Why Small Banks Need Big Data

By Bryan Yurcan

September 6, 2016

Attention community bankers: it is time to dip your toes in the sea of data if you're not doing so already.
More than ever, banking is as much an information business as it is a financial business. With the vast amount of data banks have on customers and transactions and spending habits, those that aren't effectively mining this data risk falling into irrelevance.
For some small banks, investing in data analytics remains a tough call. Margins are already thin from the operating
environment. Tech budgets are consumed with bridging the gap between running legacy technology and offering digital products. Perhaps rightly so, they are afraid to sink money into a solution without fully understanding the problem they are trying to solve.
But it is a jump they must take in order to maintain their edge with customers.
"The reason [small banks] need to budget for this is because bigger banks are absolutely budgeting for this," said Karan Bhalla, managing director at IQR Consulting, which provides analytics and statistical modeling consulting services. "The rate of data being gathered now is huge and knowing things like how often customers log in to mobile banking … is redefining what loyalty is. If you don't understand that, then some other institution will and will take away customers.

Impactful Insights

Data analytics has become the Swiss Army knife of banking.
From customer marketing to regulatory compliance, from fraud detection to cybersecurity, data plays an integral role in banks' daily operations.
Take, for instance, compliance functions. In the past, banks typically had one group of people working on anti-money-laundering compliance and other groups working on different other types
of fraud. But banks now are seeking an enterprise-wide view of risk, and investing in analytics to get it.
"If you can pull all the data together, and use things like predictive analytics or machine learning to identify hotspots and where there might be potential trouble, you can fight fraud more effectively" said David Wallace, global financial services marketing manager at analytics technology provider SAS.
Indeed, analytics tools for compliance purposes, risk management and fraud prevention are among the most popular, Wallace said.
"When it comes to fighting fraud and financial crimes, and risk management, ultimately they are regulatory compliance activities, so it's all tied together," Wallace said. "All these things are prescribed by regulators, but banks need to do them to protect customers."
Wallace added that analytics can help banks identify and combat breaches and strengthen cybersecurity. Often, if there is a data breach it might take an institution "many months to figure out what's going on," he said. Deployed in a real-time streaming environment, advanced analytics can look inside a bank's network and find anomalies that previously may have gone undetected for lengthy periods of time, Wallace claim ed. For example, such analytics could spot systems interacting in atypical ways, such as a customer service system accessing a function that supervises the general ledger, he said.

Putting the 'Big' in Big Data

It is perhaps difficult for community bank executives to see how they can effectively use data analytics when they see what some of the larger banks are doing.
Consider the $251 billion-asset State Street's Global Exchange, a unit the bank created in 2010 dedicated to data and analytics. It has 700 employees.
As a custodian bank, State Street's clients are constantly asking for access to more data to help them operate more effectively, said Jessica Donohue, chief innovation officer of the division. Especially for regulatory compliance, and things like anti-money laundering and know-your- customer efforts, data analytics are essential.
"Analytics helps us provide our clients with a clean and clear view of risk across a complex portfolio," Donohue said. "A large asset owner, or a client with a complex banking book, needs to be able to look at risk holistically across their entire portfolio or book of business."
Those are services the bank wouldn't be able to provide effectively without analytics.
"Data does not start out being easy to use; it comes from a lot of different sources and the amount of it is very large. You need good analytics tools to bring that all together and serve up usable data."
Community banks might find comfort in knowing that Donohue thinks the industry has only scratched the surface of how it can improve through analytics tools designed to make sense of a seemingly infinite amount of data.
"I feel we are still at the beginning of this space," she said. "You're seeing a lot of the attention focus now on machine learning. The technologies that are being developed allow you to work with much larger databases in much less time. That wasn't possible even five years ago."
And the proliferation of analytics technology in recent years makes it easier for even small banks to start down this path, said Bhalla,
"Analytics tools are constantly being refined to mine more and more data in a more effective manner," he said. "For smaller institutions, it's much easier now to take that first step and invest in something that will give you basic intelligence. Then you can go from there and look at ROI and decide if you want to invest more, or hire quants or whatever. But there are many tools that run the gamut of varying cost, so it has to be a part of your budget, even a small part."

Don't Forget the Human Touch

Analytics tools need to be combined with expert human analysis to be most effective, said Todd
Hammond, head of commercial underwriting at Cleveland-based KeyBank.
When making credit decisions, generally the complexity and size of the loan will dictate how much the bank will rely on analytics.
"Analytics definitely plays a huge role, but reliance on it depends on the complexity of the deal," Hammond said. "Certain small-business loans can be largely data-driven. When you move up in the commitment spectrum, we'll still use analytics but there's also a human element, such as our interactions with the client and understanding of their standing in the market."
Human reasoning is also needed to make sure Key doesn't get in trouble for who it lends to. "We definitely have people looking at reputational risk, of who we can and can't lend to," such
as legal marijuana businesses, he said.
Still, Hammond said financial institutions will continue to do more with analytics as data becomes more large and complex.
"We want to know as much about our clients so we can sell them the right products," he said. "And it's also effective for not only helping us make credit decisions, but for monitoring the effectiveness of our lending strategy."

High Stakes

For analytics to be the most effective, banks need a plan for how to use them, said David Tobin, principal of Mission Capital Advisors.
"There's a huge amount of data that can be mined. You have to decide what you are mining for," he said. "The first thing banks really need to do is to need to establish a plan of action. Such as, 'what data from a regulatory requirement perspective do we need? What data could we mine for marketing purposes?' Then you go from there and rank all the opportunities and figure out how much you can do and how much it will cost for the right analytics tools."
This is particularly important for smaller banks that likely do not have large technology budgets. But even if the bank determines it can only spend a small amount, that's better than doing nothing.
"The pitfalls of not investing in your future as a bank far outweigh any benefits you might get in money saved from not investing," Tobin said.

Big Data Alone Is Not Enough

AUGUST 19, 2016 | BY CARRIE ROSSENFELD

IRVINE, CA—Big data is not, by itself, inherently useful; information is only as valuable as its fusion with industry experience, Ten-X’s Sheridan Hitchens tells GlobeSt.com in this EXCLUSIVE roundtable about big data and the real estate industry.

IRVINE, CA—Big data is not, by itself, inherently useful; information is only as valuable as its fusion with industry experience, Ten-X’s VP of data products Sheridan Hitchens tells GlobeSt.com. We spoke
exclusively with Hitchens, along with Morgan Stewart, an attorney with Manly, Stewart &

Finaldi; Elliot Vermes, CEO of ResiModel; Joe Derhake, CEO of Partner Engineering & Science; Norm Miller, Hahn Chair of real estate finance at

the Burnham-Moores Center for

Real Estate, within the School of Business at the University of San Diego; and David Tobin,

founder of Mission Capital Advisors, about their overall assessment of big data in the real estate industry. Stay tuned for a more in-depth feature on the subject in the July/August issue of Real Estate Forum.

GlobeSt.com: What should our readers know about big data and real estate?

Hitchens: Big data is not, by itself, inherently useful. Information is only as valuable as its fusion

with industry experience. It’s not the data itself that provides value, but the application of that
data. In order to find actionable statistics, you need to analyze and curate specific insights from oftentimes oversized data sets.
At Ten-X, as an online marketplace, we’ve been able to utilize data to provide global-marketing reach, allowing for more tailored and targeted marketing on specific assets. The ability to appeal to a subset of buyers and sellers that, according to the data, may be more interested in a
specific asset helps us to create a more efficient marketplace for both buyers and sellers. In conjunction with a local investment broker, we ensure that potential buyers who are around the corner and around the world are covered.
Due to our position at point-of-sale, as well as our partnership with Google and leading industry data providers, we will be able to provide customers with real-time, actionable data. The value
of any data changes with time—a lease comp from three years ago probably isn’t very helpful
today. So if we can provide relevant, verified data at the exact moment someone is looking to transact, we will remove risk and add liquidity to the market.
Stewart: The attack on Essex serves as a warning to all real estate companies that collect and store information on behalf of renters, tenants, vendors, consultants and buyers. Multifamily property owners, management firms and related interests, however, are at particular risk due to the high-level of personal information, including banking, social security, driver’s license numbers and employment, collected on consumers during the rental process.
Property managers should also be aware that they fall under both FTC rules. Since property
managers provide a financial service for landlords they fall into the category of financial institution under the Safeguard Rule, and because they use credit reports to screen tenants, they are also subject to the Disposal Rule.

Vermes: One thing that goes hand-in-hand with big data is data visualization—charts and other graphic tools that make it much easier to grasp where things stand and when trends are in motion. Whether you’re analyzing rent revenues at your multifamily property or energy

expenses at a commercial building, the human mind is much more effective at understanding
information in a visual format rather than solely comprising a list of numbers.
In order to be able to analyze and visualize data, it first has to be in a usable format. While you may be bombarded by massive amounts of data, it is often useless without an enormous amount of time-consuming manual conversion from a yellow pad (or other non-digital
equivalents) into a standardized format. As compared with most sophisticated trillion-dollar industries, real estate has a surprisingly high number of “yellow pads.” In the multifamily space, people frequently receive rent-roll data in PDF or other difficult-to-analyze and database
formats. As a result, the data is often not analyzed as comprehensively as it could be, and those
who do analyze it face the arduous task of first getting the data in their own Excel templates prior to being able to analyze it. One of ResiModel’s most popular features is our data-capturing capability, which extracts the data out of the property management reports that you receive in PDF and Excel formats and converts it to a format that can instantly be analyzed and mined. Digitizing and standardizing information is the prerequisite to big data.

Derhake: It’s important to realize that big data creates a competitive advantage not only in terms of CRE investment and lending, but also for better-managed and -operated—and therefore more valuable–assets. Collecting data from smart building systems, tenant smart- phone usage, tenant management systems, etc., to gain a better understanding of how buildings are used allows property owners and operators to improve facility

management and capital planning. For example, building energy efficiency can be improved by adjusting building systems to more efficiently meet user demand. Better-operated buildings yield better returns, and in in this way big data has the potential to increase significantly the marketability and value of commercial real estate assets.

Miller: Technological innovation and the use of new data sources is not as easy as monitoring Google searches for your office building or other social media, although those can help manage branding. It is an experimental and labor-intensive process, which is why it will take some time

to fully implement and utilize all the new data exploding from connecting everything. The result, however, will be safer, more-efficient and better-managed real estate; faster and more efficient valuation; and speedier due diligence. Don’t expect this to happen tomorrow, yet it is very exciting to see what is possible, and that is what we are learning now.

Tobin: Extensive property-level information is readily available online today, particularly in the real estate sector. While there are many benefits to this “open-source” information, some find it disconcerting that their mortgage documents and personal information relating to their home are available online. This proliferation of data has a distinctly positive impact on real estate, bringing more lenders and capital and lower interest rates to the space, because open information

mitigates risk for lenders. I am personally a big advocate of technology and information and truly believe that the benefits of big data outweigh many, but not all, concerns.