The Critical Role of the Issuer: BIN Attributes and Analysis
In our last post we defined the various partners that are critical to the issuer in the card payment lifecycle. Now let’s talk about some of the data attributes of/from the issuer that you should be paying attention to plus a couple key considerations. I will also throw in examples of how some of these data attributes can be used.
Critical Data Attributes and Analysis
Relative to the issuer, one of the greatest sources of information for businesses is the bank identification number (BIN)—or issuer identification number (IIN)—of a card. The BIN is the first 6 (8 digits effective April 2022) and is the unique identifier of a card which defines its general characteristics. These characteristics, which I’ll refer to as data attributes, can be used for a wide range of data analysis and optimization opportunities. Primary data attributes include the following:
Issuing bank name
Category level (e.g. corporate, signature)
Card type (i.e. credit, debit, prepaid)
Prepaid - reloadable/non-reloadable
Account updater status (e.g. enabled/disabled)
You can utilize these attributes when analyzing and evaluating card transactions for a variety of opportunities including interchange and cost analysis, identifying and preventing fraud, defining marketing campaigns, or for transaction routing optimization. As a business leader these analyses are key to your contribution to your company’s objectives for revenue growth, cost savings, and customer satisfaction.
Using BIN Data
To start, let’s consider two basic use cases where BIN data can illuminate opportunities for optimization.
Card Type Handling
Payments using prepaid cards are at higher risk of leaving businesses in many industries without the ability to collect full payment. Consider the case of a rental service where damage is found after the customer has left the property or returned the rental item. A prepaid card can only be charged for as much value as has been put on it; this limits your ability to collect for damages beyond the initial amount charged.
To avoid this scenario, you could decide that your business will not accept prepaid card payments for services and use BIN data to identify a card type as prepaid in advance of completing the transaction, instead prompting the customer to provide a different form of payment.
Use BIN data to analyze for trends in declines by attributes such as issuing bank, card brand, card type, and issuer country to determine where optimization of your payment flows could improve customer satisfaction and improve revenue growth. For instance, if you notice your decline rate for a specific card brand is at 100%, contact your payment service provider (PSP) to see if there’s a configuration error on your account. If there’s a high—but not total—rate of decline for a card brand, you could dig into the data further and see if declines are coming from a particular issuing country to troubleshoot further. Check out the use case for analyzing declines by issuing country and guides for other integration approaches for more detailed steps.
Other Key Considerations
Other data attributes exist outside of BIN which can enable a greater depth of analysis: consider also looking at combinations of those attributes with BIN data. These could include whether the purchase was a one-time or subscription transaction, the AVS flag result, dollar value range of transactions, the product type, etc. For instance, maybe you need to dig further into your transaction data to see that transactions being declined didn’t include address verification service (AVS) information and you need to update your payment form to require that to reduce the rate of declines.
Are there external influences on your transactions that need/should be considered in analysis (e.g. fraud prevention, sales/marketing promotions, entertainment events, pandemics, natural disasters, etc.)? Let’s consider COVID-19 impacts. Looking at a couple studies as reference of the dozens out there (e.g. Digital Commerce 360 , trade.gov), it is clear that the pandemic boosted ecommerce sales for business-to-consumer (B2C) and business-to-business (B2B) in many markets. This demonstrates the importance of analyzing sales (e.g. refunds, chargebacks) by their BIN attributes, such as the issuing bank country. In this case, that data could be analyzed in relation to your merchant processing country to better understand consumer behaviour and potentially even the loyalty of your cross-border customers.
By collecting and analyzing the data associated with BIN attributes and factoring in the key considerations noted above, you’ll become more aware of the possibilities that can contribute to your company’s overall success. We will continue this exploration in future posts.
Pagos is here with tools offering advice and paths to action. We make it simple to understand BIN attributes with Parrot, visualize your payment data trends with Peacock, and get alerts via Canary when trends change. Toucan protects your customers’ payment information while increasing approval rates, and Loon reduces checkout friction and improves approval rates by keeping customer information fresh. Contact us to discuss your use case and needs further!