizing unusual payments, such as card tests via instant payments.
• Signal Analysis: Focuses on dynamic data (behavior, payment channels), IP, and device (though not always explicitly 1000+). The platform consolidates datasets to identify connections between data points, blocking fraud in real time (150-200 ms).
• Self-tuning models and whitebox AI: The models are patent-pending, with automated feature selection; they are explainable for compliance purposes. This facilitates rapid carding detection without manual rules.
• Educational aspect: Hawk:AI demonstrates the evolution from rule-based to AI-driven systems: machine learning reduces false positives by learning from data for accurate fraud detection. This highlights the role of AI in future-proofing against financial threats, including carding.