Mitigating Risk with Card Transaction Data 101
How risk teams use card transaction data to improve underwriting, set credit limits, and streamline pre-approvals for small business portfolios.
Eight in 10 Americans report having at least one credit card, and there were more than 511 million active consumer credit cards in the United States in Q1 2020. The pandemic accelerated movement away from cash: according to McKinsey, by the end of 2020, U.S. consumers used cash for just 28% of transactions, compared to 51% a decade prior.
For risk leaders serving small businesses, this increasing adoption of cards — and the data and intelligence they generate — unlocks new opportunities for mitigating risk and monitoring growth across your small business portfolio.
What Is Card Transaction Data?
“Card transaction data” typically refers to data generated when a credit or debit card is used to purchase goods or services from a business. To protect privacy, individual cardholders are anonymized and transactions are aggregated.
Card transaction data can include more than just consumer credit cards. The data can be derived from all kinds of cards, including debit cards, small business cards, corporate cards, government benefit cards, and charge cards. It can also include digital transactions — also known as “card not present” transactions.
Where Does Card Transaction Data Come From?
Transaction data provided by data companies can come from a variety of sources. Data may come from a bank integration, or be aggregated by a card issuer, a credit card network, or a payment processor.
When working with transaction data, it’s crucial to understand what kind of source it comes from. Many sources skew toward certain groups of consumers, geographic areas, or types of transactions. Knowing the size of the sample and any biases in the data source enables you to better understand how to derive trustworthy insights.
Raw transaction data is notoriously difficult to analyze. In its raw form, the data is messy, inconsistent, and sometimes duplicative, requiring organization and cleanup at scale before it’s ready to use.
The Entity Resolution Problem
A real-world example: different payment processors refer to the same business — a coffee shop called Bodhi Leaf in Orange, California — as “Bodhi L,” “Bodhi Leaf Coffee,” “Bodhi Leaf Coffee Traders,” “Bodhi Leaf Trading Company,” and “Bodhi Leaf Tradi.”
Uniting this data into a holistic view of transactions at a business level requires sophisticated algorithms and entity resolution techniques to clean and match the data. Enigma’s dataset aggregates and matches raw transactions to more than 10 million U.S. businesses.
How Risk Teams Use Card Transaction Data
Historically, card transaction data was used as a bellwether for consumer trends. It’s been increasingly recognized that this same data provides valuable insights about the health of a business. Trends in card revenues, transaction volumes, and customer concentrations reveal whether a business is growing or declining. When aggregated by business, this data is often referred to as “merchant transaction data.”
Card revenue does not reflect all of a business’s revenue, but the accelerating shift away from cash makes it an increasingly reliable signal. Merchant transaction data is especially helpful in industries where a high proportion of transactions are made by card — retail shops, restaurants, and service providers in particular.
Three Ways Risk Teams Are Using This Data
Accelerate the underwriting process. Timely data on business revenue removes friction from underwriting — you can ask for less paperwork and have more confidence in signals of business health built from actual card transactions. This type of data has allowed organizations to increase underwriting approvals without increasing risk.
Set, monitor, and adjust credit limits. When you can monitor portfolios and proactively identify customer accounts eligible for more credit, you can increase credit lines to drive higher spending per account. Being able to identify higher-risk accounts earlier means you can pinpoint when to decrease credit lines and dial back risk.
Streamline the pre-approval process. Get a look at monthly revenues and transactions without asking — and waiting — for bank statements or application forms.
What to Consider When Selecting a Transaction Data Source
When evaluating a card transaction dataset, the right questions help you compare options and understand which dataset best suits your needs.
Latency. How fresh is the data? How frequently is it updated?
Coverage. How many cards are included in the panel? Is it just credit cards or debit cards as well? How many businesses are covered?
Panel bias. What is the scope of the panel? Is it just Visa or just Mastercard? Is it skewed toward certain geographies or income classes?
Permissioning. Some data providers require you to get permission from a business before accessing its transaction trends. Others — like Enigma — have already integrated privacy protection into their system so that you can immediately access data about any business.
The Data: What Enigma’s Merchant Transaction Signals Include
Enigma’s Merchant Transaction Signals are derived from a panel of more than 750 million anonymized credit and debit cards — across types like general purpose credit cards, consumer and small business debit, small business credit, health savings and flexible spending accounts, gift cards, and more. The data is matched to more than 10 million U.S. businesses and refreshed monthly.
| Data | Description |
|---|---|
| Card Revenues | Monthly revenue a business receives from credit and debit card transactions |
| Card Revenue Growth | How card revenue is trending over time, with seasonally adjusted and non-seasonally adjusted views |
| Card Transactions | Monthly number of credit and debit card transactions |
| Card Transactions Stability | Distribution of transactions over time — how many days, weeks, or months saw purchases |
| Customer Counts | Average number of daily customers based on card transactions |
| Card Refunds | Refunds issued to credit or debit cards, including ratio of refunds to total revenue |
For a full breakdown of every available field, see the Enigma Data Catalog.
Putting It to Work
Whether the market is challenging or conditions are favorable, monitoring your portfolio to reduce costs and find growth opportunities is a constant imperative. Risk teams that ground decisions in timely, accurate small business intelligence will be best positioned to protect and expand their small business portfolios.
GTM teams looking for a related perspective on using this data for prospecting can read the go-to-market guide to better prospecting with card transaction data.
Ready to see how Enigma’s card transaction data can improve your risk decisions? Get in touch.