What is Credit Card Regex?

Credit card numbers follow defined structures based on the issuer (Visa, MasterCard, Amex, etc.). They typically contain 13–19 digits and may start with specific prefixes:

• Visa: Begins with 4, 13 or 16 digits

• MasterCard: Starts with 51–55 or 2221–2720, 16 digits

• American Express: Starts with 34 or 37, 15 digits

A regex pattern helps validate if the input looks like a valid card number—not if it is real or active.

Why Use Regex for Data Validation?

Regex (regular expressions) is a powerful tool for quickly checking if an input matches a specific format. In the case of credit card numbers, regex allows you to define exactly what a valid card number should look like according to the issuer’s rules. This means you can prevent obvious mistakes or invalid entries right at the door, saving time and reducing errors in your applications.

But the utility of regex doesn’t stop at credit card validation. It’s widely used for many types of pattern matching, such as:

• Email validation: Ensuring addresses contain an “@” symbol and valid domain (e.g., )

• Password requirements: Checking for length, special characters, and a mix of upper/lowercase letters

• Web scraping: Extracting product info, prices, or other data from HTML on e-commerce and other websites

By defining precise patterns, regex streamlines data validation and extraction across a variety of real-world scenarios—making it an essential tool in any developer’s toolkit.

Credit Card Regex Pattern in Python

A regex pattern to match major card types looks like this:

^(?:4[0-9]{12}(?:[0-9]{3})?|      # Visa  
5[1-5][0-9]{14}|                 # MasterCard  
3[47][0-9]{13}|                  # American Express  
6(?:011|5[0-9]{2})[0-9]{12})$    # Discover

This pattern:

• Validates 13–16 digits

• Confirms prefix for the card type

• Does not allow characters or separators

Customizing Regex Patterns for Specific Needs

While the pattern above covers Visa, MasterCard, American Express, and Discover, you may need to validate additional card types depending on your requirements. For example:

• Discover: ^6(?:0115[0-9]{2})[0-9]{12}$

• Diner’s Club: ^3(?:0[0-5][68][0-9])[0-9]{11}$

Customizing your regex patterns allows you to handle a wider range of issuers, such as Diner’s Club and others, ensuring your validation logic is flexible enough for different business needs. Simply add or modify patterns as needed to accommodate specific card formats.

How to Validate Credit Cards Using Regex in Python

Here’s a complete Python example to check if a card number string matches:

import re

def is_valid_credit_card(card_number):
    pattern = re.compile(r'^(?:4[0-9]{12}(?:[0-9]{3})?|5[1-5][0-9]{14}|3[47][0-9]{13}|6(?:011|5[0-9]{2})[0-9]{12})$')
    return bool(pattern.fullmatch(card_number))

# Test
cards = [
    "4111111111111111",  # Visa - valid
    "5500000000000004",  # MasterCard - valid
    "378282246310005",   # Amex - valid
    "1234567890123456"   # Invalid
]

for c in cards:
    print(f"{c} -> {is_valid_credit_card(c)}")

Use Cases

• Payment Gateways: Ensure only validly formatted card numbers are submitted in checkout forms.

• Form Input Validation: Catch typos or fake card numbers before attempting backend verification.

• Pre-validation for APIs: Reduce API load by screening inputs early.

• Data Entry Systems: Prevent incorrect card number entries in CRM or financial platforms.

Enhance this validation by using the Phone Number Regex Python Validator for multi-input forms.

Pro Tips

• This regex only checks format, not real-time validity or card activity.

• Don’t store credit card numbers in plaintext. Always encrypt or tokenize.

• Avoid separators like hyphens or spaces unless you handle them explicitly.

• If you’re validating pasted inputs, strip whitespace using str.replace(" ", "").

• Combine it with Password Regex Python Validator for secure signup or payment flows.

A Few More Best Practices for Credit Card Regex:

• Use masked input fields to only display part of the credit card number—protecting it from shoulder-surfers and prying eyes.

• Never log or store unnecessary details from validation steps. Reduce your risk footprint by minimizing what’s kept.

• Always send and store sensitive data using encryption. Unencrypted transmission is an open invitation to trouble.

• Review and update your regex patterns periodically—credit card formats do evolve, and you want to stay compatible with new card types.

Regex can help you quickly spot invalid formats, but the rest of your security and privacy protocols do the real heavy lifting. Treat every piece of card data as if it could walk out the door—because in the wrong hands, it just might.

Security & Privacy Tips for Credit Card Regex:

• Mask sensitive input: Show only the last four digits during entry or display, hiding the rest with asterisks or dots.

• Never log full card numbers: Resist the urge to log or store credit card numbers for debugging—mask or redact them in logs to avoid accidental exposure.

• Encrypt everything: Use industry-standard encryption for storage and transmission. HTTPS is a must-have.

• Update your patterns: New cards and formats pop up. Keep your regex fresh so you don’t reject legitimate users.

• Limit data retention: Only keep what you need, and purge credit card details as soon as possible.

Staying vigilant with these practices helps ensure your regex validation isn’t just accurate—it’s also safe for your users.