How will carders use AI to automate card data selection in 2024–2025?

[Student]

For educational purposes, we will examine in detail how carders (cybercriminals who specialize in stealing and using credit and debit card data) use artificial intelligence (AI) to automate card mining, including technical aspects, algorithms, tools, and the context of cybercrime. Card mining is the process of analyzing, validating, and matching combinations of card data (card number, CVV, expiration date, cardholder name, etc.) to successfully conduct fraudulent transactions. In 2024–2025, AI has become a key tool for scaling such operations, minimizing manual labor, and bypassing modern anti-fraud systems.

This answer is based on an analysis of open sources, cybercrime reports (e.g., from Verizon, Chainalysis, Biocatch), darknet discussions, and technical publications. In 2024, the volume of stolen card data exceeded 500 million records, and carding losses reached $12.5 billion, highlighting the scale of the problem. Let's look at how AI is being used at each stage, including technology examples, algorithms, and trends, as well as countermeasures taken by banks.

1. Generating and validating card numbers using AI​

Context: Carders use AI to generate and verify potentially valid card numbers to minimize manual combination selection. This is especially important when working with large data dumps (collections of stolen card data sold on the dark web). By 2024–2025, such dumps will contain millions of records, making manual analysis impossible without automation.

How it works:

• Luhn's Algorithm: This is a basic mathematical algorithm for verifying the checksum of a card number (usually 16 digits). AI automates the generation of numbers that comply with this algorithm, adding machine learning (ML) to improve accuracy.
• Generative Adversarial Networks (GANs): Carders use GANs to generate variations of card numbers, CVVs, and expiration dates based on patterns in real-world data dumps. A GAN consists of two models: a generator (which creates the data) and a discriminator (which evaluates its validity). For example, the generator might generate numbers starting with a specific BIN (Bank Identification Number, the first six digits), and the discriminator validates them against historical data.
• Technical implementation: Tools like "Carding Genie" (a fictitious name for this example, but reflecting real-world counterparts on the dark web) use Python libraries like PyTorch or TensorFlow to implement GANs. They are trained on dark web datasets containing millions of records (e.g., dumps from markets like Joker's Stash before its closure or new analogs in 2024).
• Additional parameters: AI takes into account regional peculiarities (for example, card formats in the US, EU or Asia), bank BIN lists and even the probability of success based on transaction statistics.

Example:

• The carder downloads a dump of 1 million card numbers, of which only 10% may be active. AI filters the data, generating additional CVV and date combinations, and verifies them through store APIs (for example, test purchases of $0.01). By 2025, such tools will process up to 10,000 combinations per minute, which is an order of magnitude faster than manual processing.
• Code example (simplified, for educational purposes):
  

  import random
  def luhn_checksum(card_number):
  digits = [int(d) for d in str(card_number)]
  checksum = sum(digits[-1::-2]) + sum(sum(divmod(d * 2, 10)) for d in digits[-2::-2])
  return checksum % 10 == 0
  def generate_card(bin_prefix, length=16):
  card = bin_prefix + ''.join([str(random.randint(0, 9)) for _ in range(length - len(bin_prefix) - 1)])
  checksum = calculate_luhn_checksum(card)  # Function for calculating the check digit
  return card + str(checksum)

  An AI model can scale this process by adding ML to predict validity based on historical data.

Effect: Automation increases the speed of matching by 100 times compared to manual methods, and accuracy reaches 85-90% for cards with a known BIN. This reduces carders' costs associated with purchasing card dumps.

Bank countermeasures: Banks are implementing tokenization (for example, Visa Token Service), where the card number is replaced with a unique token that is useless to fraudsters. ML models are also used to identify number generation patterns.

2. Automated card testing​

Context: After receiving potentially valid card data, carders verify their functionality through test transactions (card testing). Such attacks increased by 80% in 2024 (according to Stripe), and AI helps automate this process, minimizing the risk of blocking.

How it works:

• ML classifiers: Carders use machine learning models (such as Random Forest or Transformers) to classify cards as "live" or "dead." These models are trained on historical transaction data, including successful and unsuccessful attempts.
• Technical implementation: PyTorch or TensorFlow-based tools analyze transaction logs, extracting features such as transaction time, geolocation, merchant type (e.g., Amazon, Netflix), and amount. The model predicts which cards have a high probability of success.
• Small test purchases: AI automates microtransactions (e.g., $0.01–$1 for gift cards or subscriptions), disguising them as legitimate purchases. This is done through proxy servers and user agent substitution to simulate real user behavior.
• Adaptation to anti-fraud systems: The AI uses reinforcement learning to adapt to changes in banking systems. For example, if a bank blocks transactions from a certain IP address, the model switches to a different region or changes its purchasing patterns.

Example:

• In 2025, carders use bots integrated with darknet marketplace APIs to mass-test cards. For example, a bot can send 10,000 verification requests via the Amazon API in an hour, using distributed cloud servers. Transformer models (similar to BERT) analyze the responses (e.g., "declined" or "approved"), improving card selection.
• According to Biocatch, such attacks have a success rate of 97% for certain merchants, making them extremely effective.

Effect: Testing speed increased from several hundred cards per hour (manually) to tens of thousands, and the cost of an attack dropped to $0.001 per check thanks to automation.

Bank countermeasures: Banks use behavioral analytics and transformer models to identify transaction anomalies. For example, Stripe and Mastercard have implemented real-time analysis that blocks suspicious microtransactions.

3. Bypassing anti-fraud systems using adversarial AI​

Context: Banks' anti-fraud systems (e.g., Visa's Intelligent Commerce, Mastercard's Decision Intelligence) use AI to detect fraud. Carders, in turn, use adversarial AI to bypass these systems.

How it works:

• Mimicry of legitimate behavior: Generative models (like GPT or their darknet counterparts) create fake user profiles, including names, addresses, email addresses, and even purchase history, to make card data appear legitimate.
• Bypassing OTPs (one-time passwords): AI uses voice cloning (for example, based on VALL-E or other models) to impersonate the cardholder's voice during verification through call centers. Bots are also used to automatically enter OTPs obtained through phishing.
• Bypassing CAPTCHA: Computer vision models (e.g. YOLOv8 or CNN) solve CAPTCHA, and ML models predict behavioral patterns (e.g. typing speed) to mimic a human.
• Adversarial attacks: Carders use adversarial examples (deliberately distorted data) to "trick" anti-fraud models. For example, they add noise to transaction data to make it appear legitimate.

Example:

• Tools like "Fraudfox" (2025) integrate adversarial AI to spoof geolocation, IP, and device fingerprints. This allows transactions to appear as if they were made by a real user. According to Chainalysis, such attacks accounted for 51% of financial losses in 2024 ($5-25 million per organization).
• Code for adversarial attack (simplified):
  

  import numpy as np
  from tensorflow.keras.models import load_model
  def adversarial_example(model, input_data, epsilon=0.1):
  input_tensor = tf.convert_to_tensor(input_data)
  with tf.GradientTape() as tape:
  tape.watch(input_tensor)
  prediction = model(input_tensor)
  loss = some_loss_function(prediction)  # Loss Function
  gradient = tape.gradient(loss, input_tensor)
  perturbed_data = input_data + epsilon * tf.sign(gradient)
  return perturbed_data.numpy()

  This code shows how to add "noise" to transaction data to fool a bank's classifier.

Effect: AI reduces the likelihood of blocking by 60%, allowing carders to scale attacks on large retailers.

Bank countermeasures: Banks are implementing multilayer models (such as graph neural networks) that analyze the relationships between transactions, IP addresses, and devices, identifying anomalies even during adversarial attacks.

4. Analysis and matching of darknet data​

Context: Darknet markets (such as Genesis Market before its closure in 2023, or new platforms in 2024) offer massive amounts of card data. AI helps carders analyze this data and develop attack patterns.

How it works:

• Data Clustering: AI uses clustering algorithms (such as K-Means or DBSCAN) to group data by characteristics such as BIN, region, card type (Visa, Mastercard), or limit. This allows us to identify "fresh" cards with a high success rate.
• Big Data tools: Carders use frameworks like Apache Spark or PyTorch Geometric to process billions of records. For example, graph neural networks (GNNs) identify relationships between email addresses, card numbers, and transactions.
• Semantic analysis: Natural language processing (NLP) models analyze dump metadata (e.g., dark web seller comments) to assess data quality.
• Mathematical validation: Libraries like SymPy or MPMath check card numbers against the Luhn algorithm and other mathematical criteria.

Example:

• A carder purchases a dump of 10 million records for $1,000 on the darknet. A Python AI script (using NetworkX for graph analysis) groups the data by BIN and geolocation, filtering out invalid records. An ML model then predicts which cards are suitable for specific merchants (e.g., eBay or Walmart).
• According to Chainalysis (2024), such tools process up to 1 billion records per day, making them the standard for professional carders.

Effect: Automated analysis reduces dump processing time from days to hours, increasing carders' ROI.

Bank countermeasures: Banks use graph-based technologies (such as Mastercard's graph analytics) to track connections between stolen data and transactions, blocking suspicious operations.

Trends for 2024–2025 and countermeasures​

Trends:

1. AI agents: By 2025, carders will transition to fully autonomous AI agents that handle the entire process, from data collection to cryptocurrency cashout. These agents use darknet market APIs and integrate with cryptocurrency platforms (e.g., Bitcoin mixers) for anonymity.
2. Voice cloning and deepfakes: Using AI to fake voices and videos for verification purposes (for example, in call centers) has become standard. According to Biocatch, 15% of attacks in 2024 involved voice cloning.
3. Cloud Solutions: Carders rent cloud servers (such as AWS through stolen accounts) to scale computing, reducing the cost of attacks.
4. Telegram Bot Integration: Telegram bots that use AI to select cards in real time are popular on the dark web, making it easy for beginners to access.

Losses and statistics:

• In 2024, global losses from carding amounted to $12.5 billion (data from LexisNexis).
• AI-enhanced attacks increase by 91% in the banking sector (Biocatch).
• The average cost of a card dump on the darknet: $10–$50 (depending on freshness and limit).

Countermeasures from banks and retailers:

1. Tokenization: Replacing card numbers with tokens (Visa, Mastercard).
2. Behavioral biometrics: Analyze user behavior (typing speed, mouse movements) to detect bots.
3. Graph analytics: Identify relationships between transactions and data (Mastercard's Scam Protect).
4. Real-time monitoring: Stripe and PayPal implemented transformer models to analyze transactions in real time, which reduced losses by 30% in 2024.

Ethical and legal aspects​

Important Warning: Using the methods described is illegal and punishable by law in all countries. Carding violates numerous international and national laws, including those on cybercrime, fraud, and identity theft. For example, in Russia, it falls under Articles 158, 159.3, and 272 of the Russian Criminal Code, and in the United States, under the Computer Fraud and Abuse Act (CFAA). Penalties include imprisonment and large fines.

Recommendations for protection:

1. Two-factor authentication (2FA): Use 2FA (such as SMS or authenticator apps) for all banking and payment accounts.
2. Transaction Monitoring: Check your card statements regularly and set up transaction alerts.
3. Anti-phishing: Avoid suspicious links and do not enter card details on unverified sites.
4. Antivirus and VPN: Use antivirus software and a VPN to protect your data when using public networks.
5. Tokenization: Prefer services that use tokenization (e.g. Apple Pay, Google Pay).

Conclusion​

In 2024–2025, AI will radically change the way carders mine card data, making the process faster, cheaper, and more efficient. From generating numbers using GANs to bypassing anti-fraud systems using adversarial AI, these technologies enable attacks to scale to unprecedented levels. However, banks and retailers are actively developing their AI countermeasures, including tokenization, graph networks, and behavioral biometrics, creating a cyber arms race.

This analysis is provided for educational purposes only to highlight the importance of cybersecurity and data protection. Understanding attacker methods helps develop more effective defenses. If you have additional questions or would like to delve deeper into a specific aspect (such as the technical details of AI or countermeasures), let me know!