When a customer makes a purchase using a card, card schemes—such as Mastercard, Visa, American Express, and others—play a vital role in managing the transaction process. However, understanding how these schemes work and identifying the specific scheme behind a card can be tricky, especially for those new to the Fintech space.

When I first entered the Fintech world, one of my biggest challenges was identifying Card BINs—the first digits on a debit, credit, or prepaid card. To solve this, I initially relied on manual processes and built SQL case statements to analyze card schemes. But I knew there had to be a better way to make this process more efficient.

That’s why I decided to create a solution that makes Card BIN identification quick and easy for data analysts, business analysts, and others in financial institutions. My solution leverages open-source data that I collated and harmonized using Python and data engineering skills, providing a single platform for identifying card schemes.


But what exactly is a Card BIN?

A Bank Identification Number (BIN) represents the first four to six digits on a credit card. These digits are crucial as they identify the financial institution that issued the card. BINs are an important security measure that protects consumers and merchants in online transactions. With the ability to quickly identify the card scheme, businesses can make more informed decisions, improve fraud prevention, and analyze card performance more effectively.

By using my Card BIN Identification Tool, anyone working with card data can now easily identify card schemes, whether it’s Mastercard, Visa, American Express, or others. This solution makes it easier to analyze transaction performance, streamline processes, and save valuable time.

Test it yourself! Enter the first 6 digits of your card (don’t worry, it’s safe! No data is stored), and see if the card scheme information is accurate. Currently, my dataset is centered around Nigeria, but I’m open to feedback and corrections to ensure the data is as reliable as possible.

Try it here

If you find this tool helpful, please repost this to share with others who could benefit from it! Let’s make card data analysis easier for everyone in the Fintech space.

Watch demo below