BIN Use Cases
With Parrot by Pagos, you have access to accurate, up-to-date BIN data for each payment card your business processes. At minimum, this BIN data gives you quick insight into the types of cards you’re processing and where they’re from. If you’re looking to get even more from your BIN data, or you just don’t know where to get started, we’ve got your back! The practical examples outlined below demonstrate how to further interpret BIN data and apply what you learn to improve your business.
Quick Review: What Are BINs?
BIN stands for bank identification number and is the first 8 digits of a primary account number (PAN). The BIN provides background information on the card, including information about the institutions associated with the card (issuer and brand) and the card product itself (card type, associated industry, etc). Parrot provides a single access point to BIN data from all networks. To learn more about the details of BINs, check out our blog post introducing Parrot.
Once you’re ready, read on to understand how to apply BIN data to your business.
Getting To Know Your Customers
BIN data includes the card product type (e.g. prepaid, debit, credit). Analyzing card products helps you fine-tune your risk rules, target your marketing efforts, and analyze your processing costs.
Fine-tune your risk rules: Incorporating card type details helps you identify card types that you may want to reject. For example, this could help you catch when a customer tries to use a non-reloadable prepaid card or a gift card for a subscription payment; in this situation, you may want to reject the payment for a more reliable recurring payment method or flag this for proactive research so you can measure conversion by card product types.
Target your marketing efforts: Card product information is a great addition to your customer demographic analysis, and card product qualification provides an indication of income level. With this data, you can segment customers according to their behavior or specific characteristics. For example, do you typically see higher average order value (AOV) on premium cards? If you don’t, you could target promotions to premium cardholders to engage them further with your brand.
Understand your processing costs: Card type—in combination with card brand, merchant category code (MCC), and transaction type—influences your processing costs. By conducting a baseline analysis on your costs based on the product types, you can keep track of changes to fees, including interchange.
Manage Payments Performance
A core component of BIN data is identifying the issuing bank associated with cards. Once you know the issuing bank, you can use this information to segment your payments data for further analysis.
Approval rate analysis: To identify the highest and lowest performing issuers in your cardholder portfolio, start by segmenting approval rate by issuer. Ultimately, this facilitates conversations on performance. For example, you would expect to see similar approval rate performance between similar banks; if this isn’t the case, you may be experiencing challenges with risk rules, transaction flags, or even processing configuration issues.
Decline code analysis: The converse to approval rates is, of course, decline rate. Decline code management is a key part of recapturing lost revenue and identifying any challenges with issuer-based declines. Read more about decline code management on the Pagos Blog!
Payments risk analysis: Combining issuer data with chargebacks tells you where you are likely to receive the most chargebacks within your cardholder portfolio. Not only does this enhance the risk scoring of your transactions, it’s also a great indicator of where you may need to focus on chargeback reduction.
Knowing this information prepares you for any conversations with your partners, processors, and issuers, and identifies areas where you could be a better partner yourself. Once engaged in a conversation with one of your partners, you can discuss opportunities to send better data on a transaction or enable more payments capabilities to increase the chance of an approval. For example, sending more transaction data (e.g. zip code or other demographics), enabling network tokenization, or even adjusting your retry strategy at the BIN level could help boost any performance challenges you found through BIN data analysis. This could lead to a sophistication of your payments ecosystem and better collaboration with your partners.
Common Pitfalls in BIN Analysis
BIN analysis is not always a walk in the park. Overlaps within BIN files and incorrect data sometimes invalidates BIN analysis. Adjusting to the 8-digit BIN change will help with any overlapping data issues, because it increases the granularity and improves the accuracy of your analysis.
Equally important, is to source your BIN data from a reputable source. Parrot has a direct connection to the networks themselves and provides BIN updates every single week. Are you ready to level up your BIN analysis?