You’ve already learned the importance of BIN list management and recent changes to BINs overall. We have also explored the types of BIN analysis that Parrot enables. BIN list management can be a complex topic, and that complexity goes beyond mere numbers.
On November 1, Parrot by Pagos will be updated with the ability to address one of the barriers to simpler BIN analysis: inconsistent bank names. Banks tag their BINs with non-numerical names to list certain attributes more clearly in their database. For example, adding in regions, card type, or internal team ownership. However, this makes aggregating payment data across BINs of the same bank extremely cumbersome. In fact, we know many merchants that have to frequently reconcile bank names manually in order to conduct their standard BIN analysis.
Each bank can have many BINs, all with distinct names. Let’s take a look at an example.
|
BIN |
Bank Name |
|
400127 |
JPMORGAN CHASE BANK, N.A. |
|
401135 |
JPMorgan Chase Bank N.A. |
|
405607 |
JPMorgan Chase Bank N.A. Commercial |
|
405954 |
JPMorgan Chase Bank N.A. Debit |
|
410499 |
JPMorgan Chase Bank N.A. Prepaid Debit |
In the example above, you can see that the attributes listed in the bank names could be helpful identifiers for JP Morgan Chase. JP Morgan Chase likely uses tags like “Debit” or “Commercial” for BIN maintenance. You’ll also notice that some of the bank names are nearly identical, but just use different abbreviations or punctuation. This adds unnecessary challenge in conducting a simple, consistent BIN analysis.
If you were to import a BIN table and not take time to conduct aggregation, you would have this BIN represented twice within your data. If you did not complete the time-consuming manual aggregation yourself, you would have inaccurate groupings of customer information.
Now, Pagos can do the aggregation work for you in order to unify the BIN bank name taxonomy and display BIN data by bank. Our Clean Bank Name release is an automatic matching system that groups similar bank names together and labels them with a Pagos-created simple, consistent “Clean Bank Names”. Building on our example from above, the Pagos Clean Bank Name would look something like this:
|
BIN |
Clean Bank Name |
Bank Name |
|
400127 |
JPMorgan Chase |
JPMORGAN CHASE BANK, N.A. |
|
401135 |
JPMorgan Chase |
JPMorgan Chase Bank N.A. |
|
405607 |
JPMorgan Chase |
JPMorgan Chase Bank N.A. Commercial |
|
405954 |
JPMorgan Chase |
JPMorgan Chase Bank N.A. Debit |
|
410499 |
JPMorgan Chase |
JPMorgan Chase Bank N.A. Prepaid Debit |
This update reduces manual data cleansing time required from your payments or data science teams. Ultimately, it means that you can get to the real value of BIN analysis faster than ever before. Once you are able to meaningfully group the data of the same BINs from the same bank together, you are well on your way to analyzing the insights that up-to-date BINs afford: performance of card types, customer experience, decline analysis and more.
We can’t wait to continue improving the efficiency and accuracy of your payments data analysis! Stay tuned for the Parrot Clean Names release on November 1. If you don’t already use the Parrot BIN service, check out why Parrot is a better BIN service and sign up today.
