Say Hello to Open Data Sharing


Banking customers are demanding more and more access to innovative fintech services and applications that are making their financial lives easier. Big banks are responding by embracing the trend of open data, allowing fintech companies to access user information to provide a more seamless customer experience. One needs to look no further than the recent data sharing agreements with Intuit reached by Wells Fargo and JPMorgan Chase.

A big reason that Wells and Chase make agreements like these is to knit innovative fintech services, like (recently acquired by Intuit) more tightly into their service offerings. By providing fintech app providers like with access to customer data through an open application programming interface (API), banks like Wells Fargo can better integrate customers who use into their own ecosystem.

But the question is, will the trend of open data sharing benefit certain banks or fintechs over others? What are the consequences forbig banks that are slow to make their data accessible? And in the end, will regulators leave them any choice?

Big banks are adopting open data primarily for three reasons. First, they’re trying to reassure clients from an ethics and security standpoint. By opening their data to third parties, they’re demonstrating that security measures are adequate and they’re not afraid of transparency.

Second, bringing new customers who are attracted by the bank’s fintech offerings into their ecosystems creates the opportunity to upsell and cross-sell those users more traditional products like mortgages and business loans. Finally, big banks want to leverage fintech technology and innovation to expand their service offerings, without incurring the cost of internal innovation. Banks like Chase can then focus their internal IT development resources on back-end functions to support customer facing technology.

But in a world where fintechs are in an arms race to onboard users, and banks are all too happy to partner with the “next best thing” in fintech, will there be enough room in the marketplace for everyone? Big banks will obviously be able to survive in this environment, with the money and resources to cement data sharing agreements with the best fintechs. Niche fintechs will also have an enormous amount of leverage. For instance, peer-to-peer lending platforms like SoFi that are challenging traditional big bank lending will have their choice of who to partner with and how much they’re able to command. It’s the mid- to lower-sized banks and credit unions that might be challenged, as they simply don’t have the resources to adopt the “Banking as a Platform” mentality that Chase and Wells Fargo are moving towards with their data sharing strategy.

There are reasons why banks might be skeptical of the open data era. Security and privacy of data, along with the issue of who “owns” customer information being the primary concerns. However, legacy institutions that are slow to open their APIs to fintechs will likely experience negative consequences.

The cost for banks to innovate and develop products like Mint and QuickBooks (under the Intuit umbrella), are extremely high. To compete with Chase and Wells Fargo in terms of similar personal finance and accounting software, banks would have to divert significant amounts of internal IT resources away from critical areas like security and back-end infrastructure. Moreover, even if banks do successfully develop similar technologies on their own, they’re missing out on the user and customer base that fintechs have already established. As of 2016, had over 20 million users, a number that would be nearly impossible for even a very large bank to reach on its own with an internally developed and branded application.

The Consumer Finance Protection Bureau (CFPB), has already outlined its plans to advocate for open data sharing. And in fact, the trend has already been set abroad, with the European Community adopting the Directive on Payment Services Regulation (known as “PSD2”). PSD2 was implemented to encourage competition in the fintech ecosystem, and to make it easier for third-party technology providers to gain access to customer financial data. The end goal is to enhance the benefit that consumers get from banks and fintechs, and the CFPB is rowing hard in that direction.

In recent remarks at the Money 20/20 Conference in Las Vegas, CFPB Director Richard Cordray made clear that banks that don’t open their data to third parties are not operating transparently, nor in the best interests of their clients. Moreover, he believes that the CFPB can force all banks to adopt open APIs due to certain provisions in the Dodd-Frank Act. The CFPB also realizes the increasing prevalence of mobile banking, and wants to ensure those third-party mobile apps have adequate access to bank-end customer data to best serve consumers on their smartphones.

Globally, all signs point towards more open data sharing relationships between big banks and fintechs. The winners will be banks that focus on opening up sooner, rather than later, and partnering with fintechs that serve their customers’ core needs. Banks whose core business is investing, for instance, should focus on opening and partnering with investing fintechs that their customers are probably already using, such as the low-cost trading platform Robin Hood. Mature fintechs will also benefit, as they’ve already built a user base and can scale even more once they’re part of a Chase or Wells Fargo type ecosystem. Finally, legacy banking customers who seek simplicity in their experience will be big winners. Customers of big banks will begin to have access to fintech applications, technology and innovation in a “one stop shop” fashion. In the end, the CFPB doesn’t look like it will give banks much of a choice, so it’s up to them to embrace the trend or risk falling behind the competition.

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.

Data Wars to Dominate 2017


It’s the start of 2017, and many people have already blogged their predictions for the year. I won’t repeat those predictions, as the future isn’t what it used to be, but I do find it interesting to look at the common themes across them all. The standout theme for me is that 2017 is the year of The Analytic.

Data analysis to be exact. Now you can get analysis paralysis if you dwell on this too long, but data analytics will be the fuel for everything else. Effective data analysis is core to being able to leverage artificial intelligence; data analytics will be the key to unlocking the internet of things; and data analytics is essential to chatbots, augmented customer experiences and enhanced services.

Think about it: How can you deliver a decent digital service if you don’t have the data to tell you what your customers want? This then becomes the essential challenge for all incumbent institutions as their customer data is often siphoned into silos. I know that for a fact, having spent 20 years trying to create bank enterprise data stores and services. Now some banks are beginning to wake up and embrace the data opportunity and threat but, for those who are comfortable with distributed data and no ability to analyze it effectively, here’s the hard truth: You will not survive.

I’ve believed this for a long time but, with each passing year, I am sounding the alarm bell louder and louder. After all, we have argued for decades that a consistent customer experience across channels is essential. We haven’t been able to deliver it, but we’ve tried. Now we are not even talking channels anymore, we are just talking about a digital foundation that everyone accesses through open marketplaces online. We have moved from a historical, closed and proprietary architecture to an open platform structure where everyone can plug and play. But how can they do that if the data is locked in old proprietary systems that are siloed and closed?

This is going to be a key conundrum for U.S. banks, which are arguing that the only person who can access customer data is the customer. That’s a great way to lock out third-party players, shut down the aggregators and block the open systems march. However, it strikes me as being like the king who has placed his army at the gates of the castle, while not noticing that the citizens are all leaving via the back door. What is the use of having a kingdom if there’s no one in it? And that is what will happen to banks that continue to have distributed data that cannot be leveraged.

The march of the fintech community, the regulator and the customer is towards easy, convenient, proactive and personalized financial providers. Those providers are increasingly like the Amazons of the world: they know their customers digital footprint and maximize their knowledge of that footprint to the hilt. In 2017, as we watch the progression of AI, machine learning, deep learning, chatbots and personalization, any bank that keeps its data locked up in a chastity belt is missing a trick.

The Future of Banks: Platforms or Pipes?

future-banking-11-9-16.pngMuch has been written about the future of banking. In the end, it all seems to come down to one question: Will banks become platforms or pipes?

In reality, there’s no question at all. Platforms are the winning business model of the 21st century and the banking industry is well aware. In fact, banks have been platforms for decades—fintech companies are merely creating the latest set of bank platform extensions. Earlier incarnations include ATMs and online bill pay for consumers.

That said, what’s happening today is forcing banks to rethink how fast they extend their platform to avoid becoming just the pipes. The advent of the cloud and the software revolution in fintech with billions of capital being invested every quarter has brought more innovation to banking in the past two years than it has seen in the past 20. Still, the current David taking down Goliath narrative surrounding the future of banking and finance ultimately fails to account for the reality of the situation.

While it often goes unnoticed, a great many fintech startups today rely heavily on banks to enable their innovative services. The success of financial innovations like Apple Pay for instance is happening with a great deal of participation and cooperation between technology companies and financial institutions.

This relationship between banks and fintech underscores the reality of the financial services industry’s future. Yes, finance is evolving alongside the accelerating curve of technology, and yes, fintech is driving much of this change, but banks are—and will remain—squarely at the center of the financial universe for quite some time to come.

Why is this? For one, banks have been the backbone of the modern economy since its inception. They are far too ingrained in the financial system to be removed within any foreseeable time frame. Banks also have deep pockets, infrastructure and experience. Large market caps and long track records are clear signals to customers that banks can weather the inevitable downturn. Startups, on the other hand, are more susceptible to turbulence and market volatility—things banking customers, especially business customers, would rather avoid.

Big data is yet another boon to banks’ staying power. Banks have been collecting data on customer transactions and behavior for decades. This creates major advantages for banks. When used in the right way, this data can be leveraged to do things like identify customers that are ripe for new payment services or to mitigate and underwrite risk in innovative ways.

But despite all this, there is one hazard currently menacing banks: disintermediation. Starting with the ATM, technology has been distancing consumers from banks for quite some time. Today, their relationship with the consumer is slimmer than ever.

Meanwhile, fintech is picking up the slack. While traditional banking experiences can feel clunky, fintech products and services are designed to work with people’s lives and deliver value in new and unexpected ways. These upstarts pride themselves on delivering superior customer experiences—banking that is intuitive, mobile, cloud-based, responsive, available 24/7, you name it.

Fintech companies are also agile and built for rapid iteration—skill sets banks don’t yet have internally. This allows fintech companies to focus heavily on usability and keeping their user interfaces modern. At, for instance, we upgrade our onboarding experience every two weeks. By comparison, most banks have outsourced many key functions to third-party service providers like Fiserv and Jack Henry, severely limiting their ability to make product changes outside of rigid, long-term release cycles.

The comparative lack of innovation by banks is no surprise. For decades, banks have spent most of their resources driving to meet quarterly earnings targets, delivering consistent results and ensuring compliance—the key objectives most highly-regulated, publicly-traded financial institutions must focus on to meet obligations to shareholders. That leaves fewer resources and funds for experimentation, learning and new product development. This makes it difficult for banks to keep up with shifts in customer preferences and behavior the way that fintech can. Banks know this and it is exactly why they are starting to shift their strategies to reflect being a platform and not just the pipes.

When banks become platforms for their customers and fintech partners, they increase the value of what they have built over the past several decades and disintermediation on the consumer front becomes irrelevant. Instead, as banks fuse their platforms with fintech, innovation will accelerate, creating tremendous value for everyone in the food chain.

Lending Automation: The Risk of Delayed Entry


Technology is rapidly enhancing the banking industry’s ability to comprehensively and efficiently evaluate the credit worthiness of businesses and consumers alike. The abundance of available information on borrowers and the effective management of big data enables banks to minimize risk, reduce defaults and maximize returns. The challenge is leveraging that data to realize its full potential value.

Data and technology go hand in hand. Banks already have a lot of great data on the customers they serve. The problem is, unless they take advantage of available technology, most banks won’t come close to maximizing the value of the customer information they have collected.

Only through technology can banks collect, aggregate and analyze massive amounts of data in a timely manner, allowing for quick, accurate decisioning of borrower information and the streamlining of the myriad of steps that make up the end-to-end lending process. Financial institutions that do the best job of adopting new financial technology stand to gain a huge competitive advantage over those that lag behind.

For those banks slow to adopt state-of-the-art lending technology, the risks of falling behind are significant. The failure to take advantage of the innovative resources available today puts the bank at a competitive disadvantage, and has negative impacts both financially and in terms of human costs.

Customers have grown to expect the convenience and speed that come with a digital experience and judge their financial institution by how it meets those technological expectations. As a result, customers are seeking out banks with a strong fintech brand. With both business and personal borrowers, one of the key drivers is the speed in which they can have access to the capital they need to solve their financial problems. Banks that respond the fastest typically have incorporated technology into the lending process. That results in increased customer retention and higher customer satisfaction scores.

Technology not only impacts the bank’s “customer experience,” it has a major impact on the quality of the “banker experience” as well. Technology enables bankers to focus their energies on activities that enhance their productivity. When customer data is quickly translated into actionable information, it allows bankers to ask better questions, solve more problems and meet more customer needs. This enhanced “banker experience” results in greater employee retention, loan portfolio growth and increased account penetration.

Efficiencies gained through technology also have a big impact on profitability. Loans that were once loss leaders are now able to be executed profitably. It costs just as much for a bank to take a $25,000 loan through the underwriting process as it does for a $900,000 loan. With the efficiencies gained through technology, smaller loans that were once loss leaders may now be executed profitably. This impact can best be seen in the critical small business lending space. Loans that have not been pursued by banks, and in some cases even turned away, are now able to be done profitably, which opens up new markets for banks and helps them better serve their local communities. Technology enables the collection, aggregation and analysis of data in a much more cost effective way and allows for automated, streamlined processes that enhance profitability.

Finally, regulators are also trending toward more comprehensive risk analysis and the expectation of predictive modeling as an objective way to make lending decisions and monitor loan portfolios. Current Expected Credit Loss standards (CECL) are being developed requiring “life of loan” estimates of losses. More and more, banks have to rely on their ability to manipulate available data as a way to meet the regulators’ demands. That kind of analysis is difficult to accomplish consistently and accurately using manual processes, but is much easier to achieve with technology.

Embracing financial technology is the key to survival in the lending world. Banks that adopt new lending technologies early will have significant advantages in the marketplace and will slow market share losses to aggressive, tech-oriented marketplace lenders.

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.

How New Technology Drives Sales in Your Bank

4-3-15-yseop.pngIn this highly competitive and data-driven environment, financial institutions are looking for innovative new ways to drive sales in the finance sector.

For banks, one of the most exciting technologies to explore is the artificial intelligence and natural language generation (NLG) space. NLG is a technology that can write like a human and turn big data into narrative and easy-to-understand content. It serves big data analytics, customer service and sales.

Three Ways to Drive Sales
Artificial intelligence-powered NLG software allows banks to understand unprecedented levels of client data, enhance customer service and ensure regulatory compliance.

  1. Make Sense of Big Data
    Banks need tools that explain what their big data means, what to do about it and why—in plain English (or the language of their choice) and in real time. The challenge is there is too much data, too few data experts and too little time to transform volumes of data into insight. But AI-powered NLG technology can turn data into written financial reports, executive summaries or portfolio analysis, for example, and explain how and why a conclusion is reached.
  2. Provide the Highest Level of Customer Service
    Banks are competing to deliver expert customer service—on the phone, online and in the branch. AI-powered NLG systems, often called “smart machines,” can be programmed with the expertise of your bank, can connect to client data and serve as an interactive expert to guide customer service teams through interactions. These systems can turn customer service agents into top tier sales people. They can even be deployed online to replicate the in-store banking experience and help make selling complex products and services easy.
  3. Ensure Compliance and Autonomy
    The advice-giving space is fraught with the potential for litigation in the face of ever-growing levels of regulations. Financial advisors and bankers must protect themselves by keeping meticulous records. These records, a sort of audit trail in case of litigation, coupled with legal fees and the fear of legal action, cost businesses millions if not billions of dollars each year. But AI-powered NLG can help. Programmed with the bank’s unique regulatory and legal framework, it can ensure compliant, expert advice, as long as the system is kept up-to-date. In case of litigation, it creates what we would call in banking an “audit trail.” The software shows its decision-making process, the advice it gave and explains why (and pursuant to what rules) it gave the advice. Since the software is incapable of human error, it never forgets a rule.

Is AI-Powered NLG Ready for Your Business?
NLG has been around for several decades, but NLG software has only recently been commercially viable, really since 2008. Fast forward eight years and Fortune 500 companies on both sides of the Atlantic are already using the combination of NLG and AI as a single software to make sense of big data, provide the highest level of customer service and ensure compliance and autonomy—all to drive revenue. In fact, these solutions are now fully scalable so banks can build their own applications—with no need to rely on vendors. Additionally, leading vendors of AI-powered NLG software provide configuration environments so easy to use that even non-technical users can build and update their own applications.

The Next Frontier in Banking: Big Data and Artificial Intelligence

12-22-14-NarrativeSci.pngTechnological change always precedes understanding of how it will change business. The web was a research tool before we realized it would change commerce and financial transactions. Smartphones flourished as “cool devices” before drawing people away from desktops. From Facebook to wi-fi, technologies have entered the world in one form only to have us discover an unexpected use that completely changes work, life and business.

Right now, we are in the midst of a sea change with regard to three types of technologies: big data, data analytics and artificial intelligence (AI). Each holds promise for banks. The question is, how can banks use them in a way that impacts what they do?

The goal of big data is clear—to enable understanding about what’s happening with a business, with investments and with clients so that better decisions can be made. However, big data conversations tend to focus on technology infrastructures (e.g. Hadoop, MapR, cloud computing) rather than how big data can help achieve business goals. This often happens when business and technology functions work together, but the discord has been amplified in financial services because these big data projects have taken years to design and implement. The result: Lots of technology but very little satisfaction.

Banks at are at a tipping point. They must take a step back and revisit what they want technology to accomplish. I believe there are three major opportunities for big data—understanding, discovery and predictability. Each is different and needs its own focus.

We now have access to years of performance data related to sales, products, divisions and branch activities, as well as customer opinions, which improves our ability to understand, communicate and make informed decisions. AI and cognitive computing have opened up new opportunities for banks, including narratives about performance that are automatically generated by a computer. The ability to transform data into language turns what machines only previously understood into information that humans can now easily understand. In the wealth advisory space, for example, advisors can access on-demand, up-to-date tailored performance summaries for their clients, giving them the knowledge they need to make the best investment recommendations.

Another objective of big data is to find new discoveries. Using statistical techniques or machine learning, data can now be used to discover relationships between separate data points, such as customer engagement, churn, transactions, sales and success likelihood. AI then transforms the discoveries of those correlations into actual explanations of identified relationships. And in its best application, the analysis starts with a business question. For example, knowing what you want to understand (e.g. “What causes us to lose a customer?”) drives the analysis process rather than taking a random walk through the data. By letting business needs drive the process, the resulting discoveries are data relationships that can actually be used.

The third opportunity for big data is to make predictions by leveraging the results of discovery and known business rules. These predictions give financial institutions the ability to recognize and respond to situations before they become problems. This technology has helped banks identify fraudulent behavior while it is happening as well as identify what the next pattern of suspicious behavior will be.

While AI-powered technologies are newer in banking, the impact is already being seen with systems that integrate well with big data applications. For example, IBM’s Watson is already providing financial advice to returning vets. Machine learning systems are automatically learning the rules used to identify fraud and money laundering. And, narrative generation is being used to automatically provide advisors and clients with comprehensive explanations about their investments that go beyond just the numbers. For both employees and consumers, these systems are making banking more efficient by freeing people from tasks that can be handled by computers.

Bridging the gap between what machines calculate and what people can understand, big data analysis and artificial intelligence have the potential to fundamentally change our relationship with computers and data. And in doing so, computers will be able to explain everything they know to anyone who needs to know it, whenever they need to know it.

The Top Five Technology Trends in Financial Services

9-10-14-CDW.jpgFrom transactions migrating to the cloud, to mobility reaching a tipping point and the influence of big data, the financial services industry is facing a new reality in 2014 and into the future. While addressing customer needs, financial services organizations must also maintain a sharp focus on meeting regulatory compliance standards and proactively addressing cyber threats.

1. Cloud Adoption Accelerates
Shifting business models, an intensified focus on efficiency and demand for customer-orientated technology is driving cloud computing technology adoption like never before. The fact that 71 percent of financial services organizations say they will invest more in cloud computing this year, which is up from 18 percent in the previous year, is proof positive that the cloud adoption ship has sailed, according to a 2013 report from PwC. And, the cloud is infiltrating all sectors of the financial services industry. Sixty percent of banks worldwide will process transactions in the cloud by 2016, while 77 percent of capital markets firms will leverage the cloud this year alone, according to technology research firm Gartner. In addition, many in the financial services industry have started to realize the quantifiable benefits that the cloud can deliver, like reduced infrastructure and hardware costs. The average cost reduction due to cloud computing savings on infrastructure is 23 percent, says a survey of a variety of companies by Rackspace and market research firm Vanson Bourne.

2. Mobility Makes Its Move
Mobility in the financial services industry has reached a tipping point. With the number of U.S. smartphone users predicted to increase to more than 265 million by 2017, according to research and consulting firm Frost & Sullivan, financial services organizations that haven’t created a mobile strategy are putting their growth and efficiency at risk. The mobile app market is exploding as banks, credit unions and capital markets firms embrace mobile services as a way to harness this constant, real-time interaction and significantly enhance the customer experience. In fact, 32 percent of U.S. adults now bank using their mobile phones, 51 percent of consumers use their phones to make a mobile payment and 51 percent of capital markets firms intend to invest in app-driven technology to achieve efficiencies, says surveys by the Credit Union National Association, the Pew Internet & American Life Project and IPC Systems.

3. Cyber Security Concerns Loom Large
The proliferation of mobile apps, cloud computing and big data are just some of the reasons why cyber security remains a top concern for the financial services industry. While retail security breaches demand the headlines, attacks on financial institutions still loom large. In fact, 37 percent of cyber attacks are directed at financial services organizations, according to a Verizon Data Breach Investigations Report. Recognizing that it’s critical to proactively combat rising cyber crime, 93 percent of financial organizations are maintaining or increasing their cyber security investments, says Ernst & Young. This includes allocating 46 percent of information technology (IT) spend toward security improvement, expansion and innovation over the next 12 months.

4. Big Data in the Driver’s Seat
Seventy-one percent of the financial services industry is already using big data and predictive analytics, but the demand for big data is getting bigger, according to the University of Oxford and IBM. This is the year that organizations will truly leverage big data to change the way they do business and achieve a competitive advantage by enhancing their knowledge of customers and prospects. Leading financial services firms are more confident than ever before about how they can use big data: 70 percent of data leaders say they can generate forward-looking insights from their data and 72 percent understand how to integrate performance and risk analytics, according to State Street Corp., which surveyed global financial institutions.

5. Regulatory Compliance Remains a Priority
Making it a priority to stay ahead of the regulatory compliance curve is crucial to the success of any financial services organization. Unfortunately, this is easier said than done. Nearly 80 percent of financial institutions admit to significant concerns about staying abreast of regulatory change and complying with regulator demands, according to a survey by Wolters Kluwer Financial Services. That is likely the reason many are heavily investing in compliance technology—to the tune of a 35 percent growth in compliance spending by 2015, says the Aite Group—as the market maintains its focus on compliance and reform. The trickle down effects of regulatory reforms—such as the Dodd Frank Act and recent Volcker Rule—continue to impact some 87 percent of financial services companies, according to Deloitte.

Big banks still grapple with their own complexity, risk

puzzle.jpgThe world’s largest banks have made a lot of progress revamping how they handle risk in the wake of the financial crisis, but they keep bumping up against the limitations of their own technology.

That’s one of the more interesting conclusions from a report that came out this week from Ernst & Young and the Institute of International Finance, a global association of 400 financial institutions and agencies. This latest report is the second to monitor changes the group recommended in July 2008.

It’s a little less sexy than the issue of bank CEO pay, but still pretty important in light of the last few years of financial pain. How are the world’s biggest, most complex financial institutions able to understand the risks posed by their own balance sheets and do something about them?

Ernst & Young conducted the survey of the group’s membership between October and December of last year, resulting in 60 online survey responses and 35 interviews with bank executives at firms such as Bank of America, PNC Financial Services and the Royal Bank of Canada, among others.

The survey identified areas of the greatest “progress” in banking: 83 percent of banks surveyed said they increased board oversight of risk and strengthened the role of the chief risk officer, for example. (Most chief risk officers now actively participate in business strategy and planning).

Ninety-two percent of banks surveyed have made changes to liquidity risk management in the last two years and 93 percent have implemented new stress testing.

But more than 80 percent of respondents cited “problems with inefficient, fragmented systems that can’t ‘talk to each other’ to extract and aggregate the accurate, quality data needed to conduct stress testing across the enterprise,’’ the report said.

Many are struggling with the demands on the resources needed to execute what is often a manual process of conducting tests and gathering results across the portfolios and businesses. One executive told us it takes 150 people across the businesses to analyze the scenarios mandated by both the regulators and the board risk committee.

Ugh. The problems associated with risk management don’t get much better:

More than 50 percent of those interviewed rate their ability to track adherence to risk appetite as moderate. The reasons cited range from the lack of clarity around which metrics align with risk appetite, to ill-defined methodologies for capturing and reporting information, to poor data quality and inadequate systems.

Poor data quality and inadequate systems? These are the largest banks in the world, remember. Perhaps this is an issue that will take only a few years to iron out. This is definitely one of those problems that won’t get a lot of publicity, but will really matter in preventing the next financial crisis.