Banks Face Cultural, Technical Hurdles to Create Analytics Services for Commercial Customers

by   |   February 22, 2013 5:03 pm   |   0 Comments

Large commercial banks have long been able to deliver cash flow reports for business customers, reports that demonstrate where their business capital is going—and where their revenue comes from, when it arrives, and other such details. But not everyone is convinced these reports are actually very useful. And while analytics and industry experts believe banks can improve their offerings, industry-watchers differ on the best ways for banks to step up their game to provide customers useful analytics services.

Banks’ business customers theoretically benefit from existing cash-flow reports in much the same way consumers look to outfits like to for their own finances: The bank’s reports provide a clearer picture of an entity’s financial situation, both past and present, and that information helps the customer make better financial decisions.

At the moment, Marc Andrews, vice president of IBM‘s global big data industry team, often hears banks affirm that, yes, they already provide cash-flow analysis for customers. But upon further inspection, there’s plenty of room for improvement. “Most of them still have a lot of work to do,” he said.

Related Stories

Sprint and N.Y. Stock Exchange look to convert analytics expertise into data products and services.

Read more»

MasterCard buys stake in Mu Sigma—and big data analytics.

Read more»

Dollar General turns 1010data data warehouse into service for analytics queries.

Read more»

Financial data reporting standard gets little used by Wall Street.

Read more»

For now, banks are likely to offer simple reports on cash flow, Andrews explained, but that doesn’t meet the more complicated needs of businesses. They need more fine-grained, real-time analysis to make smarter decisions about the future. Simple reports show historical trends, but lack the real-time information needed to compare those past moves to current events.

Banks could do a better job of helping customers know, for example, when they’ll have extra short-term cash they can invest, or to prevent cash-flow problems before they arise. It would also increase banks’ level of service and sales efficiency, Andrews said, because it would add more dimensions to that “360-degree view” of customers that all businesses crave.

Interest rates and exchange rates for currency trades represent additional opportunities for fresh insights.

It’s not that banks, especially big ones, are not doing this at all. Michael Versace, global research director at Framingham, Mass.-based consultant IDC Financial Insights, said he was much more sanguine than Andrews on the banking industry’s current capabilities. JP Morgan Chase, Citi, Bank of America—all those and more have been providing working capital analysis reports for the last five to seven years, Versace said.

But Versace agreed that these reports could be improved. Banks could provide additional details, such as reports on pending transactions or the company’s debt capacity, or they could show a business how it compares to its peers in certain categories, or give links to financial events, media reports or other market data. Importantly, they could be faster—providing information in real time, instead of the next day.

All of this, placed in CFOs’ hands, would help those executives make better decisions, he said.

Bank executives understand this, Versace said, and the industry is working to improve their offerings for a data-driven view of business.

John Carlson echoed that assertion. He is senior vice president of BITS, the technology division of the Financial Services Roundtable, an association of the 100 largest financial services companies in the U.S., and he said that talk of better data analysis has been stirring among these institutions.

It’s still early to spot trends in activity  among banks, Carlson said, but generally speaking, financial institutions are all interested in more sophisticated data analysis to help them better understand their customers. The primary goal at this point is to integrate their aggregated customer data with external information, such as market conditions, but do it in a way that still protects customers—their transactions and information must be safeguarded.

Experts cited three additional challenges the banking industry needs to address while the banks look to innovate:

1. Customer data security. Security concerns have long shaped the way banks collect data in the first place, Carlson said, as banks have traditionally stored customer information in closed, proprietary systems. Any changes in how data is collected, stored or used must be done with utmost care for the security of that data.

That emphasis on security is very much in keeping with the experiences of David O’Connell, senior analyst with Boston-based researcher Aite Group.  Commercial banks will jump on technology that provides better security and fraud detection, or that respond to regulatory requirements, he said. But analytics that might improve customers’ cash-flow results is one area that’s a tougher sell.

The benefits of these systems are hard to quantify, O’Connell said, and those security and regulatory concerns are simply of much more immediate import. Commercial bankers are a conservative lot, he added, in contrast to investment banks that may invest technology projects of all sorts.

2. Proprietary datasets. This type of data analysis has also historically been difficult to do, IBM’s Andrews said, even in large banks that theoretically have the resources to make it happen, because of the way data is currently gathered and stored in those closed back-end systems.

While banks could try to create linkages between those systems, that creates a classic big data challenge: With no common classification and storage system for data points, trying to create bridges between them is awkward and often ineffective. Only recently has it been possible to create a big data platform to pull all that information into a single environment.

3. Differentiated offerings for commercial customers. Souped-up reporting capabilities are only part of the potential for big data in banking, said Ambreesh Khanna, Oracle Financial Services’ vice president of product management. Khanna said he would describe cash-flow analysis as a commoditized product at this point, and he is skeptical that improved cash-flow analysis is really where banks should focus, since large companies already do very well in analyzing their own financial information.

Banks could, however, use cash management data as a jumping-off point to find more useful tidbits. Banks can see payment histories for individual transactions or the ZIP codes where the payment originated, for example. A drop-off or a spike in sales, for example, could trigger the bank to investigate possible reasons why, and that could dig up a slew of potentially valuable information for the business.

Say a construction supply retailer has a regular customer who suddenly makes several large purchases that don’t fit his usual purchase pattern, Khanna said. Alerted to this, the bank investigates and sees there’s a large construction project in progress nearby. The bank notifies the retailer, and the retailer uses that information as it sees fit – maybe to make special offers or reach out to its customers in the area.

Khanna estimates such services won’t be a reality for perhaps a year, but believes that technology providers such as his company can create and install systems that allow banks to do that kind of reporting and analysis on their own. The banks can then serve up this data to customers, making themselves more valuable and appreciated in the process.

Laura Schreier, a freelance writer based in Boston, can be reached at

Home page image of bank vault from Winona, Minn., Savings Bank by Wikipedia user Jonathunder.  

Tags: ,

Post a Comment

Your email is never published nor shared. Required fields are marked *

You may use these HTML tags and attributes: <a href="" title=""> <abbr title=""> <acronym title=""> <b> <blockquote cite=""> <cite> <code> <del datetime=""> <em> <i> <q cite=""> <s> <strike> <strong>