How banks detect fake BINs: protection mechanisms

[Cloned Boy]

Educational analysis for understanding anti-fraud systems.

1. What is a fake BIN?​

BIN (Bank Identification Number) — the first 6-8 digits of the card number, identifying the bank, card type and country.
A fake BIN is:

• Non-existent numbers (eg 000000).
• BINs that do not correspond to real bank ranges.
• Stolen BINs used to generate cards.

2. Methods for detecting fake BINs​

2.1. Validation by Luhn algorithm​

• Banks check whether the card number complies with the Luhn Algorithm.
• Example:
  • Card 4147 2020 1234 5678 → Checksum verification.
  • If it does not pass the test → rejected.

Code for testing (Python):

def luhn_check(card_number):
total = 0
for i, digit in enumerate(reversed(card_number)):
num = int(digit)
if i % 2 == 1:
num *= 2
if num > 9:
num -= 9
total += num
return total % 10 == 0

print(luhn_check("4147202012345678"))  # True/False

2.2. Checking against BIN databases​

• Banks and payment systems (Visa, Mastercard) maintain databases of valid BINs.
• Examples of services:
  • Binlist.net (open API).
  • Visa BIN Lookup, Mastercard BIN Search (for partners).

How it works:

1. The transaction comes with BIN 123456.
2. The system checks if such BIN exists in the database.
3. If not → the transaction is rejected.

2.3. Behavior Analysis​

• Suspicious patterns:
  • Many requests with BIN from different countries in a short time.
  • Attempts to use BINs that do not match the country of the IP address.
• Example:
  • BIN 5154 61 (Germany) + IP from Nigeria → fraud flag.

2.4. EMV 3-D Secure and Cryptography​

• For BINs that support 3DS2, the bank requires a cryptographic signature of the transaction.
• Fake BINs cannot pass this check.

3. How scammers try to bypass checks (For protection, not for attacks!)​

1. Using old BINs:
   • Some BINs issued before 2010 may not have strict security.
   • Solution: Banks are gradually withdrawing them from circulation.
2. Substitution of BIN in transactions:
   • Rarely, but possible due to vulnerabilities in the merchant's software.
   • Solution: EMV 3DS 2.0 verifies the BIN signature.

4. Example of the system operation​

Scenario:

1. The fraudster generates a card with BIN 000123.
2. Trying to pay for a purchase.
3. Bank system:
   • Checks BIN → it is not in the database.
   • Checks Luna algorithm → number is invalid.
   • Rejects the transaction.

5. How is protection improved?​

• Dynamic BIN lists: Automatically update BIN lists.
• Machine Learning: Real-Time Attack Pattern Analysis.
• Tokenization: Replacing the real card number with a token (Apple Pay, Google Pay).

Conclusion​

Banks detect fake BINs via:
Luhn Algorithm → Filters out non-existent numbers.
BIN databases → Checks the legitimacy of ranges.
Behavioral analysis → Detects anomalous patterns.
3-D Secure → Cryptographic verification.

For legal research:

• Experiment with the Binlist API.
• Learn PCI DSS (Card Data Security) standards.

Need more details about tokenization or 3-D Secure? Ask!