What is Credit Card Regex?

In Go, credit card regex is a pattern-based method used to quickly check if a card number follows the format rules of known card issuers like Visa, MasterCard, and American Express.

It’s a first line of defense in web forms and payment integrations to ensure users input the correct type of card before deeper backend verification.

You can validate:

• Visa (starts with 4)

• MasterCard (starts with 51–55 or 2221–2720)

• American Express (starts with 34 or 37)

How Credit Card Regex Works

A credit card regex uses a sequence of characters—literals and special metacharacters—to define the expected structure of a valid card number. With regex, you can match:

• Prefixes that are unique to each issuer (like Visa’s “4” or Amex’s “34/37”)

• Specific lengths (Visa: 13 or 16 digits, Amex: 15 digits, etc.)

• Numeric characters only (no letters or special symbols)

Common Regex Metacharacters:

Understanding a few fundamental regex symbols will help you interpret or modify card validation patterns:

• Start of string

• End of string

• Any digit between 0 and 9

• Exactly n occurrences

• Between n and m occurrences

• Zero or one occurrence (makes previous token optional)

• ()^4[0-9]{12}(?:[0-9]{3})?$ checks for a Visa card by looking for a number that starts with 4 and is either 13 or 16 digits long.

By leveraging these patterns, you can quickly filter out obviously invalid credit card numbers right at the frontend, improving user experience and reducing unnecessary backend processing.

Anatomy of a Credit Card Number

A typical credit card number is more than just a string of digits—it’s carefully structured to include key information:

• Issuer Identification Number (IIN): The first six digits, pinpointing which bank or company issued the card. For example, Visa cards always start with a '4', MasterCard ranges from '51' to '55' or '2221' to '2720', and American Express begins with '34' or '37'.

• Account Number: The middle section, unique to the cardholder, identifies the specific account linked to the card. Its length varies depending on the card issuer.

• Checksum Digit: The very last digit, calculated using the Luhn algorithm, serves as a built-in validation check to catch simple errors in entry.

Understanding these components not only helps in building accurate regular expressions, but also clarifies why specific patterns are required for different card types.

Understanding Credit Card Number Structure

Credit card numbers aren’t just random digits—they follow a standardized structure, and knowing this helps you build precise regex patterns:

• Issuer Identification Number (IIN): The first six digits identify the card’s issuing institution (e.g., a bank or card network).

• Account Number: The digits following the IIN (up to the last digit) are unique to the cardholder’s account.

• Checksum Digit: The final digit is a calculated checksum, used to help catch errors in data entry.

By understanding these components, you can craft regex patterns that not only match the right card type but also ensure the card number is structured correctly.

Credit Card Regex Patterns (with Samples)

These patterns validate length, structure, and prefix rules based on the issuer.

// Visa
^4[0-9]{12}(?:[0-9]{3})?$
// Matches: 4111111111111 or 4111111111111111

// MasterCard
^(5[1-5][0-9]{14}|2[2-7][0-9]{14})$
// Matches: 5555555555554444, 2223000048400011

// Amex
^3[47][0-9]{13}$
// Matches: 371449635398431

Customizing Regex for Other Card Issuers

Need to handle cards beyond Visa, MasterCard, and Amex? You can tailor regex patterns to fit additional issuers by updating the rules to match their unique prefixes and lengths.

For example:

• Discover cards typically start with 6011 or 65 and are 16 digits long:
  ^6(?:0115[0-9]{2})[0-9]{12}$

• Diner’s Club cards often begin with 300–305, 36, or 38, and contain 14 digits:
  ^3(?:0[0-5][68][0-9])[0-9]{11}$

Customizing your patterns like this allows you to extend validation to as many card types as your application requires. Simply add or adjust the regex based on each issuer’s format guidelines.

How to Validate Credit Card Numbers in Go

Here’s a Go example to validate a Visa card using regex:

package main

import (
    "fmt"
    "regexp"
)

func isValidVisa(card string) bool {
    visaRegex := regexp.MustCompile(`^4[0-9]{12}(?:[0-9]{3})?$`)
    return visaRegex.MatchString(card)
}

func main() {
    testCard := "4111111111111111"
    fmt.Printf("Is '%s' a valid Visa card? %t\n", testCard, isValidVisa(testCard))
}

Want to support multiple card types?

You can define a map of regexes for each issuer and switch based on the prefix.

Why Use Regex for Credit Card Validation?

Regex, short for regular expression, is a powerful tool for defining text patterns. When it comes to credit cards, regex helps you enforce the specific structure, length, and prefix rules that each issuer requires—catching simple mistakes before they cause downstream headaches. This is especially important for data integrity in checkout forms, banking apps, and any workflow where you collect or process card numbers.

Data validation isn’t just about catching typos—it’s about making sure that the data you collect is as accurate and secure as possible. Regex acts as a gatekeeper, ensuring only numbers that fit known issuer patterns move forward. For instance, if a user tries to enter a 16-digit number that doesn’t start with the right digits for the selected card type, regex will flag it instantly.

Security and Best Practices

When working with credit card data, security and privacy are non-negotiable:

• Never log or store full card numbers unnecessarily. Only keep what’s essential, and always use proper encryption for storage and transmission.

• Mask card numbers after validation—only show the last 4 digits when displaying back to the user.

• Use regex for format validation only. To really confirm a card number, always follow up with a Luhn algorithm check (checksum validation).

• Strip spaces and hyphens from user input before applying regex.

• Pre-compile your regex in Go using for better performance.

• Keep issuer-specific regexes separate for easier updates and maintenance.

• Regularly review and update your regex patterns to support new card types or formats as standards evolve.

• Use masked input fields in your UI to prevent overexposure of sensitive information.

By combining these practices with robust regex validation, you create a validation workflow that’s both user-friendly and secure—minimizing the risk of invalid data and protecting sensitive cardholder info.

Common Use Cases

• Checkout Pages: Prevent incorrect card number entries before API submission.

• Form Validation: Highlight invalid input early for better UX.

• Banking Apps: Quick verification during KYC or card linking.

• Data Cleansing: Validate card fields in customer or transaction datasets.

Pro Tips

• Use regex for format validation only. Use Luhn’s algorithm for checksum validation.

• Strip spaces and hyphens from user input before applying regex.

• Pre-compile your regex in Go using MustCompile() for performance.

• Mask card numbers after validation when displaying to users.

• Keep issuer-specific regexes separate for better maintainability.

Tools to Reference from Different Categories:

1. Credit Card Generator (from Generator tools)

Why: After validating a regex pattern, users often want to generate test data. You can mention:

“Need dummy test data? Use our [Credit Card Generator] to instantly create sample card numbers that pass most regex validation formats.”

2. MD5 Hash Generator (from Hash Generator)

Why: Hashing is commonly used after validation in secure applications. You can mention:

“Want to store card data securely after validation? Convert it using the [MD5 Hash Generator] or explore SHA variants.”

3. Base64 Encoder (from Encoders & Decoders)

Why: Some systems encode card data before transmission or storage. You can suggest:

“For lightweight encoding of validated data, try our [Base64 Encoder].”

4. Password Regex Go Validator (from Regex Testers — but different field)

Why: To show regex versatility across data types:

“If you’re working with form validations, don’t miss our [Password Regex Go Validator] for secure password input checks.”

Other Language Validators (Same Function):

• Credit Card Regex Java Validator – For backend developers working in Java.

• Credit Card Regex Python Validator – Perfect for quick validation in Python-based scripts or backends.

• Credit Card Regex JavaScript Validator – Ideal for browser-based or Node.js form validations.

Use case blurb:

“Working across multiple stacks? Validate card formats in Java, Python, or JavaScript using our cross-language validators.”

Related Go Validators (Different Fields):

To expand regex relevance while staying within Go:

• Email Regex Go Validator – For validating user email formats.

• Phone Number Regex Go Validator – For mobile or contact form validations.

• SSN Regex Go Validator – Common in fintech or identity validation.

• UUID Regex Go Validator – Great for ID/token validations.

Blurb idea:

“Building a complete validation pipeline? Explore our Go-based validators for Email, Phone Numbers, SSNs, and more.”