Designing an Informed BIN-Based Routing Strategy

Improve your overall approval rate by incorporating BIN data into your data-driven routing decisions.

Given the recent explosion in online payments, available payment methods, and payment routing options, companies have a lot to think about when it comes to processing transactions. One such challenge—and opportunity—is deciding which processor to send any given transaction to. While theoretically a payment sent to any processor should see the same result (e.g. success or failure), the complexity of the payments ecosystem and the sheer number of players involved means that’s not always the case. In reality, it’s easy for two processors to diverge in performance due to data quality, compliance updates, fraud rules, integration setup, and even customer mix changes.

One way to make data-driven routing decisions is with payment card Bank Identification Numbers (BINs). In the following use case, we’ll incorporate BIN data not just into a routing analysis, but into the executed strategy and ongoing monitoring of the results. Being data driven means you should baseline your performance in a normalized way, establish a hypothesis to test, and then track the performance of a given change.

Note: While this example was written using real data, all data and BINs have been changed or anonymized for the purposes of this story. You can also assume here that the transaction volume distributed across the two processors and BIN is sufficient for a comparison, and that other related metrics like chargeback rates and refund rates are sufficiently similar.

Why BINs?

When it comes to learning who your customers are, where they’re located, and what their preferences are, you can’t do much better than BIN data. Similarly, BINs allow you to divide your customer base up into logical segments for making informed decisions around processing, fraud analysis, tokenization, and more. In this example, we’ll use BINs as a segmentation tool for transaction routing.

To learn more about BINs, check out Unlocking the Power of BIN Data.

The BIN-Based Analysis

In this use case, we’ll put you in the role of the payments manager at a mid-sized business. Imagine your business processes transactions in the United States through two processors. Your current routing strategy involves the following breakdown of your transaction volume:

  • 25% through Processor A

  • 75% through Processor B

Your overall approval rate across all your card transaction volume is fair—around 86%—but you want to improve it to increase your revenue. You’re specifically interested in how you might be able to increase your overall approval rate by being more strategic with your transaction routing.

To better understand your approval rate (and how to improve it), you start a deep dive analysis with Pagos Insights. Because Pagos ingests all your data from both your processors and aggregates it together into a single, harmonized dataset, you can see all your transaction data in one place, broken down by whatever parameters matter to you most. For this particular investigation, you’re interested in seeing your approval rate broken down by BIN for both processors. In other words, you want to see if some BINs perform better with one payment processor over the other; if so, you can design routing rules to send transactions made with certain BIN ranges to the processor where it’s most likely to succeed!

For this analysis, you filter your data from the last six months to only see approval rates for transactions routed through Processor A, broken down by BIN. You do the same for transactions from Processor B.

The BIN-Based Strategy

Organized in descending order of transaction count, the top 5 BINs processed through both processors have some overlap, but with some glaring differences in approval rates. Let’s look at that sample data:

Processor A

Bin & Issuing Bank

Bin & Issuing Bank

Issuing Country

Issuing Country

Approval Rate

Approval Rate

3703 82

American Express

United States

95.50%

4147 20

Chase Bank

United States

97.43%

5176 04

Bank of America

United States

76.29%

6539 49

Discover

United States

87.43%

4400 66

Bank of America

United States

93.88%

Processor B

Bin & Issuing Bank

Bin & Issuing Bank

Issuing Country

Issuing Country

Approval Rate

Approval Rate

4147 20

Chase Bank

United States

73.28%

3703 82

American Express

United States

93.56%

4400 66

Bank of America

United States

76.22%

4266 84

Chase Bank

United States

78.90%

5178 05

Capital One

United States

62.31%

Immediately, you identify two BINs for which the approval rate via Processor A is significantly higher than Processor B:

  • BIN 414720

    • Processor A approval rate : 97.43%

    • Processor B approval rate : 73.28%


  • BIN 440066

    • Processor A approval rate : 93.88%

    • Processor B approval rate : 76.22%

Given these values, you have a real opportunity here for improving your overall card approval rate—and bottom line—by sending more transactions from both BINs through Processor A over Processor B. The next step after identifying this opportunity is to run a test. We recommend only pushing a portion of both BIN’s transactions to Processor A; this allows you to continue to compare transaction approval rates for those BINs across processors side-by-side to ensure the witnessed trend wasn’t a temporary occurrence.

Ongoing BIN-Based Monitoring

Next, you want to continuously monitor these BINs to dictate how your routing strategy evolves moving forward. As with all things in the payments world, changes will happen over time and some BINs that previously had higher success rates through one processor may suddenly do better through another.

Since Pagos also ingests your refund and chargeback data, you can even start researching potential root causes for how particular issuing banks and BINs perform over longer periods of time, and further modify your routing or processing strategy. Similarly, in instances where approval rates through Processor A and Processor B reach an equal level, you can use Pagos to compare the cost of processing certain BINs through each processor and make additional changes.

Now It’s Your Turn

Integrating BINs into any payments data analysis can unveil new strategies for enhancing approval rates and optimizing revenue. By leveraging BIN data, you can segment your customer base effectively, enabling you to make informed decisions around transaction routing, fraud rules, and more. If you’re looking for an easy, no-code solution for monitoring all your payments data in one place, look no further than Pagos. We have the payment intelligence and observability tools you need to optimize your processing strategies. Contact us to get started today!

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Pagos helps you achieve optimal payments performance.

Pagos helps you achieve optimal payments performance.