Transaction data on bank statements can be fairly useless if neither the bank nor the customer can understand what they say. Banking technology provider Nymbus and data analytics company Segmint are partnering to clean up that data for analytics and loyalty.
A bank customer making a transaction at a major retailer through a checking account or credit card typically shows up on statements as a series of codes and numbers, with possibly part of a merchant name in the middle.
"This makes it not easily recognizable and can cause unnecessary confusion with an account holder," said Nate Shahan, co-founder and chief product officer at Akron, Ohio-based Segmint Inc. "It results in poor customer experiences, unnecessary disputes and other problems."
As an example, Shahan said a payment to a Disney resort might turn up on an account statement as WDW-CRO-REF. After Segmint's merchant data cleansing process, that would be changed to Disney Vacation Club (Travel/Vacation Resorts).
"In addition to enriching the record with a human recognizable brand name, categories describing the merchant are produced along with brand logos and categories' icons," Shahan said.
The bank or credit union owns the enriched data and can decide what to do with it, including whether to share it.
"There is no obligation to share information back out to merchants," Shahan noted. "However, there is likely also considerable value to merchants with this data."
Banks may be reluctant to share the data with merchants, simply so as not to appear to be giving some type of competitive advantage to one merchant client over another. But that could change over time as companies like Nymbus and Segmint determine how to possibly pare data down in a way to serve specific merchants wanting to analyze the data.
The Nymbus-Segmint partnership comes at a time when the traditional banking model is being transformed as consumers demand contactless payments, digital platforms and personalized experiences. Bank statements have gone digital, but they haven't necessarily been modernized.
"It's been a little bit of an ongoing challenge because the data printed on that statement is not customer friendly," said Tiffani Montez, senior analyst with Aite Group's retail banking and payments practice. "From a bank executive perspective, you have to learn what each one of those codes and short descriptions mean, which means the way the data exists today it is not actionable."
Because financial institutions have so much payment data about consumers, they have to improve at discerning what each transaction means, Montez said. "It is a challenge for institutions to have more than an enterprise view of a customer relationship across products and channels."
In looking to minimize that challenge, Segmint and Miami-based Nymbus view the data cleansing as a way for financial institutions to better understand their customers' lifestyles, financial literacy, banking behaviors and common spending categories.
"The data available to a financial institution about a payment is limited in fields and quality," Segmint's Shahan said. "The bank or credit union core is the last stop for this data and by the time they see it, it is riddled with manipulation and truncation. For this reason, Segmint's artificial intelligence requires only cryptic payment/transaction descriptions to provide enriched value."
Banks that find a way to analyze customer payment data more clearly will pick up on various aspects of the customer that would indicate life changes and actual needs. With that knowledge at hand, the bank can provide product offers that help the customer address those needs.
But financial institutions have to be careful about how they use the data and whether the customer is actually benefiting or being pestered, Aite's Montez said.
"If you show up at the right place at the right time with the right offer for something the customer really needs, and they see value in that, then you are a hero," Montez said. "If you fail at any of those things, then you are creepy … there is no other way to put it."
The best result is for data-cleansing companies to create a win-win experience for the financial institution and the merchant or customer, she added. "It becomes about how you add value for everyone in those parties, and what are the needs and how do you develop that value."
