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Why a Merchant Should Be Using BIN Data
Stop playing the "blame game" with payments. When authorization rates drop by two percent, the instinct is to blame the risk model or the processor. It's neither.
The solution is in the data you're already capturing. Look no further than the first eight digits of every credit card number on file: Bank Identification Number (BIN) data.
For large merchants with high transaction volumes, BIN data is one of the most underutilized levers available to impact authorization rates and capture more revenue. BIN data can sharpen fraud detection, optimize routing decisions, reduce interchange costs, and improve the cardholder experience without a major infrastructure overhaul.
What is BIN Data?
BIN data refers to the first six or eight digits of a credit or debit card number. These digits are not random. This identifier lets you determine the card type (Visa, Mastercard, American Express) and the card issuer (Chase, Bank of America, Wells Fargo).
Not all BIN data is created equal. The depth of information available depends on the level of enrichment your payment infrastructure supports. There are two tiers of BIN details: Standard and Enhanced.
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Standard BIN details provide essential information about the payment card, such as the card network, card type, and issuing country. This is great for gaining insights into your payments ecosystem and helps you make informed decisions during payment optimization or fraud detection.
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Enhanced BIN details provide more information about your customers, their accounts, and the associated costs of processing payments.
Basis Theory supports both.
How Merchants are Using BIN Data
Merchants and fintechs use BIN details to improve their business in a number of ways. Fraud detection is the most established use case for BIN data, and for good reason.
Common fraud patterns BIN data can help surface:
- A domestic billing address paired with a card issued in a high-risk country is a common indicator of card testing or account takeover fraud.
- Prepaid cards on high-value purchases carry elevated chargeback risk. BIN data identifies prepaid status immediately before the transaction is approved.
- Cards with category restrictions (gambling restrictions on a gaming platform) can be flagged before authorization fails, avoiding processor fees.
- A mismatch between the card issuing country and the transaction currency can signal a cross-border fraud attempt.
This information can provide more informative error messages to your customers and deliver exceptional support to customers experiencing payment issues.
Other use cases for BIN data:
- Risk Management: Assess the risk of each transaction and make informed decisions about whether to approve or decline the transaction. This helps to protect merchants from fraudulent transactions and to reduce their chargeback rate.
- Payment Processing: Choose the best payment processor based on estimated processing costs and avoid transactions on cards that cannot be supported. This analysis can help reduce processing costs and improve the overall payment processing experience.
BIN Data for Authorization Rate Optimization
BIN data can directly impact authorization rates. By understanding the characteristics of the card being used for payment, a merchant can make a more informed routing decision to capture more of the payments they want. And the successful authorization can happen behind the scenes, without changing anything visible to the customer.
For subscription businesses and retailers with stored payment credentials, BIN-informed routing can be the difference between a successful renewal and a churned customer.
Other examples of BIN data impacting authorization rates would include:
- Routing international cards to a payment service provider (PSP) with stronger cross-border approval rates rather than a single primary processor.
- Flagging 3DS-eligible cards where required in regulatory markets.
- Identifying cards eligible for Network Tokens or Account Updater.
Knowing which PSP handles specific card types best—and routing them accordingly—turns into an advantage that compounds month over month.
Level 2 and Level 3 BIN Data
Level 2 and Level 3 BIN data is one of the most overlooked—and one of the most financially significant things BIN data can reveal.
For large merchants processing a high volume of card types—corporate, purchasing, or fleet cards— these qualify for reduced interchange rates when additional transaction details are submitted alongside the charge. That detail is called Level 2 or Level 3 data, and it includes line-item information, tax amounts, and purchase order references.
The catch is that you can only submit Level 2 or Level 3 BIN data on cards that are eligible. Submitting it on ineligible cards will waste engineering effort. Skipping it on eligible cards means paying a higher interchange rate.
BIN data tells the merchant, at the moment of capture, whether the card qualifies. This then tells your system to collect and automatically pass the right details to lower rates. Across thousands of transactions, this can add up to real dollars—without changing the checkout experience.
Unlocking the Power of BIN Details
The most effective way to operationalize BIN data is to keep it natively in the same place where card data is stored. With Basis Theory, that would be the payment vault. When BIN enrichment is part of the tokenization layer, every card captured generates a set of properties available for any downstream use, without additional API calls.
Basis Theory appends BIN data directly to the token at capture time. When that token is referenced for a transaction, routing decision, or fraud check, the BIN attributes travel with it.
This is not a future capability. Merchants today are using it to make smarter routing decisions, catching fraud earlier, and lowering transaction costs. Oftentimes, without any checkout flow changes.
Put your card data to work. Schedule a demo with our team to discuss your use case and see exactly what your card data is telling you—and what to do with it.
