Smart Data Emphasizes Quality, Not Quantity


International Data Corp. (IDC) suggests that worldwide revenues for big data and business analytics will grow from $130 billion in 2016 to more than $203 billion in 2020. The commercial interest in data comes as no surprise given the immense role it plays in facilitating innovation in the financial services industry and beyond. After all, for banks of any size, data is at the core of their vital business decisions. It enables the appropriate risk assessment of every financial operation and allows banks to accurately estimate the creditworthiness of existing and potential customers, among other things.

The value of data, however, has long been correlated with its quantity rather than quality, laying a foundation for big data analytic tools and intensive data generation in relationships between companies and consumers. While we can’t deny the value of such an approach in displaying major industry trends and assessing customer groups on a general level, financial technology startups nowadays are proving that innovation in the financial services industry will likely come from a smart use of more limited, but higher quality data rather than its scale. In addition, given the diversity of sources and ever-accelerating speed of data generation, it becomes more difficult to drive meaningful insights.

Smart Data’s Value as Raw Material
Smart data represents a more sophisticated approach to data collection and analysis, focusing on meaningful pieces of information for more accurate decisions. Coupled with advanced capabilities of AI and machine learning, smart data presents an opportunity for startups to efficiently derive deeper insights from limited, but relevant data points. Professionals from Siemens and an increasing number of organizations across industries believe that smart data is more important than big data. Moreover, in the future, the most important raw material will be smart data.

For banks, smart data represents an opportunity to change the way a prospective customer’s creditworthiness is assessed, hence, a chance to expand credit to new groups of population that have previously been overlooked. In fact, financial inclusion starts with the use of smart data. While national financial institutions are looking for reasons to deny someone of access to financial services, tech companies like Smart Token Chain, BanQu and others are looking for reasons to expand connectivity and open new opportunities for those excluded from the financial system. Those companies aim to leverage a different set of records for inclusive growth and a better tomorrow.

The Anatomy of Smart Data
Mike Mondelli, senior vice president of TransUnion Alternative Data Services, listed property, tax, deed records, checking and debit account management, payday lending information, address stability and club subscriptions as some of the sources for alternative data. As he emphasized, “These alternative data sources have proven to accurately score more than 90 percent of applicants who otherwise would be returned as no-hit or thin-file by traditional models.”

Other alternative sources of data used by technology companies include web search history, phone usage, social media and more. Sources can be combined into clusters, which some professionals distinguish as traditional, social and online.

Source: Forbes,

The data sources emphasized above are certainly not exhaustive and their combination can vary depending on the goal and availability. In any case, the goal is to find the most relevant, even though limited, data that corresponds with the goal of its use. Fortunately, there is a variety of fintech companies leveraging the benefits of alternative data for inclusive initiatives, credit extension and more. Such examples include ZestFinance, Affirm, LendUp—all of which use data from sources such as social media, online behavior and data brokers to determine the creditworthiness of tens of thousands of U.S. consumers who don’t have access to loans.

Companies like Lenddo, FriendlyScore and ModernLend use non-traditional data to provide credit scoring and verification along with basic financial services. Those companies are creating alternative ways to indicate creditworthiness rather than relying on traditional FICO scores. For banks, such companies open up opportunities to expand their customer base without compromising their financial returns and security, while leveraging technological advancements for adopting innovative ideas and enhancing community resilience.

Getting Big Value out of Big Data


If my bank calls me, I brace for bad news. It shouldn’t be that way.

Banks are considered leaders in data analytics-most have been at it a long time, have a lot of data and know a lot about their customers. But some banks aren’t actually doing a great job of translating data analytics into better customer service and smarter relationship development, or even taking advantage of opportunities to monetize data.

My bank has more data about me-salary, mortgage, purchases, FICO score, family birthdays, how much I spend and save, where I vacation, live and work-than any other single entity, and certainly enough to make some great proactive suggestions. But I never get that call offering special services for my kid who’s going off to college.

A great banking relationship should be about delighting the customer. More and more, that means using analytics to anticipate customer needs, flag (and fix) patterns that precede complaints, and deliver experiences that exceed customer expectations. Banks should consider watching and learning from my social, location and digital interactions: As the wheels of my plane touch down in Hawaii, a coupon for my favorite local restaurant should pop up in my mobile wallet app. Many customers now expect this level of anticipation of their interests–enabled by data analytics-and if you can’t deliver it, loyalty may not keep them with you. Banks that up the ante on data analytics will be able to attract and keep customers. Banks that don’t step up likely won’t be able to compete with innovators and retailers that consistently deliver personalization.

Many banks are also missing a huge opportunity to monetize data. No one likes receiving unsolicited offers that miss the mark, but when information is targeted and presented appropriately, it can be something customers actually appreciate knowing about. Banks have the opportunity to deliver a privacy-compliant data feed to retailers, to enable targeted marketing and higher customer satisfaction.

The great news is that analytics technology is good and getting better. Advances in distributed data architecture, in-memory processing, machine learning, visualization, natural language processing and cognitive analytics can help banks gain and deliver personalized, granular insights.

Cognitive computing-training computers with machine learning and process automation techniques to enhance human decision making-can analyze massive datasets in a variety of data types, including numbers, text, images and speech. Tasks traditionally performed only by humans can now be accomplished with less direct involvement, such as evaluating credit risks, fraud detection, loan application processing, collateral lien search or making real-time recommendations. For example, the CFPB, OCC, Fed, and FRBNY have required larger institutions to data mine complaints to check for any high-risk incidents that were not escalated properly. Using advanced machine learning techniques, including speech and text analytics, banks can now search for regulatory terms and consumer protection requirements to identify regulatory risks and look for patterns in complaint escalation. Cognitive solutions can also help customers develop sound financial habits through their bank’s mobile app. Clinc’s Finie is a voice-enabled digital assistant that can check spending against budgets and habits, transfer money between accounts and retrieve historical statements.

Advanced analytics also enable more engaging customer experiences that reflect each customer’s profile, habits and situation in that moment, so when a client reviews investing forums for impacts of geopolitical events, a wealth manager can deliver a personalized scenario risk analysis from the investment office. The message could also include an option to request a meeting with a financial advisor. For banks, it’s time to make the crucial shift from insight into action, using cross-channel analytics to drive new messaging and behavioral analytics to deliver targeted offers and in-bank personalization. Luckily, the technology is there to help you take it to the next level.

To harness the full potential of data and analytics at scale, banks will likely have to invest in sustained programs that are truly embedded in business processes and culture-industrialized analytics that are woven into the DNA of the organization. It requires a serious commitment to the vision of insight-driven customer service, business strategy and risk management, as well as a serious investment in talent, data management, analytics and infrastructure for repeatable results and scale. Executing well has the potential to achieve remarkable gains in customer satisfaction, cross selling, complaint reduction and efficiency, all key levers for becoming a more efficient, nimble and profitable bank.

Need to Grow? Try Data

growth-10-3-16.pngTo survive, a plant at a minimum needs soil, sunlight and water.

Plants that grow better than others have usually received fertilizer on a regular basis. Think of the vegetable garden that produces bushels of produce throughout the summer.

Farms that produce commercial volumes utilize all of these resources, but they also have someone directing strategy based on a big-picture view including weather forecasts, equipment maintenance needs, field reports on pests, research on future risks to the crop, etc.

Banks, too, can subsist on the basics: good staff, products that meet the market’s current needs and essential data about the customer or operations. These financial institutions may be able to get by without analyzing the tons of data in their systems. Other banks may “fertilize” their growth by analyzing some of their data to shape product development or efficiency processes.

However, even at these institutions, a common factor stunting growth is disconnectedness between analysts, teams and departments when it comes to day-to-day operational or regulatory information. Just as the data is siloed, so is the insight and communication, making it challenging to provide either top-down or bottom-up strategy reviews. When people from multiple departments try to piece together data from multiple systems, it can be nearly impossible to glean actionable insight for outpacing current and future competitors. This quandary is magnified at top management levels, where executives must balance strategic objectives and pressures without a data-driven big picture.

Indeed, bank CEOs, directors, chief information officers and chief technology officers responding to Bank Director’s 2016 Technology Survey recently overwhelmingly indicated their institutions are plagued by the inability to effectively use data.

Financial institutions using data over the life of a loan are better able to manage and direct the big picture, shaping institutional strategy for superior growth. They can help determine not only where the institution has been making money, but also where it can expect to make money, how it can maximize profits and how it can minimize risk.

For example, at an ill-equipped institution, loan pricing decisions may be based only on competitive information. While comparability of terms is important to borrowers, it can also lead the institution into a disadvantageous relationship—one that could lose money for the institution. However, at an institution using a life-of-loan system, the loan officer would have an accurate measure of risk and overall profitability of the relationship, providing the loan officer with a range of acceptable terms that still ensure the bank meets its targets. When decisions aren’t made in a vacuum or from a single lender’s spreadsheet, the bank benefits from better decisions, and when better decisions happen across the commercial portfolio, the institution wins.

In addition to pricing, an integrated solution streamlines and automates much of the:

  • loan origination process
  • credit analysis
  • loan approval
  • loan administration and
  • portfolio risk management.

Connecting the data throughout the entire loan process allows bankers, underwriters and risk management professionals to communicate better and more efficiently. These systems also tend to unify employees with diverse skills into a more cohesive unit while building in a layer of awareness and appreciation for the full life of the loan.

All of this enables the financial institution to make better lending decisions based on relationship profitability and strategic goals, and it makes it easier for management to make informed decisions that ensure outperformance—even in an environment where interest rates and loan demand remain low and compliance risks are high.

In short, an integrated solution addresses the three greatest business concerns cited in Bank Director’s Technology Survey: regulatory compliance, becoming more efficient and competition from other banks.

The intersection of insight provided through an integrated solution not only creates more opportunity to develop an effective strategy, it can also guide the strategy. It gives bank management the ability to pivot, and the knowledge of where best to pivot to, so that the institution can focus its investments, development and sales efforts on the right areas for growth. In this way, the financial institution can flourish, rather than simply survive.

Want to learn more about integrated banking solutions? Sageworks has a free guide for bank executives.

Joining the Advisory Team


Greetings from the United Kingdom. I’m part of the FinXTech Advisory Group and will be writing brief updates here from time to time. You may not know me and so you can find out what I get up to over here and on my blog. In case you don’t want to do that, one of the advisors to President Obama called me “the most authoritative voice” in fintech anywhere, which is why I guess the guys at FinXTech asked me to come on board.

Conversely, why have I joined the FinXTech Advisory Board?Mainly because its membership is comprised of many of the fintech leaders that I respect in the United States from the largest financial institutions, leading investment firms, technology companies, service providers and government entities. FinXTech is not just another media company—it’s a platform for connection via the website, conferences for networking and interactive brainstorming sessions for real world application.

FinXTech’s mission is simple: to connect those who are truly shaping the future of financial services. The fintech ecosystem consists of five distinct groups:

  • The leaders of fintech companies who are producing, researching and creating new technological solutions.
  • Financial institutions that are embracing, adopting and/or seeking to implement cutting edge advancements.
  • Service providers, consultants, advisors and lawyers who are guiding the regulatory, compliance and implementation processes.
  • The investor and venture capital communities that determine who and what might be the next best thing for financial services.
  • And the government voices, be it from the Office of the Comptroller of the Currency, the Consumer Financial Protection Bureau or even the White House.

By establishing a group of advisors, FinXTech is able to set the course and agendas for their platform, based on the thoughts and feedback from some of the best and brightest in the industry—and me. So naturally, I joined, too—to be on the inside cutting edge, in addition to adding to it.

You probably already know a lot about fintech, although you may not know who is leading it. Is it Silicon Valley? Is it Wall Street? Is it London? Or maybe Singapore? In fact, financial technology is everywhere. During my travels—and I travel so much that when people ask me where I live, I usually say the British Airways executive lounge—I see every country with a financial focus creating a fintech focus. Oslo, Berlin, Zurich, Amsterdam, Tel Aviv, Dubai, Bangkok, Sydney, Shanghai, Hong Kong, Mexico City, S??o Paulo—fintech is happening in all of these places.

Why are so many billions of dollars being poured into these new technologies for finance?

The answer is that we are revolutionizing financial services through the Internet. For the past 50 years, bank technology has mostly been deployed for internal efficiencies and usage. Today, technology is creating external efficiencies, particularly through peer-to-peer networking. Apps, APIs, analytics, artificial intelligence, big data, blockchain, cloud, distributed ledgers, machine learning and the Internet of Things are changing everything. Everything is now networked and open sourced through marketplaces and connected platforms. This technological revolution has been bubbling for years, starting with the Amazons and Alibabas of the world, moving along to the Facebooks and Baidus, Tencents and Googles. Now we have the Ubers and Airbnbs, and everyone wants to know who will be the next PayPal or AliPay.

This is why fintech is so exciting, as we have major new players like Stripe and Square appearing almost overnight and gaining multi-billion valuations. There is no doubt that we’ve got it going on, and in my next few pieces here I’ll outline the key trends, players and developments.

For now, I wish you a big hearty British welcome to FinXTech. Glad you could make it and it’s good to be here.

A Fear of Missing Out


Recently, I had the opportunity to spend time with some of Deloitte’s most senior team in both New York City and at the White House Fintech Summit in Washington, D.C. Together, we explored issues on the minds of many bank executives today; namely, how banks should approach corporate innovation and work with fintech companies. Certainly, collaboration between technology companies and traditional financial institutions has increased — think proofs of concept, partnerships and strategic investments — but much still needs to be done.

From my perspective, the evolution of the banking world is first and foremost a business issue. Historically, banks organize themselves along a line of products. There are banks such as Umpqua Bank, BankMobile (a division of Customers Bank) and Live Oak Bank that have oriented their operations around customer needs and expectations. However, these are more exceptions then the rule.

Consequently, as new technology players emerge and traditional participants begin to transform their business models, there is growing sentiment that successful institutions need to enable financial services for life’s needs through collaboration and partnerships with the very fintech companies that once threatened to displace them.

As Joe Guastella, global and U.S. managing director for financial services at Deloitte Consulting, shared, “incumbents can indeed thrive in a disrupted world. They can learn from history and be proactive in managing the change instead of being passive participants. But first they need to understand how fintech affects them before taking advantage of all the potential benefits fintech offers.”

Accordingly, here are three questions that I posed to Guastella and his colleagues that anyone responsible with growing and changing a bank needs to address.

Q: What do banks need to do so as not to be left behind?

Michael Tang, a partner and head of global digital transformation and innovation at Deloitte, believes institutions must “experiment with intent and purpose… avoid the Fear Of Missing Out (#FOMO) syndrome and investing and dabbling for the sake of it.” He is of the opinion that banks need to “take greater interest in the customer needs analysis from ethnographic research and behavioral economics.”

Thomas Jankovich, a principal in Deloitte and the innovation leader for the U.S. Financial Services Practice, echoed this. He opines that banks should work towards becoming platform based, data rich and capital light — with an infinite ability to scale. He challenges those senior-most bankers to re-think how their executives are educated, immersed and motivated to make bold decisions and take hold of the concept of “Platform as a Service.”

Q: How are some of the more successful financial institutions developing corporate and/or business-unit strategies to take advantage of digital opportunities?

Tang and Jankovich shared that the more progressive and successful banks are taking advantage of emerging opportunities in nuanced ways. For instance, they are:

  • Using a combination of supportive leadership providing the mindset, right incentives and performance metrics to truly support a digital business model;
  • Curating the right talent_ while leveraging the “buy, build, partner” model for capability; and
  • Retaining customers by providing an experience that includes usability, data analytics and competitor awareness.

Q: Should banks become more like tech companies?

Cathy Bessant, the chief operations and technology officer of Bank of America, recently opined that banks shouldn’t see themselves as fintech companies. She reasons that a bank’s customers have such high expectations in terms of reliability and security, that the “fail fast” mindset of many technology firms doesn’t jive with customer expectations. As she made clear, “the potential cost of failure at scale is something to be avoided.”

So with most everything technology-oriented coming back to continuity, security and third parties, one must balance the need for exceptional service with the push for change. According to Michael Tang, one needs “a portfolio approach and clear expectations on the purpose and roles between run and change.” By extension, in terms of corporate innovation, Thomas Jankovich believes that banks need to move beyond the concept of “Run the Bank / Change the Bank” to actually “innovating the bank” in order to disrupt itself.

Yes, banks will be challenged to meet the future expectations of their customers as well as to assess the additional risks, costs, resources and supervisory concerns associated with providing new financial services and products in a highly regulated environment.

Size and scale doesn’t have to be a drawback. It can, however, be an advantage in this environment.


As a starting point for such an internal discussion, take a look at “Disaggregating Fintech: Brighter shades of disruption,” a report that looks at the the impact of fintech in six areas within financial services and across six business dimensions. Questions or comment? Email meat

Preparing for the Great Wealth Transfer


The net worth of millennials is slated to more than double by 2020, with estimates ranging from $19 to $24 trillion, according to a report released by Deloitte Consulting. This, combined with the fact that more than two-thirds of wealth managers’ current clients are over the age of 60, means that wealth managers should be preparing for a massive wealth transfer.

In anticipation of the great wealth transfer, it is important to recognize the expectations of this younger generation. Millennials, a group that has grown accustomed to instant search results and access to on-demand advice, expect to be treated as unique individuals and value the ability to make data-driven decisions. Yet, as much as this generation embraces digital technology and on-demand services, when it comes to finances, they also want a personalized approach.

Consequently, it is no longer sufficient to place a client in a generic portfolio model, especially when the client can pay next to nothing for a similar portfolio allocation through an automated investment service online. The quality and level of service that this new generation of clients demands is higher, as they want to be involved in making informed decisions about their money and now have cheaper options for managing their wealth.

And wealth managers cannot rely on millennials to just “inherit” their services from parents or grandparents. Fifty-seven percent of millennials would change their bank relationship for a better technology platform solution, according to the Deloitte study. In order to remain profitable as client demographics shift, and to meet the demands of millennials, wealth managers should leverage technology and data analytics tools to successfully engage their clients and maximize the value of service provided.

By harnessing the availability of data analytics, wealth managers can adequately get to know their clients and identify the distinct human capital factors in their clients’ lives, enabling them to provide truly tailored financial advice and investment recommendations.

Rather than simply assessing a client’s age and income, existing technology allows wealth managers to consider other aspects such as, geography, work sector, health, family, real estate, balance sheet and time until retirement. Taking a more holistic approach to wealth management makes it possible to customize a client’s investment portfolio, designed to fit each client’s unique risks and financial situation. This approach also delivers a more interactive, consultative wealth management process for both the wealth manager and the client.

To illustrate the value of this method, consider a petroleum engineer in her thirties living in Houston, where the oil industry drives property prices. An automated investment service or a conventional approach to wealth management would likely propose a wealth portfolio that is based largely on her age and income; however, this would fail to identify the concentration in oil within her career, property and subsequently, her portfolio.

Furthermore, changing market conditions can also be challenging for even the most experienced wealth managers, but today’s technology can help wealth managers mitigate risk and market fluctuations for their clients. Digital platforms and data analytics can adjust the risk exposure of the portfolio and compare performance over various market scenarios, enabling wealth managers to propose targeted solutions in an engaging, diagnostic context.

Ultimately, wealth managers must focus on understanding the needs of their new clientele to remain profitable in an increasingly competitive market. In fact, a recent PwC survey revealed attrition rates of more than 50 percent in intergenerational transfers of wealth, highlighting the fact that the next generation relies very little on the services of their parents’ wealth manager. This means that banks and their wealth managers must expand their technological capabilities and digital offerings, while gaining a deeper understanding of their clients to successfully build a sustainable business in today’s evolving market.

The industry should recognize that this group of clients has incorporated technology into almost every aspect of their lives and they expect nothing less of the businesses and financial advisers they interact with. As the industry quickly approaches the transfer of wealth to millennials, wealth managers will have to satisfy the demands of a new generation, and technology will play a critical role in engaging those who are accustomed to the benefits and polished user experience associated with digital tools and devices.

Getting Diverse Candidates to the C-Suite

*This video was originally published in Bank Director digital magazine in February, 2016.

U.S. Bank has a diverse workforce. It has a diverse board. But where it lacks diversity is the C-suite. U.S. Bank’s head of human resources, Jennie Carlson, talks about the bank’s strategy to change that.

She discusses:

  • U.S. Bank’s strategy for finding diverse candidates for the C-suite
  • The natural biases that every person has
  • Surprising demographic data that U.S. Bank found

In Plain Sight: The Extraordinary Potential of Big Data

big-data-7-30-15.pngThe era of big data has arrived, and few industries are better positioned to benefit from it than banking and financial services.

Thanks to the proliferation of smartphones and the growing use of online social networks, IBM estimates that we create 2.5 quintillion bytes of data every day. In an average minute, Yelp users post 26,380 reviews, Twitter users send 277,000 tweets, Facebook users share 2.5 million pieces of content and Google receives over four million search queries.

Just as importantly, data centers have slashed the cost of storing information, computers have become more powerful than ever and recently developed statistical models now allow decision makers to simultaneously analyze hundreds of variables as opposed to dozens.

But while fintech upstarts like Simple, Square and Betterment are at the forefront of harnessing data to tailor the customer experience in their respective niches, no companies know their customers better than traditional financial service providers. The latter know where their customers shop, when they have babies and their favorite places to go on vacation, to mention only a few of the insights that can be gleaned from proprietary transactional data.

When it comes to big data, in turn, banks have a potent competitive advantage given their ability to couple vast internal data repositories with external information from social networks, Internet usage and the geolocation of smartphone users. In the opinion of Simon Yoo, the founder and managing partner of Green Visor Capital, a venture capital firm focused on the fintech industry, the first company to successfully merge the two could realize “billions of dollars in untapped revenue.”

Few financial companies have been as proactive as U.S. Bancorp at embracing this opportunity. Using Adobe Systems Inc.’s cloud computing services, the nation’s fifth-largest commercial bank “integrates data from offline as well as online channels, resulting in a truly global understanding of its customers and how they interact with the bank at multiple touch points,” says an Adobe case study.

By feeding cross-channel data into its customer relationship management platform, U.S. Bancorp is able to supply its call centers with more targeted leads than ever before. The net result, according to Adobe, is that the Minneapolis-based regional lender has doubled the conversion rate from its inbound and outbound call centers thanks to more personalized, targeted experiences compared to traditional lead management programs.

Along similar lines, a leading European bank studied by Capgemini Consulting employed an analogous strategy to increase its conversion rates by “as much as seven times.” It did so by shifting from a lead generation model that relied solely on internal customer data, to one that merged internal and external data and then applied advanced analytics techniques, notes Capgemini’s report “Big Data Alchemy: How Can Big Banks Maximize the Value of Their Customer Data?”

Another European bank discussed in the report generated even more impressive results with a statistical model that gauges whether specific customers will invest in savings products. The pilot branches where the model was tested saw a tenfold increase in sales and a 200 percent boost to their conversion rate relative to a control group. It’s this type of progress that led Zhiwei Jiang, Global Head of Insights and Data at Capgemini, to predict that a “killer app” will emerge within the next 18 months that will change the game for cross-selling financial products.

The promise of big data resides not just in the ability of financial companies to sell additional products, but also in the ability to encourage customers to use existing products and services more. This is particularly true in the context of credit cards.

“In a mature market, such as the U.S., Europe or Canada, where credit is a mature industry, it is naïve for a bank to believe that the way it is going to grow revenue is simply by issuing more credit cards,” notes a 2014 white paper by NGDATA, a self-described big data analytics firm. “The issue for a bank is not to increase the amount of credit cards, but to ask: How do we get the user to use our card?”

The answer to this question is card-linked marketing, an emerging genre of data analytics that empowers banks to provide personalized offers, savings and coupons based on cardholders’ current locations and transactional histories.

The venture capital-backed startup edo Interactive does so by partnering with banks and retailers to provide card users with weekly deals and incentives informed by past spending patterns. Its technology “uses geographical data to identify offers and deals from nearby merchants that become active as soon as the customer swipes their debit or credit card at said merchant,” explains software firm SAP’s head of banking, Falk Rieker.

Founded in 2007, edo has already enrolled over 200 banks in its network, including three of the nation’s top six financial institutions, and boasts a total reach of 200 million cards.

Poland’s mBank offers a similar service through its mDeals mobile app, which couples the main functions of its online banking platform with the company’s rewards program. “What makes this program so innovative is its ability to present customers with only the most relevant offers based on their location and then to automatically redeem discounts at the time of payment,” notes Piercarlo Gera, the global managing director of banking strategy at Accenture.

A third, though still unproven, opportunity that big data seems to offer involves the use of alternative data sources to assess credit risk.

The Consumer Financial Protection Bureau estimates that as many as 45 million Americans, or roughly 20 percent of the country’s adult population, don’t have a credit score and thereby can’t access mainstream sources of credit. The theory, in turn, is that the use of additional data sources could expand the accessibility of reasonably priced credit to a broader population.

One answer is so-called mainstream alternative data, such as utility payments and monthly rent. This is the approach taken by the VantageScore, which purports to combine “better-performing analytics with more granular data from the three national credit reporting companies to generate more predictive and consistent credit scores for more people than ever.”

Another is to incorporate so-called fringe alternative data derived from people’s shopping habits, social media activity and government records, among other things. Multiple fintech companies including ZestFinance, LendUp and Lenddo already apply variations of this approach. ZestFinance Vice President for Communications and Public Affairs Jenny Galitz McTighe says the company has found a close correlation between default rates and the amount of time prospective borrowers spend on a lender’s website prior to and during the loan application process.

“By using hundreds of data points, our approach to underwriting expands the availability of credit to people who otherwise wouldn’t be able to borrow because they don’t have credit histories,” says McTighe, pointing specifically to millennials and recent immigrants to the United States.

While this remains a speculative application of external data by, in certain cases, inexperienced and overconfident risk managers, there is still a growing chorus of support that such uses, once refined, could someday make their way into the traditional underwriting process.

This list of big data’s potential to improve the customer experience and boost sales at financial service providers is by no means exhaustive. “It’s ultimately about demonstrating the art of the possible,” said Wells Fargo’s chief data officer, A. Charles Thomas, noting that big data could one day help the San Francisco-based bank reduce employee turnover, measure the effectiveness of internal working groups and identify more efficient uses of office space.

It’s for these reasons that big data seems here to stay. Whether it will usher in a change akin to the extinction of dinosaurs, as Green Visor’s Yoo suggests, remains to be seen. But even if it doesn’t, there is little doubt that the possibilities offered by the burgeoning field are vast.

Data Rich, Information Poor: Bank Leaders Want More Knowledge on Data

More than half of bank leaders want to know how to better use data, and they agree that it’s one of the top technology concerns for their institution, according to Bank Director’s 2014 Growth Strategy Survey. Large banks are more likely to use data to support growth, but all institutions can find benefits in data analytics.

Mining Data to Make Money

mining-data.pngAt U.S. Bank, big data is not an abstract concept. It is making real money for the Minneapolis-based $371-billion asset bank. The bank tracks transactions and customer online interactions to flag those for a personalized marketing message, people who might be a good prospect for a loan, for example. Recently, a branch manager used such flags to call a business customer who had recently moved a large sum out of the bank. It turned out that the customer, a business owner, was planning to apply for a loan for his business at another bank, hence the money transfer. Instead, the banker was able to persuade him to apply for a loan at U.S. Bank.

“Not only were they able to get that customer the funding that customer needed to expand his business but they were able to give him a line of credit for operating expenses as well,’’ says Richard Martino, senior vice president of enterprise data and analytics at U.S. Bank.

Big data is no longer a futuristic concept for technicians and marketers to fantasize about. Credit card companies have used data analysis to make sophisticated fraud detection and credit offers for years, but such analysis wasn’t widely used by banks for other purposes. That is changing. Even small banks now buy slices of information for targeted marketing campaigns that hit on their core strategies, rather than worrying about compiling and understanding unwieldy databases of information. The cost of such information for a variety of companies is now much more affordable than it used to be and easier to use. Driving this is data warehouses that know everything about you and that you have never heard of such as Acxiom in Little Rock, Arkansas, and credit rating agencies such as Equifax in Atlanta. The sheer amount and scope of information that can be purchased on consumers across the country is unfathomable. Data warehouses can basically find customers for you to target in marketing campaigns based not only on where they live, but what their shopping habits are, their income levels, their debt and how much investable assets they have. You can basically underwrite them before you offer them a loan via an email campaign.

Do you want to know which of your customers are getting ready to buy a home? No sweat. Buy the data. Which ones are shopping for an automobile? Check. Would you like to contact all the people who move within a mile of one of your branches within a week of their move (before they have a chance to sign up with a different bank or tell the post office)? Check again. Data analytics can be used in customer service and relationship management, as well as risk and compliance. The challenge is deciding what data your bank needs, interpreting it correctly, and executing your plan to use that data well.

“Banks and credit unions struggle with this so much,’’ says Patrick Grosserode, director of product management for Deluxe Marketing Services, a division of Deluxe Corp. in Shoreview, Minnesota. “They can’t find the mix that works. It seems like you are trying to boil the ocean.”

Big banks have hundreds of employees who work in data analysis, just trying to figure out what their data says about their customers. Some work in marketing or fraud detection or compliance management. The biggest banks might have 40 people looking at branch analytics, 50 people doing pricing analytics and 100 people doing market analytics, says Sherief Meleis, a managing director at the New York-based financial services consulting firm Novantas, who estimates that banks that use customer analytics to drive marketing, distribution and pricing can improve revenues by 3 to 4 percentage points per year, and earnings by 10 to 15 percentage points per year.

“The big [banks] have armies the size of our company churning through the data,’’ says Equifax’s Retail Banking Leader Brad Jones. “They are myopic. They don’t even know what their companies are doing [with it.] They are just looking at the data.”

To protect consumers, agencies such as the Federal Deposit Insurance Corp. require banks to issue privacy statements to consumers letting them know how their personal information will be used, and letting them opt out of certain kinds of use. According to the Office of the Comptroller of the Currency, Regulation DD—which implemented the Truth in Savings Act—established standards for marketing content for deposit accounts. The Fair Credit Reporting Act spells out the permissible uses of consumer credit reports and prescreened consumer reports. Prescreening consumer report lists is only permitted if a bank is making a firm offer of credit or insurance, for example.

The key to good data marketing is targeting your customers without making them feel like they are being spied upon. In the U.S. Bank example, where the bank was using internal data rather than prescreened consumer report lists, the banker didn’t mention the large transaction to the business owner when he called. He just asked the guy if he was happy with the bank.

Banks can also use outside vendors to drive the marketing campaign. For example, if a credit rating agency can see you are making a car payment every month for a certain amount, they can surmise what your rate is, when you will be buying another car, and tell your bank when it’s time to make a better offer. Banks have a lot more information on their customers than they can possibly sift through. But data compilation is much simpler than it used to be. Core processors such as Fiserv, FIS and Jack Henry & Associates are holding on to much of this data that their banks could use. “The great thing about data analytics these days is, it is much cheaper than it used to be,’’ says Corey Booth, managing partner of The Boston Consulting Group in Boston. Cloud-based solutions can help store data. Start-up technology firms are trying to take advantage of cheaper software. “It’s so hard, but it’s not as hard as it used to be.”

For example, StrategyCorps, which sells a checking account product called BaZing, will use the bank’s own data to help banks determine which of their customer households are profitable based on everything from debit fee income to loans. Profitable customers get the value-added checking account with coupons and other services for free. Unprofitable customers can pay $6 per month for the checking account (or opt for a no-frills account that is free if a minimum deposit amount is met). Banks from $500 million to $750 million in assets typically use BaZing. [Full disclosure: StrategyCorps is partially owned by the same investor who owns Bank Director magazine.]

Grosserode says there are several important issues that banks must sort through first if they are going to use data well. You have to define what you are trying to do. Then, you have to ask yourself if that is even possible to measure. If not, you should stop right there. Ask yourself the next question: Is it possible to generate a profit from this campaign? If there are only seven people you end up going after, that might not be so profitable. Is the segment you want to focus on stable enough not to vanish over time? How will you measure and track your success? Is your success, for example, based on the number of people who click on an online ad or the number of people who actually go through the process of filling out an application online and getting approved? Establish a control, as if you were conducting a scientific study. What might happen if you don’t do this marketing campaign, and how will you know that? Banks that don’t have sizeable marketing departments will probably have to hire a marketing firm to help make decisions and devise a campaign.

What happens if you don’t get into data analytics? After all, it sounds time consuming and expensive. The old fashioned way to generate home mortgage leads is to generate relationships with real estate agents. But those traditional avenues may not cut it anymore. People do a lot more research online than they ever used to, both for auto loans and mortgages. Younger consumers rely on recommendations from friends more than they do real estate agents, says Stephen Ramirez, the CEO of Beyond the Arc, which does data analytics for banks. People will price their mortgages and get offers online. “These innovations are beginning to take root now,’’ he says. “It is even more vitally important to develop the business strategy and match the strategy to your priorities.”

Paul Schaus, the president, CEO and founder of consulting firm CCG Catalyst, says it’s a critical time now for banks to address how they want to use data, and not get left behind. “Technology has allowed us to pinpoint our clients and attract them to our bank. If you don’t do that, your competitors are going to get those customers and you are going to be left with the customers nobody else wants.”