How Banks Detect BIN Attacks: Defense Mechanisms

[Cloned Boy]

Educational analysis for understanding anti-fraud systems.

1. What is a BIN attack?​

A BIN attack is a method of selecting card numbers in a certain BIN range (for example, 4147 20XXXXXX). Fraudsters use:

• Number generators (based on the Luhn algorithm).
• Automated scripts for checking card validity.

Target:

• Find "live" cards for fraudulent transactions.

2. How do banks detect BIN attacks?​

2.1. Behavior Analysis​

Method	How it works	Example
Velocity Checking	Blocks multiple requests from one IP/device in a short period.	100+ requests to BIN 414720 in 1 minute → blocking.
Geo-anomalies	If requests come from countries not associated with the BIN (eg US BIN + Nigeria IP).	BIN 4276 29 (Sberbank) + IP Thailand → suspicion.
Non-standard amounts	Test payments ($0.99, $1.00) or recurring equal amounts.	10 transactions at $1.00 → risk flag.

2.2. Machine learning technologies​

• Prediction models: Analyze attack patterns (e.g. sequentially trying XXXX in a card number).
• Graph algorithms: Identify connections between suspicious transactions (common IP, devices, BIN).

Example:
If 50 cards with BIN 5154 61 are checked through one proxy → the system marks them as a coordinated attack.

2.3. BIN lists and blacklists​

• Banks and payment systems (Visa, Mastercard) maintain databases of “dangerous” BINs (for example, prepaid cards).
• Dynamic update: New BIN ranges are added to stop lists after the first attacks.

2.4. CAPTCHA and Challenge tests​

• After several attempts the system requires:
  • CAPTCHA (filters out bots).
  • Additional authentication (e.g. 3-D Secure for suspicious BINs).

3. Example of protection operation​

Attack scenario:

1. The fraudster generates 100 card numbers with BIN 4147 20 (Bank of America).
2. Checks them through the store's website by sending requests for $0.50.

How the bank reacts:

• Notices 10+ requests/sec from one IP → blocks IP.
• Sees abnormal amounts ($0.50) → requires 3-D Secure.
• Checks BIN in stop list → rejects all transactions.

4. How is protection improved?​

• EMV 3-D Secure 2.0:
  • Uses tokenization (replacing the real card number with a token).
  • Analyzes device behavior (mouse, keyboard, IP).
• Biometrics: For BINs from the “risk group”, Face ID/fingerprint is mandatory.

Conclusion​

Banks combine:
Behavioural analysis (anomalous queries).
Machine learning (pattern detection).
BIN blacklists.
Multi-factor authentication.

For legal research:

• Experiment with demo APIs (Stripe, Adyen).
• Study open cases (e.g. NIST fraud reports).

Want to understand how graph algorithms work in antifraud or tokenization methods ? Ask!