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RegexMagic   RegexMagic pattern for credit card numbers
Generate regular expressions that match valid credit card numbers with RegexMagic.
Don't have time to learn how to create a regular expression that matches valid credit card numbers? Get RegexMagic and use its Credit Card Number pattern to generate regular expressions that match valid credit card numbers according to your specifications in just a few clicks. You don't need to know or learn the regular expression syntax.

Finding or Verifying Credit Card Numbers

With a few simple regular expressions, you can easily verify whether your customer entered a valid credit card number on your order form. You can even determine the type of credit card being used. Each card issuer has its own range of card numbers, identified by the first 4 digits.

You can use a slightly different regular expression to find credit card numbers, or number sequences that might be credit card numbers, within larger documents. This can be very useful to prove in a security audit that you're not improperly exposing your clients' financial details.

We'll start with the order form.

Stripping Spaces and Dashes

The first step is to remove all non-digits from the card number entered by the customer. Physical credit cards have spaces within the card number to group the digits, making it easier for humans to read or type in. So your order form should accept card numbers with spaces or dashes in them.

To remove all non-digits from the card number, simply use the "replace all" function in your scripting language to search for the regex [^0-9]+ and replace it with nothing. If you only want to replace spaces and dashes, you could use [ -]+. If this regex looks odd, remember that in a character class, the hyphen is a literal when it occurs right before the closing bracket (or right after the opening bracket or negating caret).

If you're wondering what the plus is for: that's for performance. If the input has consecutive non-digits, e.g. 1===2, then the regex will match the three equals signs at once, and delete them in one replacement. Without the plus, three replacements would be required. In this case, the savings are only a few microseconds. But it's a good habit to keep regex efficiency in the back of your mind. Though the savings are minimal here, so is the effort of typing the extra plus.

Validating Credit Card Numbers on Your Order Form

Validating credit card numbers is the ideal job for regular expressions. They're just a sequence of 13 to 16 digits, with a few specific digits at the start that identify the card issuer. You can use the specific regular expressions below to alert customers when they try to use a kind of card you don't accept, or to route orders using different cards to different processors. All these regexes were taken from RegexBuddy's library.

If you just want to check whether the card number looks valid, without determining the brand, you can combine the above six regexes using alternation. A non-capturing group puts the anchors outside the alternation. Free-spacing allows for comments and for the regex to fit the width of this page.

^(?:4[0-9]{12}(?:[0-9]{3})?          # Visa
 |  5[1-5][0-9]{14}                  # MasterCard
 |  3[47][0-9]{13}                   # American Express
 |  3(?:0[0-5]|[68][0-9])[0-9]{11}   # Diners Club
 |  6(?:011|5[0-9]{2})[0-9]{12}      # Discover
 |  (?:2131|1800|35\d{3})\d{11}      # JCB

These regular expressions will easily catch numbers that are invalid because the customer entered too many or too few digits. They won't catch numbers with incorrect digits. For that, you need to follow the Luhn algorithm, which cannot be done with a regex. And of course, even if the number is mathematically valid, that doesn't mean a card with this number was issued or if there's money in the account. The benefit or the regular expression is that you can put it in a bit of JavaScript to instantly check for obvious errors, instead of making the customer wait 30 seconds for your credit card processor to fail the order. And if your card processor charges for failed transactions, you'll really want to implement both the regex and the Luhn validation.

Finding Credit Card Numbers in Documents

With two simple modifications, you could use any of the above regexes to find card numbers in larger documents. Simply replace the caret and dollar with a word boundary, e.g.: \b4[0-9]{12}(?:[0-9]{3})?\b.

If you're planning to search a large document server, a simpler regular expression will speed up the search. Unless your company uses 16-digit numbers for other purposes, you'll have few false positives. The regex \b\d{13,16}\b will find any sequence of 13 to 16 digits.

When searching a hard disk full of files, you can't strip out spaces and dashes first like you can when validating a single card number. To find card numbers with spaces or dashes in them, use \b(?:\d[ -]*?){13,16}\b. This regex allows any amount of spaces and dashes anywhere in the number. This is really the only way. Visa and MasterCard put digits in sets of 4, while Amex and Discover use groups of 4, 5 and 6 digits. People typing in the numbers may have different ideas yet.

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Regular Expressions Examples
Numeric Ranges
Floating Point Numbers
Email Addresses
Valid Dates
Credit Card Numbers
Matching Complete Lines
Deleting Duplicate Lines
Two Near Words
Catastrophic Backtracking
Making Everything Optional
Repeated Capturing Group
Mixing Unicode & 8-bit
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PowerGREP 4
PowerGREP PowerGREP is probably the most powerful regex-based text processing tool available today. A knowledge worker's Swiss army knife for searching through, extracting information from, and updating piles of files.
Use regular expressions to search through large numbers of text and binary files. Quickly find the files you are looking for, or extract the information you need. Look through just a handful of files or folders, or scan entire drives and network shares.
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Use regular expressions to rename files, copy files, or merge and split the contents of files. Work with plain text files, Unicode files, binary files, compressed files, and files in proprietary formats such as MS Office, OpenOffice, and PDF. Runs on Windows 2000, XP, Vista, 7, 8, and 8.1.
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