Payment Gateways II: Credit Card BINs and Card Intelligence

Gateways 102

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on Aug15

The purpose of this article is to familiarize the key merchant services industry players with the concepts of credit card BINs (bank identification numbers) and card intelligence as advanced features to be considered during payment gateway selection. If this is the first time you are reading “Payment Gateways II” series, please, start with the Introduction as it will improve your understanding of the current post.

Card intelligence concept is based on detecting certain patterns (what card types are used, where they come from) based on analysis of processed card numbers. First 6 to 9 digits of a credit/debit card number contain considerable amount of information about card type and its origin (detailed information on issuer identification numbers can be found here ). For example, the first digit usually identifies the association (Visa, Amex, etc), next digits define the card type (debit/credit/gift), country of issue, issuing bank etc.

The concept is fairly new, but it is rapidly gaining popularity among merchants and processors. Payment gateways utilizing credit card BINs are capable of providing additional analytical data to their customers (merchants). Examples of such data are given below.

Why credit card BINs are important

An important card intelligence aspect is differentiation of various card types (credit/debit/pre-paid/gift cards), because in some cases debit cards can be processed in a special way (PIN-debit or PIN-less debit), while pre-paid and gift cards are not a preferred option in the context of recurring billing agreements.

BIN also allows merchants to predict the cost of transaction. For example, if desired, a merchant can exclude (stop processing) reward cards that carry cash back and have high processing cost, or foreign-issued cards.

Credit card BINs can also be used to determine whether the card can be used in specific types of transactions (for example, HSA (healthcare) cards, fleet cards, purchase cards and level-III cards).

BIN also identifies the issuing bank, its location and contact data, enabling merchants to research problems around specific transactions.

Based on credit card BIN data, declines can be analyzed with respect to card types or issuing banks; respective patterns can be detected and measures taken.

In general, credit card BIN data analysis and patterns derived through it might help merchants make informed decisions in the area of credit card transaction processing. Illustrative examples of such patterns and decisions are provided below.

Examples

A merchant gets a certain number of declines during recurring billing, but, due to lack of card intelligence, it is unable to detect a potential pattern in declines. A lot of declines might come from a specific issuing bank. In this case the merchant might try to resolve the problem with the issuing bank.
As mentioned above, an important card intelligence aspect is the ability to identify a gift or pre-paid card during recurring billing. For instance, a health club customer is using a pre-paid card to buy a recurring contract for a year. If the amount on the card is relatively small, then there is a high likelihood of decline after it is consumed.
If some card type (credit/debit/gift or pre-paid) prevails in transactions, it might be appropriate to switch to a processor offering “cost plus” pricing model.
If many clients are using foreign-issued cards, the merchant/reseller might be able to conclude that the business has many customers from abroad (Canada/Europe), and may consider opening a foreign merchant account to reduce the costs.

Conclusion

A payment gateway/processor must be able to deliver the information about a specific card based on the first 9 digits of its number. Using this information, the business management will be able to make intelligent decisions.

The next post will cover aggregation models used by large-size merchants, wholesale resellers and PSPs.

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