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  The Year of Going Granular
The Year of Going Granular

 

By Christina P. O’Neill

 

Data analytics, also known as ‘big data, was once accessible to only the largest banking institutions. But 2016 may be the tipping point in which lower costs and increased accessibility of data analytics will be increasingly adopted by small and midsized banks with assets in the hundreds of millions.

These community institutions have had to spend heavily on regulatory compliance in recent years, but now, their budgets are freeing up to invest in other things, industry observers said. The critical role of big data is to link banks’ internal data, such as customer accounts, credit scoring, payment history and assets, to external data, such as interest rates, macroeconomics and customer preferences.
Community banks are cautious on this score. They’re not only budget-conscious but also extremely protective of customer data, and often are reluctant to share it with third parties. However, the increasing improvement in ease of use of today’s data analytics holds promise to lighten the workload and increase the effectiveness of their compliance departments, which may have only one or two staffers whose knowledge of software as a service is usually not in the specialized range.
A 2014 report from Everest Group Research, “Analytics in Banking,” predicted that adoption of third-party analytics business services by banks would quadruple by 2020.
Last September, Boston-based Aite Group published two reports regarding payment analytics. A Sept. 16 report, “Beyond ROI: Better Metrics for Evaluating Commercial Banking Technologies,” asserted that while 30 percent of decision-makers relied on ROI, which most effectively measures cash flows with significant upfront investment, traditional financial metrics may not be the best yardstick to measure results versus ongoing costs. Instead, Aite Group recommended using annual deployment net earnings and annual deployment net earnings margin.
A Sept. 23 Aite Group report, “Payments Analytics: Gaining Insights and Creating Competitive Advantages,” recommends combining payment data with externally-obtained information to generate value-added services and meaningful competitive advantage.
A version of this practice had been utilized by researchers who used it to determine quality of life in Spain’s provinces using bank card data anonymized and provided by one of the country’s largest banks (see sidebar).
A Democratization of Analytics
Big data can help banks improve their compliance with anti-money laundering and know-your-customer requirements, fraud protection, FATCA, FCPA and FINRA rules. It can also enable banks to price their products effectively and to move away from mass marketing strategies that yield limited response rates, to smaller campaigns to fewer but more receptive customers.
Data analytics can also improve due diligence by presenting a better picture of performance and risk metrics, essential components of M&A decisions.
“I see a movement toward analytics used by businesses. In the past, you’d have to be a quant expert,” said Ed O’Brien, director of the banking channels practice at the research firm Mercator Advisory Group. Today’s big data is more accessible. Business users with some analytic background can now use many powerful tools to create a “what if” scenario without taking months to write code, he said. “It’s a democratization of analytics.”
Third-party core systems providers such as Fiserv, Fico and Jack Henry can help banks work with savings account and mobile-banking information; smaller institutions are more likely to partner with them now than was done even five years ago, O’Brien said – despite the ongoing cautiousness about privacy protection.
Adoption of big data use by community banks is still more evolutionary than revolutionary, indicated L. Cary Whaley, vice president, payments and technology policy at the Independent Community Bankers Association. Generally, because of their tight margins, community banks won’t take the lead in these methods.
“They see certain trends in the industry and [some] move fast to get there,” he said. “Half are fast followers, and half are wait and see. What we’re seeing in adoption right now, fast followers are looking at ways to use more analytics; the rest are waiting and seeing.”
The ease-of-use attraction of cloud- and software-based analytics is offset by banks’ caution about putting any vital data in a cloud environment. Those community banks that do use data are sharing product lines within their own institutions; very few are sharing it with a third-party provider, Whaley said. A small group shares data with affiliates in the realm of fraud prevention.

Data Analytics in the Field
In a 2013 report, “How Advanced Analytics Are Redefining Banking,” McKinsey director Toos Daruvala cited a large bank that brought in third-party data from external sources, increasing its predictive accuracy from the 40 percent to 45 percent range to the 70 percent range. Another bank purchased payment data from a local telephone company to more effectively determine who was a good credit risk. A third bank drew from social media to determine which products its customers would be most likely to buy.
Mercator’s O’Brien cited the use of customer checking account, automatic deposit and ACH information to map whether a customer has a new job or might be eligible for a higher-return money market fund. Customers might have had trusted advisors in previous generations, but now Millennials, in particular, are increasingly left to their own devices. This gives community banks the opportunity to follow the growth in their lives, and grow along with them.O’Brien said that while customers are interested in receiving good offers and discounts on rates and loans, and better returns on savings accounts, they want to know that their information is not shared with outside parties.
Banks want to reach out to those whom they believe might be inclined to engage with them. “There’s nothing worse than being approached when you don’t want to [be],” he said. “Hopefully, they only get reached out to when they want to and results are relevant to them.”
Banks can profile neighborhoods in terms of average income or investable assets, sending emails to residents in ZIP code-plus-four digits, targeting decisively without crossing over privacy lines, O’Brien said.
ICBA’s Whaley says the privacy issue is foremost for community banks. They take information in an aggregate form and use it to develop patterns and shapes, such as in the case of fraud analytics.
“You’re really not interested in the fact that a customer goes to [a particular] country,” he said. “The fact that they might have gone overseas may be significant, but only if it corresponds to a fraud trend.”
He cited the current priorities for community banks, including cybersecurity, data security and regulatory compliance. Then there’s cost containment, implementing mobile technology and preventing fraud. The third tier is enterprise risk management and customer profitability.
While he predicts that data analytics will become more of a way of doing business, “community banks are incredibly protective of their customers’ data. Having third parties misuse it or have it vulnerable, is a serious concern of community banks. … What you don’t see is combining internal data with external data, but I do think it’s something that could happen.”■


Posted on Tuesday, February 23, 2016 (Archive on Monday, May 23, 2016)
Posted by Scott  Contributed by Scott
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