How banks counteract fraudsters' adaptation

[Mutt]

Fraudsters (carders) are constantly adapting to banks’ risk analysis systems, including those used by 3D-Secure, in an attempt to bypass anomaly detection algorithms such as Isolation Forest and autoencoders. Banks, in turn, are developing complex strategies to counter these adaptations, combining technology, processes, and collaboration. Here’s a detailed explanation of how banks are dealing with fraudster adaptations, with a focus on machine learning (ML) and other approaches.

1. Understanding Scammer Adaptation​

Fraudsters use a variety of methods to bypass detection systems:

• Imitation of normal behavior: Conduct transactions that resemble legitimate ones (for example, small purchases at the client’s familiar stores).
• Device Compromise: Use stolen devices or authentication data to make transactions appear "normal."
• Social Engineering: Trick customers into getting OTPs (one-time passwords) or access to accounts.
• Card testing: Perform a series of small transactions to check the validity of the stolen data, avoiding suspicious patterns.
• Using proxies and VPNs: Mask geolocation or IP so that transactions appear to be from the client's native region.
• SIM swapping: Intercepts SMS with OTP, replacing the victim's SIM card.
• Mass attacks: Use botnets or automated systems to conduct multiple transactions simultaneously, making them difficult to analyze.

Banks are countering these methods by updating technology and processes to stay ahead of fraudsters.

2. Technological countermeasures​

Banks are using ML and other technologies to adapt to new threats.

Updating and retraining ML models​

• Frequent retraining:
  • Models (Isolation Forest, Autoencoders, Random Forest) are retrained daily or weekly to account for new fraud patterns.
  • For example, if fraudsters start using VPNs to mask their IP, the models are trained to recognize anomalies in proxy usage (such as unstable IP addresses).
• Incremental learning:
  • Models are updated gradually, adding new data without the need for complete retraining, which saves resources.
  • Algorithms such as online gradient descent for neural networks or ensemble updating for Isolation Forest are used.
• Detecting new threats:
  • Unsupervised algorithms (such as autoencoders) detect unknown anomalies even if fraudsters change tactics.
  • Ensemble methods combine multiple models (e.g. Isolation Forest + neural networks) to improve robustness against new attacks.

Advanced feature analysis​

• Dynamic Features: Banks add new features to ML models such as:
  • Behavioral data: typing speed, mouse movements, device tilt angle (for mobile applications).
  • Technical indicators: browser type, network latency, use of emulators or virtual machines.
  • Temporal patterns: analyzing transaction sequences with LSTM (Long Short-Term Memory).
• Contextual analysis: Compare a transaction to global trends (e.g. a surge in fraud in a particular region) or a store's reputation.
• Proxy/VPN detection: Models analyze IP addresses to see if they belong to data centers or known proxy servers.

Real-time analysis​

• Speed of response: Fraud Detection Systems (FDS) process millions of transactions in milliseconds using cloud platforms (AWS, Google Cloud) or Apache Spark.
• Adaptive thresholds: Anomaly thresholds (e.g. in autoencoders) are dynamically adjusted based on current fraud trends.

Biometrics and Strong Customer Authentication (SCA)​

• Within the framework of 3D-Secure 2.0 and PSD2, banks use biometric data (fingerprints, Face ID), which is more difficult to counterfeit than SMS-OTP.
• Passive biometrics (analysis of behavior, such as how a user holds a phone) helps identify device compromise.

Tokenization and virtual cards​

• Tokenization: Replaces card data with a unique token (like in Apple Pay) that is useless if intercepted.
• Virtual cards: Limit amounts and expiration dates, minimizing the damage from compromise.

3. Process measures​

Banks are implementing organizational measures to make it more difficult for fraudsters to adapt:

• Multi-level authentication:
  • Combine OTP, biometrics and behavioral factors in 3D-Secure 2.0 to increase the difficulty of counterfeiting.
  • For example, even if the OTP is intercepted, biometric verification is required.
• Monitoring and notifications:
  • Customers receive push notifications about every transaction, allowing them to quickly report suspicious activity.
  • Banks monitor attempted mass attacks in real time and temporarily block suspicious IPs or stores.
• Client training:
  • Banks are running awareness campaigns against phishing and social engineering to prevent customers from sharing OTPs with scammers.
  • Provide recommendations such as using official apps and avoiding suspicious links.

4. Collaboration and data exchange​

• Consortia and databases:
  • Banks share fraud information through platforms such as Visa Advanced Authorization or Mastercard Fraud Intelligence.
  • For example, if one bank detects a spike in attacks from a particular IP, other banks update their models.
• Cooperation with law enforcement agencies:
  • Banks report fraud to Interpol or local cyber units to track and disrupt organized schemes.
• Partnerships with technology companies:
  • Integration with FDS providers (FICO, Feedzai) to access global fraud data.
  • Using external services to check the reputation of IPs, devices or stores.

5. Counteracting specific adaptations of fraudsters​

• Imitation of normal behavior:
  • Banks use behavioral biometrics and time series (LSTM) to identify subtle anomalies that fraudsters cannot perfectly fake.
  • For example, analyzing the input speed or the tilt angle of the device.
• Device compromise:
  • Devices are checked for malware or emulators using signatures and ML models.
  • Banks require biometric authentication through secure apps rather than SMS.
• SIM swapping:
  • Telecom operators are implementing protection against SIM substitution (for example, two-factor authentication for SIM change).
  • Banks are switching to push notifications in apps that are harder to intercept.
• Testing cards:
  • ML models (e.g. LSTM) analyze sequences of transactions, identifying testing patterns (repeated small payments).
  • Banks temporarily block the card after several suspicious transactions.
• Using proxy/VPN:
  • The models analyze network metadata (latency, connection type) and compare it with proxy server databases.
  • Geographical correlations are used (for example, mismatch between IP and declared geolocation).

6. Example of a script​

A fraudster steals card details and tries to conduct a transaction for 500 rubles in a familiar store, using a VPN to impersonate the client's IP. Bank:

• ML analysis: Isolation Forest notices an anomaly in VPN usage (unusual IP), and the autoencoder records high recovery error due to non-standard time or device.
• Response: Requests biometric verification via app (3D-Secure 2.0).
• Update: If the attack is new, the bank adds data to the training set so that the model adapts to using that VPN.
• Collaboration: Shares IP information with other banks through a consortium.

7. Limitations and Challenges​

• Speed of adaptation: Fraudsters can change tactics faster than banks can update their models.
• False positives: Overly aggressive measures (such as frequent OTP requests) irritate customers.
• Resources: Constantly updating models and infrastructure requires significant resources.
• Privacy: Collecting behavioral data raises privacy concerns.

8. Future Directions​

• AI for attack prediction: Using generative models (e.g. GANs) to simulate and prepare for new types of fraud.
• Quantum computing: Potentially speed up big data analysis for faster discovery.
• Blockchain for authentication: Can provide decentralized and secure verification of transactions.

If you want to dive deeper into a specific aspect, like how banks protect themselves from SIM swapping or how ML models are updated in real time, ask and I'll give you a detailed answer with examples!