How does artificial intelligence affect carding and antifraud? (AI in fraud and protection, examples of attacks and countermeasures)

[Student]

For educational purposes, I will provide a more detailed analysis of the impact of artificial intelligence (AI) on carding (fraud using stolen credit card data) and anti-fraud (measures to prevent fraud), focusing on technical aspects, attack examples, countermeasures, and current and future challenges. I will also try to explain complex concepts in an accessible language so that the material can be understood by a wide audience, including those studying the topic for educational purposes.

What is carding and how does AI affect it?​

Carding is a type of fraud in which criminals use stolen credit or debit card data to make unauthorized transactions, purchases, or withdrawals. Card data is usually obtained through phishing, skimming, data leaks, darknet purchases, or database hacks. AI is enhancing both fraudsters’ capabilities and defense systems, creating a dynamic “arms race” between carders and anti-fraud systems.

AI affects carding and anti-fraud in the following ways:

1. Automate and scale attacks/defenses.
2. Analyzing data to identify vulnerabilities or anomalies.
3. Generating synthetic data to deceive or protect systems.
4. Adaptability to new methods of defense or attack.

Let's take a closer look at how AI is used in carding and antifraud, with examples of attacks, countermeasures and technical details.

AI in Carding: How Do Fraudsters Use AI?​

Fraudsters use AI to improve efficiency, bypass security systems, and minimize the risk of detection. Here are the key ways AI is used in carding:

1. Generating fake data​

• How it works: Generative AI models, such as generative adversarial networks (GANs) or transformer-based models (e.g., GPT), create synthetic data that looks credible. This could be fake names, addresses, phone numbers, emails, or even biometric data (voice, photo).
• Attack example: A fraudster uses GAN to create fake documents (passport, driver's license) to pass KYC (Know Your Customer) on cryptocurrency exchanges. AI can generate a photo of a face that does not exist, but looks realistic.
• Technical aspect: GANs consist of two neural networks - a generator that creates data, and a discriminator that evaluates its plausibility. Once trained, a GAN can generate data that is indistinguishable from real data, making it difficult to verify authenticity.

2. Automation of attacks​

• How it works: AI bots using machine learning algorithms automate the process of checking stolen card data. They can test thousands of cards per minute on sites with low security levels, selecting valid combinations.
• Attack example: Carders use an AI bot to check card numbers on small online stores that lack 3D-Secure (additional authentication). The bot adapts to restrictions (such as pauses between attempts) to avoid suspicion.
• Technical aspect: Such bots often use reinforcement learning algorithms, where the AI is trained to maximize the success of transactions by avoiding blockages. The bot can analyze server responses (e.g. error codes) and adjust the strategy.

3. Advanced Phishing and Social Engineering​

• How it works: AI, especially natural language processing (NLP) models, analyzes victims' data from social media, leaks, or public sources to create personalized phishing emails, messages, or calls.
• Attack example: The scammer uses AI to create an email that looks like an official notice from the bank, with precise mentions of the victim’s name, recent transactions, or even communication style. The email contains a link to a fake website where the victim enters their card details.
• Technical aspect: NLP models like BERT or GPT are trained on large text datasets to generate persuasive text. AI can analyze data leaks (e.g. through the dark web) and match them with social media profiles for targeting.

4. Bypassing anti-fraud systems​

• How it works: AI algorithms study the behavior of anti-fraud systems, identifying their weaknesses. For example, fraudsters can use AI to imitate legitimate behavior (geolocation, purchase patterns, device type).
• Attack example: The carder uses AI to emulate transactions from the victim's region by faking device metadata (browser, IP address). This allows bypassing geographic filters of anti-fraud systems.
• Technical aspect: AI can use clustering algorithms to analyze transaction data and identify “safe” patterns of behavior that do not raise suspicion.

5. Deepfakes to spoof biometric authentication​

• How it works: AI creates deepfakes - fake videos, images or voice recordings that are used to bypass biometric systems or call centers.
• Example attack: A fraudster uses AI to create a voice deepfake to call a bank and verify themselves as the account holder.
• Technical aspect: Deepfakes are created using deep neural networks, such as autoencoders or GANs. For example, voice deepfakes are generated by models like WaveNet, which synthesize speech based on a few seconds of real voice.

AI in Antifraud: How Do Companies Fight Carding?​

Companies, banks, and payment systems use AI to prevent fraud, detect anomalies, and protect users. AI allows you to process huge amounts of data in real time, adapt to new threats, and minimize false positives.

1. Analysis of user behavior​

• How it works: AI systems based on machine learning (such as clustering algorithms or neural networks) create user behavior profiles based on transaction history, geolocation, login time, devices, and other parameters. Any deviation from the norm is flagged as suspicious.
• Example countermeasure: If a user typically makes purchases in one region and at a certain time, and then a transaction occurs from another country, the AI system (e.g. MasterCard Decision Intelligence) will block it and request two-factor authentication (2FA).
• Technical aspect: Anomaly detection algorithms such as Isolation Forest or autoencoders are used, which are trained on historical data and identify outliers. For example, the model can compare the current transaction with a “normal” user profile, calculating the probability of fraud.

2. Phishing detection​

• How it works: AI analyzes the content of emails, links, and websites using NLP and computer vision to detect phishing attacks. Algorithms identify suspicious text patterns, fake domains, or visual elements.
• Example of a countermeasure: Systems like Barracuda Sentinel or Google Safe Browsing use AI to analyze incoming emails. If an email contains suspicious keywords or a link to a phishing site, it is blocked.
• Technical aspect: NLP models (e.g. BERT) analyze the semantics of the text, and computer vision algorithms check the visual similarity of fake sites to the original ones (e.g. bank logos).

3. Improving biometric authentication​

• How it works: AI strengthens biometric verification systems (facial, voice, fingerprint recognition), making them resistant to deepfakes and other attacks.
• Example of a countermeasure: Systems such as iProov or BioCatch use AI to analyze facial micro-movements (e.g. blinking) or behavioral biometrics (mouse movement, typing speed) to distinguish a real user from a fake.
• Technical aspect: Computer vision algorithms (e.g. convolutional neural networks, CNN) analyze the video stream in real time, checking for signs of forgery (e.g. inconsistencies in lighting or textures). Behavioral biometrics uses recurrent neural networks (RNN) to analyze temporal sequences of actions.

4. Detecting anomalies in transactions​

• How it works: AI analyzes millions of transactions in real time, identifying suspicious transactions based on their characteristics (amount, seller, time, geolocation).
• Countermeasure example: FICO Falcon Fraud Manager uses AI to analyze transactions and assign them a "risk score." If the score exceeds a threshold, the transaction is paused for additional review.
• Technical aspect: It uses ensemble machine learning methods (e.g. gradient boosting on decision trees, XGBoost) that are trained on historical data of fraudulent and legitimate transactions. The model calculates the probability of fraud for each transaction.

5. Counteracting bots​

• How it works: AI systems analyze user behavior (mouse movement, typing speed, interaction with the interface) to distinguish bots from people.
• Example countermeasure: Akamai Bot Manager or Google reCAPTCHA use AI to detect automated attacks, blocking bots that try to test card data.
• Technical aspect: Classification algorithms (e.g. SVM or neural networks) analyze session metadata (e.g. HTTP headers, time intervals between actions). Bots often give themselves away due to the lack of "human" patterns, such as random delays or variations in mouse movement.

Specific examples of attacks and countermeasures​

1. Attack: Carding via Mass Card Checking​

• Description: Fraudsters buy databases of cards on the darknet (for example, through sites like Genesis Market) and use AI bots to check their validity on sites with poor security.
• Countermeasure: Payment systems like Visa Advanced Authorization use AI to analyze transactions in real time. If the system notices many small transactions from the same IP address or device, it blocks them and notifies the bank.
• Technical aspect: AI uses time series algorithms to identify patterns of mass checks (e.g. high frequency of transactions with errors).

2. Attack: Phishing with AI-generated emails​

• Description: Fraudsters use AI to create personalized phishing emails that contain the victim's data (name, address, recent purchases) obtained from leaks.
• Countermeasure: Anti-phishing systems like Microsoft Defender for Office 365 use AI to analyze email headers, links, and content. If an email contains a fake domain (e.g. bank0famerica.com instead of bankofamerica.com), it is blocked.
• Technical aspect: AI uses clustering algorithms to group emails by features (e.g. text style, sender) and blocks abnormal groups.

3. Attack: Bypassing KYC with Deepfakes​

• Description: Scammers use AI to create fake videos or voices to pass identity verification on platforms.
• Countermeasure: Systems like Jumio or Onfido use AI to analyze videos in real time, checking for signs of deepfakes (e.g. lighting inconsistencies, lip movement anomalies).
• Technical aspect: Computer vision algorithms analyze the video stream using CNN and also check for "liveness" (liveness detection), requiring random movements from the user (for example, turning the head).

4. Attack: Emulating Legitimate Behavior​

• Description: AI bots imitate real user behavior (e.g. geolocation, device type, purchasing patterns) to bypass anti-fraud systems.
• Countermeasure: Systems like BioCatch use behavioral biometrics, analyzing the user's unique interaction patterns with the device (e.g., the angle of the phone, the speed of scrolling).
• Technical aspect: The AI uses recurrent neural networks (RNN) to analyze temporal sequences of actions, creating a unique “fingerprint” of the user.

Challenges and Problems​

1. Arms race:
   • Fraudsters and anti-fraud systems are constantly improving their AI algorithms. For example, if an anti-fraud system gets better at detecting deepfakes, fraudsters develop more complex models, such as improved GANs.
   • Solution: Companies must invest in continuous training of AI models (online learning) to adapt to new threats.
2. False positives:
   • AI antifraud sometimes mistakenly blocks legitimate transactions, which causes customer dissatisfaction. For example, a purchase made while on holiday abroad may be flagged as suspicious.
   • Solution: Use adaptive models that take into account context (e.g. travel data from the user's calendar).
3. Ethics and Confidentiality:
   • AI systems collect huge amounts of data (geolocation, transaction history, behavior), which raises privacy concerns.
   • Solution: Use differential privacy techniques that allow data to be analyzed without revealing personal information.
4. AI Availability for Fraudsters:
   • Open source AI models (such as those available through GitHub) and cloud services make it easier for fraudsters to access powerful tools.
   • Solution: Limit the distribution of high-performance models and tighten regulation of the darknet.

The Future of AI in Carding and Anti-Frode​

1. Improved generative AI: Fraudsters will use more complex models to create deepfakes and synthetic data, which will require anti-fraud systems to use new detection methods (e.g. quantum machine learning).
2. Real-time and automation: Anti-fraud systems will increasingly rely on AI to instantly analyze transactions, minimizing delays for users.
3. Data Collaboration: Banks and companies will begin to increasingly share fraud data (with privacy concerns) to train more accurate AI models.
4. AI regulation: Governments may introduce strict regulations on the use of AI for fraudulent purposes, making it harder for carders to access the technology.

Conclusion​

AI is radically changing the landscape of carding and anti-fraud. Fraudsters are using AI to automate attacks, create fake data, and bypass security systems, while companies are using AI to analyze behavior, detect anomalies, and strengthen biometric authentication. Technical aspects such as neural networks, anomaly detection algorithms, and behavioral biometrics are key in this fight. However, the arms race is ongoing, and success depends on the speed of adaptation, the quality of the data, and the ethical use of technology.

For educational purposes, it is important to understand that AI is a tool that can be used for both good and evil. Understanding its capabilities and limitations helps develop more effective defenses and prevent fraud. If you want to dive deeper into a specific technical aspect (such as anomaly detection algorithms or creating deepfakes), let me know and I will provide even more detailed information!