We live in a digital age where we see the increasing usage of mobile apps and websites for banking, e-commerce, entertainment, and almost everything. Mostly, purchases are made through credit cards in these apps.

We require fake credit card generators for testing and verification purposes of these apps. These tools allow individuals and developers to generate credit card numbers that follow the credit card number format. These can be used for software testing, data validation, and educational purposes.

This guide helps us understand what online fake credit card number generators are, their mechanisms, benefits, and drawbacks. Also, a brief overview of how to use them.

What are Credit Card Number Generators?

Credit card (CC) number generators are online tools or software programs designed to produce credit card numbers that follow standard numbering formats and algorithms. However, these generated numbers are not linked to any real financial account; they are valid in structure.

They follow industry standards, which include a specific issuer identification number (IIN), an individual account identification number, and a check digit calculated through the Luhn algorithm.

Who Can Use Credit Card Number Generator Tool?

  1. Developers and Testers: If you are involved in software development or e-commerce website creation, these tools are invaluable for testing payment gateways. These tools help to ensure that credit card processing systems work correctly without using real financial information.
    To learn in detail about fake credit card numbers for testing purposes, this article by testRigor presents an extensive guide on automating the testing of online fake credit card numbers.
  2. Educators and Students: In academic settings, these generators can teach the principles of e-commerce, digital security, and financial technology without exposing actual financial data.
  3. Quality Assurance Professionals: QA teams use these numbers to rigorously test the security of financial software/applications.

How do Credit Card Generators Work?

The mechanism behind credit card number generators is deeply rooted in the principles of mathematics and industry standards that dictate the structure of credit card numbers. Understanding this mechanism requires a look into the anatomy of a credit card number and the algorithm that ensures its validity.

Structure of a Credit Card Number

A credit card number typically consists of 16 digits, divided into sections with specific purposes:

  • Issuer Identification Number (IIN): The first six digits of the card number identify the issuing bank or organization. This is also known as the Bank Identification Number (BIN).
  • Account Number: The digits following the IIN up to the final digit represent the individual account number assigned by the card issuer.
  • Check Digit: The final digit is known as the check digit. Its purpose is to provide a way to verify the card number’s authenticity using the Luhn algorithm.

The Luhn Algorithm: Checks Validity

This algorithm is also known as the “modulus 10” or “mod 10” algorithm. It is the cornerstone of the mechanism behind credit card generators. Here’s how it works:

  1. Starting with the second-to-last digit and moving backward, double the value of every other digit. If doubling a number results in a two-digit number, add those two digits together to get a single-digit number.
  2. Add together the digits that were not doubled with the results of the doubled digits.
  3. The total sum obtained from step 2 should be a number that, when added to the check digit, results in a multiple of 10. If this condition is met, the credit card number is considered valid according to the Luhn Algorithm.

Luhn Algorithm Check Function (JavaScript):

function luhnCheck(cardNumber) {
  let total = 0;
  const numDigits = cardNumber.length;
  const parity = numDigits % 2;

  for (let i = 0; i < numDigits; i++) {
    let digit = parseInt(cardNumber[i], 10);

    if (i % 2 == parity) {
      digit *= 2;
      if (digit > 9) digit -= 9;

    total += digit;

  return total % 10 == 0;

// Example usage:
const cardNumber = "4123456789012349";

console.log(luhnCheck(cardNumber)); // This should log 'true' if the card number passes the Luhn check

Luhn Algorithm Check Function (Python):

# Given credit card number for Luhn check example
card_number = "4123456789012349"

# Function to check if the card number passes the Luhn Algorithm
def luhn_check(card_number):

  total = 0

  reverse_digits = card_number[::-1]

  for i in range(len(reverse_digits)):
    digit = int(reverse_digits[i])

    if i % 2 == 1: # Every other digit, starting from the right (0-based indexing)
      digit *= 2
      if digit > 9:
        digit -= 9

    total += digit

  return total % 10 == 0

# Perform Luhn check on the example card number
luhn_validity = luhn_check(card_number)

Steps for Generating Valid CC Numbers

Credit card number generators use the Luhn algorithm to produce numbers that can pass validation checks without being linked to actual financial accounts. Here are the steps:

  1. Selecting an IIN/BIN: The generator picks an issuer identification number appropriate for the type of card being generated (e.g., Visa, MasterCard). Each major card network has specific ranges of numbers assigned to them. These ranges are publicly known and are used to easily identify the card type from the number itself.

    Here’s a basic overview of the ranges for some of the most common card issuers:

    • Visa: Visa card numbers start with a 4. The IIN/BIN for Visa cards is typically six digits long, like all other card numbers and any number starting with four is recognized as a Visa card.
    • MasterCard: MasterCard numbers range from 51 to 55 or start with numbers in the range of 2221 to 2720, reflecting the network’s expansion of IINs. Thus, any card number that begins with digits within these ranges is identified as a MasterCard.
    • American Express (AmEx): American Express card numbers start with 34 or 37, distinguishing them easily from Visa and MasterCard.
    • Discover: Discover card numbers begin with 6011, 644-649, or 65. The range is broader for Discover, but like the others, the first few digits are unique to the Discover network.
  2. Creating the Account Number: The generator then randomly assigns the account number digits. In this process, it makes sure that the overall length of the number matches the standard for that card issuer (typically 16 digits, but can vary).
  3. Calculating the Check Digit: Using the partial number created from steps 1 and 2 (without the check digit), the generator reverses the Luhn Algorithm to calculate the check digit to make the entire number valid.

Fake Visa Card Number Generator: Example

Let’s break down a hypothetical Visa card number to understand each part of its structure. Remember, Visa card numbers start with a 4, and the standard length is 16 digits. A fictional Visa card number might look like this: 4123 4567 8901 2349.

  • First Digit (Major Industry Identifier – MII): The first digit is the MII, which identifies the category of the entity that issued the card. For Visa, the number 4 indicates it is a banking and financial card.
  • First Six Digits (Issuer Identification Number – IIN): The first six digits, including the MII, comprise the IIN (or BIN). For Visa, this could be any number starting with 4. Let’s say our example card has an IIN of 412345.
  • Next Nine Digits (Account Number): These digits uniquely identify the individual account. In our example, let’s use 678901234. This part of the number varies widely and is what differentiates one Visa card from another within the same bank.
  • Final Digit (Check Digit): The last digit is the check digit, which is calculated using the Luhn Algorithm to validate the credit card number. In our example, let’s say the check digit is 9.

Putting it all together, a breakdown of a hypothetical Visa card number would look like this:

  • MII: 4 (Visa and financial)
  • IIN/BIN: 412345 (Specific to the issuing bank or entity within Visa’s network)
  • Account Number: 678901234 (Unique to the cardholder)
  • Check Digit: 9 (Ensures the number is valid according to the Luhn Algorithm)

So, the complete card number in our example would be 4123 4567 8901 2349, which passes the Luhn check. This means that according to the Luhn Algorithm, which is used to validate various identification numbers, including credit card numbers, this card number is structurally valid. The algorithm ensures the number complies with a mathematical formula (listed above) to catch common errors, like mistyping a digit.

Remember, while this number passes the Luhn Algorithm’s check for structural validity, it is still a hypothetical example not associated with any actual financial account. It is entirely fictional and for illustrative purposes only. Real credit card numbers contain complex patterns and security features not captured in this simplified breakdown.

Fake MasterCard Card Number Generator: Example

Now, let’s break down an example of a MasterCard number. MasterCard numbers generally start with the numbers 51 through 55 or fall within the range 2221-2720, reflecting MasterCard’s expansion of its IIN/BIN ranges. The standard length of a MasterCard number is 16 digits, similar to a Visa. Here’s a fictional example of a MasterCard number: 5521 2345 6789 0123.

  • First Digit(s) (Major Industry Identifier – MII): The first digits indicate the card network and the industry. MasterCard’s numbers range from 51 to 55 for older cards and 2221 to 2720 for newer issuances, indicating a financial and banking institution.
  • First Six Digits (Issuer Identification Number – IIN or BIN): The first six digits include the MII and identify the issuing bank or entity. In our example, 552123 is the IIN/BIN, specifying MasterCard as the network and pointing to the issuing institution.
  • Next Nine Digits (Account Number): These digits, 456789012, in our example, uniquely identify the individual account holder’s account with the issuing bank. The bank assigns this portion, which can vary in length but typically ensures the total card number is 16 digits.
  • Final Digit (Check Digit): In this case, the last digit, 3, is the check digit. This digit is calculated using the Luhn Algorithm based on the rest of the number to verify its validity.

Let’s put it all together for this MasterCard example:

  • MII: 5 (Indicates a banking and financial card, part of the MasterCard network)
  • IIN/BIN: 552123 (Identifies the card as a MasterCard issued by a specific bank)
  • Account Number: 456789012 (Unique to the cardholder)
  • Check Digit: 3 (Validates the card number with the Luhn Algorithm)

For example, MasterCard number 5521 2345 6789 0123 does not pass the Luhn Algorithm’s check. This means that, according to the Luhn Algorithm, which is used to validate credit card numbers, this specific card number is structurally invalid.

American Express (AmEx) and Discover Cards Number

In this guide, we’ve provided detailed breakdowns of Visa and MasterCard numbers to illustrate their structure and the principles behind the Luhn Algorithm. While we have yet to cover American Express (AmEx) and Discover cards in the same detail, we can offer insights into these upon request.

If you’re interested in learning about the specific format and validation process for AmEx or Discover card numbers, or if you have any other questions related to credit card number generation and validation, please don’t hesitate to ask in the comments below.

Drawbacks of Using a Credit Card Number Generator

  • Misuse Potential: There is a risk of these tools being used for fraud, though numbers generated are not linked to real accounts.
  • Limited Functionality: Generated numbers can only be used for testing and cannot facilitate genuine transactions, limiting their practicality outside development and educational purposes.
  • Legal and Ethical Concerns: Misuse of generated numbers, even unintentionally, could lead to legal issues or ethical dilemmas if mishandled.

Final Words

Online fake credit card number generators are essential in software testing, education, and security testing. They offer a simulated environment for developers, educators, and testers to work with credit card numbers without exposing real financial data.

However, using these tools responsibly and being aware of their limitations and ethical considerations is essential.