Big Data Analytics Drive Big Growth in Prepaid Card Market

By Jeff Lewis

Prepaid cards are a hit with consumers, and BFSI Research forecasts the global market for them will reach $3.65 billion by 2022. Accordingly, prepaid card services are an increasingly popular offering for financial institutions, provided they can keep up with the steep data storage and analysis requirements. In the past, these requirements precluded smaller, regional banks from getting into the prepaid card market; the requisite IT staff, hardware setup and software licenses were too expensive for their smaller budgets. But this barrier to entry is lowering, thanks to the rise of integrated big data analysis platforms and open source software.

When we first entered the market, Sutton Bank, a $490 million bank in north central Ohio, was relying on a third-party vendor to provide the data warehousing, formatting and analysis needed to onboard new customers, spot fraudulent transactions and meet the bank’s regulatory requirements. It was a cumbersome, high-touch process that kept our prepaid card business from scaling to meet demand.

There were three specific areas where we wanted the bank’s data capabilities to improve:

  • The bank needed to increase the speed at which we onboarded new client data.
  • We had to step up our compliance game to improve fraud oversight and reporting to keep overhead low and the business profitable.
  • Our data analysis had to not only be fast but flexible enough to supply data in whatever format management required to make better decisions about pricing our services, properly staffing our IT team and identifying new regions and retailers that could benefit from our prepaid card services.

To meet these requirements, we decided to implement an in-house solution. There are many options available for a big data solution like this, but after conducting our research and due diligence, Sutton Bank selected the FinanSeer platform from DataSeers, an Atlanta-based company specializing in data solutions for the financial industry. Prior to our implementation of the big data platform, Sutton Bank was struggling to support its data warehousing needs. Between our use of manual processes for reporting data and reliance on external vendors, we were spending more time wrestling with data than we were servicing existing clients and landing new ones. It routinely took us two months to onboard a new customer. Now, the bank can do it in seven days, putting us in a much better position to attract new clients.

Our big data platform integrates open-source software from HPCC Systems to provide data integration and analysis capabilities. It is also supported by a worldwide community of developers, and DataSeers works with that team closely to fine tune the platform’s performance and develop new applications to better leverage data, all without the costly per seat licenses and support contracts required by proprietary big data solutions. For Sutton Bank, this allowed us to support a larger customer base, letting us compete with the major national banks. Better still, Sutton Bank can support customers nationwide, yet still provide the more personal customer service experience our clients expect from a regional bank.

Complying with regulatory reporting demands was also accelerated. For example, federal banking regulations require Sutton Bank to confirm customers are following established rules and processes for collecting consumer card transaction data. Previously, we performed our compliance analysis manually by analyzing hard copy spreadsheets provided by vendors, a process that could take weeks or months. Now that our partner compliance audits are automated by the big data platform, Sutton Bank can analyze and warehouse all of a customer’s data on our own in one night, giving us the ability to spot compliance issues and fraudulent transactions in near real-time before they become too costly.

For example, the bank’s compliance team discovered a customer was submitting transaction data that included suspiciously similar billing and email addresses; a red flag for possible fraud. Thanks to the speed of our big data platform, we were able to search a data set of over 400 million records to identify all of the suspicious transactions in less than 10 minutes.

The success of Sutton Bank’s big data implementation has presented one challenge for me and my team, albeit a cultural problem rather than a technical one. The biggest challenge was getting my management team to understand that they can get data however they want it almost instantly. They were surprised to learn they didn’t have to wait days to receive transaction reports, followed by hours combing through printed spreadsheets to try to determine what the data was telling them. Now, we can quickly iterate reports until the team gets the data they want, presented just the way they want it via online dashboards they can easily customize. It’s had a profound effect on our growth.

Since the adoption of the platform in April 2017, we have doubled our prepaid card business, and we estimate the bank is gaining 300,000 new prepaid consumer card users a month. Despite the booming growth, the bank has kept up with a rapidly expanding data set without the need for additional IT staff.

We expect to double our business again in 2018. Previously, to manage our customer base, we estimated one employee per 500,000 end-user accounts. With big data, that statistic is expected to improve tenfold. Our response time to solving problems flagged by the big data platform is so fast we’re now spotting and fixing problems with incoming transactions before our customers do. That wasn’t possible before we started using a big data platform, and it’s a key competitive differentiator for us.


Jeff Lewis is a senior management executive with 25 years of experience taking complex technology and related-services to market. He is currently senior vice president of prepaid services with Sutton Bank, headquartered in Attica, Ohio.

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