Data Enhancement Is a Precursor to Financial Innovation at Banks

Financial services companies are sitting on mountains of data, but they often don’t know what it says, much less what to do about it. As a result, illegible transaction descriptions confuse customers, inaccurate datasets fuel distrust across companies, and limited access to data stymies future innovation. It’s also all very expensive, with one estimate from Gartner Research finding that poor quality data costs an average of $15 million per year

By contrast, expanding the scope of available data and then enhancing it results in major benefits, such as powering your products and applications to improve customer lifetime value with personalized notifications, relevant marketing offers, insightful healthscores, customized reward programs, and alternative credit data that can widen your borrower pool. You can also reduce costs by lowering call center volume associated with perceived fraud and identifying recurring transactions to better understand cash flow, all while increasing your competitive advantage by pinpointing where your customers engage with your competitors based on product channel and spend.

Most importantly, data enhancement is a precursor to financial innovation at banks. Looking to offer artificial intelligence, machine learning, chatbots, automated financial assistants, or more? In every case, these innovations are only as effective as the data behind them. After all, what good is voice-enabled banking if the data behind it is unintelligible? Imagine a customer asking what their last transaction was only to be given a string of characters such as “CHEV-897yvo-Char.” Or imagine them asking for total gas expenses in the month of July only to have the voice-enabled bot return nothing to them because the transaction data isn’t categorized by expense type. 

In a range of cases, the innovations of the future hinge on data enhancement.

So, how do you move forward on this front?

First, it’s critical to access as much data as possible by aggregating from a variety of sources via tokenized, credential-free API connections. With these secure and reliable connections, users can decide what data they want to share with what organizations, while financial services companies get a 360-degree view of their customer’s financial lives, setting the stage to put the data to use.

Second, transaction data is collected into a data engine where each transaction is given a cleansed description, assigned a spending category, classified as a payment type (bill pay, point of sale, etc.), stacked with metadata (merchant name and logo location, interest rate, etc.), and then returned to the appropriate merchant.

Data can include:

• Cleansed Merchant Description
• Balance
• Available Balance
• Spending Category
• Location
• URL
• Interest Rate
• Credit Limit
• Time
• Date
• Subscription (Y/N)

Since there are vendors that sell a wide range of products, it’s also essential to optimize merchant-level data according to specific purchases. For example, a purchase at Amazon might be categorized as shopping, entertainment, groceries, or something else depending on what type of item was purchased — giving financial services companies and customers the clarity they need to act on their data.

, including details around our 119 spending categories and our industry-leading categorization accuracy of up to 95%.