Big Data, Big Profits?

BigDataProfits_770

By Karen Epper Hoffman

Data analytics begins opening new doors to community bank earnings

As with many resources, information is as valuable as you make it.

Community banks are sitting on a wealth of potentially profitable information about their customers, technology experts have said for years. That information trove includes the number of customer accounts, their balances and transaction activity, they say. It includes their products, services and the channels their customers favor, as well as other potentially vital demographic, geographic and personal data.

However, with data analytics still a fledgling technology arena, community banks “have been using their data very little, and in a very basic way,” contends Peter Wannemacher, senior analyst with Forrester Research, a technology consulting firm in Cambridge, Mass. “These banks did not grow up with a lot of incentive to go beyond basic measures of profitability. And some banks see a risk in analyzing for the sake of analysis. It’s been a mix of culture and perceived cost.”

Jim Trautwein, senior director at the technology management consulting firm Cornerstone Advisors Inc. in Scottsdale, Ariz., agrees that in general community banks with $3 billion or less in assets may be collecting card data and some basic information on lending and account opening. “But it’s really been fragmented,” he says. “They might have good directional data about profitability, but there are usually so many gaps that [banks] are not getting a clear picture, especially with online transactions.”

Indeed, until recently, most community banks haven’t used much data analytic functions, sometimes called business intelligence, to better determine how they could become more profitable. But that’s changing.

“Community banks and their core system providers have had difficulty in the past connecting all of the components of non-interest income to the customer accounts generating this income,” says Jeff Morris, managing director and principal at Austin Associates LLC, a technology consulting firm in Toledo, Ohio. A good example is the interchange income related to the growing use of debit cards, he points out. “Now, through their profitability database systems, community banks are appropriately reflecting this income for each customer, branch and
product profitability.”

However, many core systems vendors and third parties now offer new stand-alone systems with data analytical capabilities or have added analytical modules to longstanding systems. Software now can help community banks manage data for customer sales and tracking, lending, various product and services, as well as product pricing and profitability. Other expanded data sources include enhanced customer demographics that, for example, may be collected through mobile apps and electronic banking channels, beginning with basic items such as email addresses, mobile phone numbers and personal preferences for communication, and shopping habits, Morris says.

Change drivers

Larger community banks in particular are starting to see greater opportunities in analyzing their data to better understand which people and products are making them money, and which are costing them, says Tom Frale, director of business development for RLR Management Consulting, a technology consulting firm in Palm Desert, Calif. But in many cases, industry insiders like Frale point out, there are basic steps before a bank can profitably begin to analyze their data.

“The vast majority of community banks have a lot of data,” he says, “but there’s no structured process in place to transfer it, parse the data out and manipulate it in order to analyze it.”

Jon Nordhausen, vice president for product strategy for Bank Intelligence Solutions in Norcross, Ga., a division of the technology services company Fiserv Inc., says community banks are “getting almost too much data,” which, for too long, have not been integrated in a way to make them actionable. “Getting there, that’s the hardest step,” he says. “It sounds easy, but it’s not.”

As community banks recruit executives from larger financial institutions who are accustomed to using the newest analytic software systems, Trautwein says, “they are starting to set the bar higher on themselves and their teams.”

Another driver for community banks is the continued rise of the electronic delivery channels, as Wannemacher points out. “Fifty years ago, even 15, a community bank could tell a lot about its customers’ preferences by what they saw in their branches,” he says. “It’s harder to ‘see’ digital behavior.”

Paul Schaus, president and CEO of CCG Catalyst Consulting Group, a consulting firm in Phoenix, Ariz., says the first step for community banks drowning in their own data is to take a breath and determine what they’re trying to accomplish. Bankers should consider what they want to first know about their customers and their products or channels, and then potentially what external data sources (outside the bank) they want to look to for comparison. Knowing what exactly makes customers profitable or not is an important first step.

As a best practice, Morris recommends moving toward creating a “multidimensional profitability system that provides all available views on profitability—customer, product, branch, household, officer, line of business, department—wherein all views reconcile directly to the bank’s overall financial statements, and each view reconciles to each other view.” To accomplish this, community banks may need to refine their Customer Information Files, which captures everything a bank knows about each customer, he says.

“When new accounts are opened, too little time and attention are given to understanding who this new customer is and what other accounts they and their related family members may already hold with the bank,” Morris offers.

Getting started

Because community banks typically rely heavily on their core banking vendors, having their support with their data analysis efforts can make a huge difference. Industry consultants maintain that although core vendors sell data analysis tools, they have not always provided the support in formatting the data or helping banks make the most of their analysis. Also, financial technology startups and third parties have great tools, but the community banks do not always have the technical and financial resources for them, Schaus says.

But according to Frale, all the major core banking service providers are developing analytical functions as the demand for better profitability monitoring grows. “They see that if banks can do a better job of using the data they have, it’s a win-win across the board,” he says.

Nordhausen says that the early adopters he has seen are typically putting in “the disciplined processes to understand the whole customer, pulling together data from their digital banking and other channels to get a broader view.” While they rely on their vendors, many of these banks, he says, are hiring their own staff with backgrounds in statistical analysis.

Nordhausen adds that community banks are increasingly seeking to fill their need for internal data analytics know-how. At the same time, he encourages banks to look first and foremost at the transactional and demographic data on their

“Fifty years ago, even 15, a community bank could tell a lot about its customers’ preferences by what they saw in their branches. It’s harder to ‘see’ digital behavior.”
—Peter Wannemacher, technology consultant

customers, to ensure the best customers are identified appropriately.

Through analysis of customer profitability, community banks can encourage desired behavior and cultivate stronger customer ties. They also can restructure product offerings to meet the needs of specific groups, including millennials and baby boomers, which will in turn make serving them more profitable, Morris says. To achieve this, he says, it is critical for banks to determine their funds transfer pricing of loans and deposits, and assign a cost of funding to loan accounts and a credit for funding to deposit accounts to ascertain the net interest margin level of profitability for the customer’s relationship.

In addition, Morris suggests capturing profitability calculations on all of the fee-based income customers’ accounts, factoring in the potential credit costs related to the customers’ loan accounts.

Ultimately, Frale underscores that data analysis is not a “set and forget” situation; customer preferences change and banks need to adopt a continuous process of profitability analysis. “The tools are there for the most part,” he says. “What is missing is the development of the process to take advantage of it, and the dedication to stick with it. Most banks will be gung-ho for six months and then the interest drops off.”


Karen Epper Hoffman is a financial writer in Washington state.

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