Harmonizing Issuing Bank Names in Your Payments Data

You want to use your payments data to identify opportunities for doing better. Regardless of how you define “better”—processing more efficiently, bringing in more revenue, cutting the cost of payment acceptance, or perhaps all three—you can’t hope to understand and improve your payments stack without aggregated and harmonized payments data. In other words, to perform a comprehensive payments analysis, you need a single dataset containing all your payments activity from across your entire setup. 

Unfortunately, it’s a difficult and time-consuming task to combine data from multiple sources into a standard format for analysis. That’s why we built Pagos! Using our no-code data connections or Data Ingestion API, you can send all your payments data to us and we’ll take care of the rest; we’ll clean the data, harmonize it together, and even augment it with information from our BIN service. 

We’re always looking for ways to improve our data harmonization processes and provide you with the most actionable data possible. For example, we just upgraded our data visualization platform to use the same standardization of bank names across payment processors and networks that we use in our BIN service. This change, known internally as “Clean Bank Names,” makes it easier than ever to analyze your payments data and uncover trends tied to specific banks. With this feature fully implemented in our platform, you can spend less time wrestling with messy data and more time acting on critical insights! Let’s dig into why this matters.

Why Clean Bank Names?

If you’ve attempted to harmonize your payments data in the past, you will have noticed how much variation there can be in issuing bank names. For example, cards issued by Chase Bank might appear as any of the following:

• Chase

• Chase Bank Usa N.A. 

• Chase Manhattan Bank

• Chase Manhattan Bank Usa, N.A.

• Chase Card Services Canada

• Chase Paymentech Europe Ltd

Why all the different names? For one, every payment processor labels issuing banks a little differently, using their own formatting style and punctuation. So if you’re harmonizing data across multiple processors, you’re bound to encounter this variety. Additionally, when the same bank issues different card products or on multiple networks, they often do so under slightly different names, sometimes identifying details like issuing country, card product, or card type in the name itself. While it is important to retain these details for your analysis, you don’t need the specific issuer name to learn them; those same card details appear in other fields associated with the issued card’s BIN.

Where You’ll See Clean Bank Names

Our efforts to clean up issuer bank names will make your data deep-dives within our data visualization platform easier than ever. Before we brought that platform to parity with the clean bank names in our BIN service, an analysis of all transactions made with cards issued by Chase Bank would require you to filter your data by issuing bank and select every possible variation of Chase. This fragmented data made it difficult to filter, segment, and compare transactions (or other payment events like chargebacks, refunds, and declines) by issuing bank across multiple data sources and networks. With Clean Bank Names, we’ve unified these variations under a single, consistent label. Now, when you search for “Chase Bank” in the Issuing Bank filter, only one option appears.

This improvement is reflected throughout our payments data visualization platform, wherever data is organized by issuing bank. For example, you can use the Issuing Bank bar list in your Metrics pages to break down your data by a specific issuing bank; click on a single bank name to filter the entire page. From there, you can drill down into card types, issuing countries, multi-processor behavior, and more.

Unlocking New Insights with Issuer Data

Viewing your data broken down by clean issuing bank names doesn’t just save you time—it also opens the door to deeper insights! We recommend analyzing the following key data for each individual issuer:

• Approval Rate: Compare approval rates across issuers to identify any underperforming banks. This can help you refine routing strategies or processor configurations.

• Customer Mix: Identify the breakdown of card types and card products for each issuer as a means of better understanding customers and their preferred payment methods.

• Chargeback Trends: Pinpoint issuers with unusually high chargeback rates. From there, you can protect your bottom line by creating fraud rules that block or limit transactions made with cards issued from specific banks.

• Fraud Declines: Some issuers may be more vulnerable to specific types of fraud. By isolating data from those banks, you can develop targeted fraud prevention strategies.

A Cleaner, More Efficient Payments Analysis

The Clean Bank Names project is part of our broader mission to take the complexity out of payments data. By streamlining issuing bank information, we’re helping you save time and focus on what matters most: driving better payment outcomes that increase revenue and reduce costs. With harmonized data at your fingertips, you’re better equipped than ever to unlock the full potential of your payments ecosystem.

Contact us to get started with Pagos today!