Using Intelligent Automation to Bank Smarter, Not Harder


technology-5-4-19.pngBy this point in 2019, most consumers and companies are somewhat familiar with the concept of artificial intelligence. Executives and consultants have discussed its application in financial services for years; lately, the conversations have been brisk and some organizations are doing more than just talking. Many tangible AI use cases have emerged at financial institutions of all sizes over the last 12 months, and intelligent technology is beginning to make an impact on banks’ productivity and bottom lines.

Still, AI remains a largely abstract concept for many institutions. Some of the biggest challenges these banks face in preparing and executing an AI strategy starts with having a too-narrow definition of these technologies.

Technically, AI is the ability of machines to use complex algorithms to learn to do tasks that are traditionally performed by humans. It is often misrepresented or misunderstood in broader explanations as a wider range of automation technologies — technologies that would be more appropriately characterized as robotics or voice recognition, for example.

Banks interested in using intelligent automation, which includes AI, robotic process automation, and other smart technologies, should target areas that could benefit the most through operational efficiencies or speed up their digital transformation.

Banks are more likely to achieve their automation goals if executives shift their mindsets toward thinking about ways they can apply smart technologies throughout the institution. Intelligent automation leverages multiple technologies to achieve efficiency. Some examples include:

  • Using imaging technology to extract data from electronic images. For example, banks can use optical character recognition, or OCR, technology to extract information from invoices or loan applications, shortening the completion time and minimizing errors.
  • Robotic process automation, or RPA, to handle high-volume, repeatable manual tasks. Many institutions, including community banks with $180 million in assets up to the largest institutions in the world have leveraged RPA to reduce merger costs, bundle loans for sale and close inactive credit and debit cards.
  • Machine learning or AI to simulate human cognition and expedite problem solving. These applications can be used in areas ranging from customer service interactions to sophisticated back-office processes. Some industry reports estimate that financial institutions can save $1 trillion within the next few years through AI optimization. Several large banks have debuted their own virtual assistants or chatbots; other financial institutions are following suit by making it easier and more convenient for customers to transact on the go.

What are next steps for banks interested in using AI? Banks first need to identify the right use cases for their organization, evaluating and prioritizing them by feasibility and business need. It’s more effective to start with small projects and learn from them. Conduct due diligence to fully assess each project’s complexity, and plan to build interactively. Start moving away from thinking about robots replacing employees, and start considering how banking smarter – not harder – can play out in phases.

Drafting a Data Strategy


data-4-29-19.pngBanks need to be aware of trends in data analytics that are driving decision-making and customer experience so they can draft an effective data plan. Doing so will allow them to compete with the biggest banks and non-bank technology competitors that are already using internal customer data to predict behavior and prescribe actions to grow those relationships. These approaches leverage concepts like machine learning and artificial intelligence — buzzwords that may seem intimidating but are processes and approaches that can leverage existing information to grow and deepen customer relationship and profitability.


analytics-4-29-19-tb.png10 Data and Analytics Trends Banks Should Consider
Current trends in analytics include focusing on the customer’s experience, using artificial intelligence and machine learning in analysis, and storing and organizing information in ways that reduce risk. Banks also need to know about threats like cybersecurity, long-term developments like leveraging blockchain, and how to build a governance program around the process. Knowing the trends can help companies make educated choices when implementing a data strategy.

datat-trends-4-29-19-tb.pngHow Banks Can Make Use of Data-Driven Customer Insight
Banks can use machine learning and artificial intelligence to gain insights into customer behavior and inform their decisions. These data-driven approaches can efficiently analyze the likeliness of future events, as well as suggest actions that would increase or decrease that likeliness. Many institutions recognize the need for new technical capabilities to improve their customer insight, but a significant percentage struggle to embrace or prioritize the technology among other priorities at their bank. These institutions have an opportunity to establish a data strategy, map out their internal information and establish appropriate governance that surrounds the process.

How Analytics and Automation Can Improve Shareholder Value


automation-2-8-19.pngAdvanced data science technologies like artificial intelligence (AI), machine learning and robotic process automation are delivering significant benefits to many banks.

As part of their mandate to protect shareholder value and improve financial performance, bank directors can play an important role in the adoption of these promising new technologies.

Technology’s expanding influence
With fintech companies generating new competitive pressures, most traditional banks have recognized the need to adopt some new techniques to meet changing customer habits and expectations. Declines in branch traffic and increased online and mobile banking are the most obvious of these trends.

Yet, as important as service delivery methods are, they are in a sense only the top layer of bigger changes that technology is bringing to the industry. New data-intensive tools such as AI, machine learning and robotic process automation can bring benefits to nearly all areas of a bank, from operations to sales and marketing to risk and compliance.

Advanced data analytics can also empower banks to develop deeper insights and make better, more informed strategic decisions about their customers, products and service offerings.

The power of advanced analytics
Historically, business data systems simply recorded and reported what happened regarding a customer, an account, or certain business metrics. The goal was to help managers understand what had happened and develop strategies for improving performance.

Today’s business intelligence systems advance this to predictive analysis – suggesting what is likely to happen in the future based on what has been observed so far. The most advanced systems go even further to prescriptive analysis – recommending or implementing actions that increase or decrease the likelihood of something happening.

For example, AI systems can be programmed to identify certain customer characteristics or transaction patterns, which can be used for customer segmentation. Based on these patterns, a bank can then build predictive models about those customer segments’ likely actions or behaviors – such as closing an account or paying off a loan early.

Machine learning employs algorithms to predict the significance of these customer patterns and prescribe an appropriate response. With accurate segmentation models, a bank can tailor marketing, sales, cross-selling and customer retention strategies more precisely aligned to each customer.

Automating these identification, prediction, and prescription functions frees up humans to perform other tasks. Moreover, today’s advanced analytics speed up the process and can recognize patterns and relationships that would go undetected by a human observer.

Industry leaders are using these tools to achieve benefits in a range of bank functions, such as improving the effectiveness of marketing and compliance functions. Many large banks already use predictive modeling to simplify stress testing and capital planning forecasts. AI and machine learning technology also can enhance branch operations, improve loan processing speeds and approval rates and other analytical functions.

Getting the data house in order
While most banks today are relatively mature in terms of their IT infrastructures and new software applications, the same levels of scrutiny and control often are not applied to data itself. This is where data governance becomes crucially important – and where bank directors can play an important role.

Data governance is not just an IT problem. Rather, it is an organization-wide issue – and the essential foundation for any advanced analytics capabilities. As they work to protect and build shareholder value, directors should stay current on data governance standards and best practices, and make sure effective data governance processes, systems and controls are in place.

AI, machine learning, and robotic process automation are no panacea, and banks must guard against potential pitfalls when implementing new technology. Nevertheless, the biggest risk most banks face today is not the risk of moving too quickly – it’s the risk of inertia. Getting started can seem overwhelming, but the first step toward automation can go a long way toward taking advantage of powerful competitive advantages this technology can deliver.

What Bankers Should Know About Conversational AI in 2019


omnichannel-12-21-18.pngWe’ve come a long way since filmgoers watched nervously as the computer “Hal” struck out on his own with the bland yet threatening response, “I’m sorry Dave, I’m afraid I can’t do that,” in Stanley Kubrick’s “2001: A Space Odyssey.”

Today, humans are comfortable interacting with machines. Twenty-five percent of customer service and support operations will integrate virtual customer assistant (VCA) or chatbot technology by 2020, up from less than 2 percent in 2017, according to Gartner, Inc. And in some cases, consumers seem to prefer machines to humans. Therapy bots like Woebot are successful in part because users don’t experience the fear of judgment that may exist speaking with another human.

The technology that enables machine-to-human interactions is known as conversational AI. It powers virtual assistants across apps, websites, messaging and smart speakers. In 2018, we saw virtual assistants take off in banking – finding their way into the apps and websites of the world’s largest banks. Pilots turned into production, and virtual assistants started engaging with real consumers at scale.

This technology is a growth engine for banks by servicing customers more efficiently, engaging customers to boost brand loyalty and acquiring customers to increase their lifetime value. But all conversational AI solutions are not the same.

Here are three key trends for banks implementing conversational AI in 2019.

Think omnichannel, not multichannel
Consumers’ expectations for banking are evolving from siloed multichannel experiences to deeply personalized omnichannel experiences. They expect the experience with their bank to be consistent and informed, no matter which channel they interact on, and they expect to move smoothly between channels. Banks implementing conversational AI should support “channel traveling” and never lose sight of who the customer is – not just their unique ID, but their preferences, history and more.

Make sure your solution supports sophisticated customer journeys and hand-offs between channels. Your customer should be able to start a conversation with your virtual assistant on Amazon Alexa, and the virtual assistant should be smart enough to follow up with more related details in the mobile app. The virtual assistant knows the optimal interaction model for each channel and generates the right response for the channel of choice.

Conversations that explain “why”
By now, consumers are accustomed to automated assistants that respond to them. A virtual assistant that answers questions has become table stakes. In 2019 and beyond, we’ll see consumers gravitate toward services that can give them answers to questions and explain their finances. People will come to expect answers to “why” in addition to “what.”

For example, customers will want to know their balance, but also why it is lower than expected. Or, they may ask if they can afford a vacation now, and if they could still afford it in six months. They’ll want to know their FICO score, and why it is lower than last year.

Banking customers already know chatbots can give their balance and move their money. In 2019, their expectation will be that conversational AI will do more to help manage their money with context and insights.

The era of available data is here
After years of waiting for banking data to be available, the future is finally here. Inspired by regulations such as PSD2, or the Payment Services Directive, in the European Union, large banks around the world are adopting open banking standards and launching modern developer portals that enable a new world of banking services. This is good for conversational AI, because its real value comes from personalized, actionable experiences—experiences that require data and services. With financial institutions such as Wells Fargo, Citi, Mastercard and Standard Chartered streamlining access to APIs, building meaningful conversational experiences and integrating them with the banks’ other services will be much easier and faster.

In 2018, we’ve seen conversational AI is here to stay, and in 2019, we need to make virtual assistants do more than respond to FAQs and complete simple tasks. Banks implementing conversational AI should remember consumer expectations are growing every year. To meet those expectations, leverage the abundance of available data via APIs to create omnichannel customer journeys that can understand your customers and explain the context to them.

Driving Efficiency Through Automation



Automation makes it possible for banks to gain efficiencies and help their employees be more effective. But how can bank leaders ensure they’re getting the most out of these solutions? Richard Milam, the CEO of the software company Enablesoft, explains that people—not technology—will drive success in these efforts, and culture plays an important role.

  • How Banks Use Automation Today
  • Successfully Deploying Automation
  • Advice for Bank Leaders

Considering Conversational AI? Make Sure Your Solution Has These 3 Things


AI-10-9-18.pngThe pace at which consumers adopt new technologies has never been faster. Whether it’s buying coffee, booking travel, or getting a ride, or a date, consumers expect immediacy, personalization, and satisfaction. Banking is no different. According to a study by Oracle, when banks fall short of their consumers’ digital expectations, a third of consumers are open to trying a non-bank provider to get what they want – and what they want, increasingly, is a digital experience that’s smart, intuitive, and easy to use.

Conversational AI—a platform that powers a virtual assistant across your mobile app, website, and messaging platforms—is core to providing the experience consumers want. Whether you choose to build or buy a conversational AI solution, it needs three key things.

Pre-packaged Banking Knowledge
A platform with deep domain expertise in banking is what gives you a head start and accelerates time to market. A solution fluent in banking and concepts such as accounts, transactions, payments, transfers, offers, FAQs, and more, is one that saves you time training it about the basics of banking. Deep domain expertise is also necessary for a virtual assistant or “bot” to hold an intelligent conversation.

Your conversational AI solution should already be deeply familiar with concepts and actions common in banking, including:

  • Information about accounts – so customers can check balances and credit card details such as available credit, minimum payment and credit limit.
  • Information about transactions – so customers can request transactions by specific accounts or account types, amount, amount range (or above, or under), check number, date or date range, category, location or vendor.
  • Information about payments – so customers can move money and make payments using their bank accounts or a payment service such as Zelle or Venmo.

Human-like Conversations
Most conversational AI systems answer a question, but then leave it up to the customer as to what they should do next. Few conversational AI systems go beyond answering basic questions and helping customers accomplish one simple goal at a time, and that’s sure to disappoint some customers.

A conversational AI platform should be able to track goals and intents so bots and virtual assistants can do more for consumers. It should go beyond basic Natural Language Understanding and combine deep-domain expertise with the ability to reason and interpret context. This is what gives it the ability to help customers achieve multiple goals in a fluid conversation – creating a “human-like” conversation that not only understands what the customer is texting or saying but tracks what the customer is trying to do, even when the conversation jumps between multiple topics.

Platform Tools
Under the hood of every Conversational AI platform are the deep-learning tools. Effective analysis of data is at the core of every good conversational AI platform—understand how it collects and federates, builds, trains, customizes and integrates data. This will have a huge impact on the accuracy and performance of the virtual assistant or bot.

After you deploy the system, you want to be empowered to take full control of the future of your conversational AI platform and not be trapped in a professional services cycle. Make sure you have a full suite of tools that allow you to customize, maintain and grow the conversational experiences across your channels. You’ll need to measure engagement and continually train the virtual assistant to respond to ever-changing business goals, so you’ll want an easy way to manage content and add new features and services, channels, and markets.

Above all – is it Proven in Production?
There is a huge difference between a proof of concept or internal pilot with a few hundred employees to a full deployment with a virtual assistant or bot engaging with customers at scale in multiple channels. A conversational AI platform is not truly tested until it’s crossed this chasm, and from there can improve and grow with additional use cases, products and services and new markets.

During the evaluation, ask for customer engagement metrics, AI training stats, and business KPIs based on production deployments. Delve into timelines related to integration – are the APIs integrating with your backend systems fully tested in production? Understand how the system is trained to extend and do more. What did it take to roll out new features with a system already deployed?

If the platform has been deployed in production several times with several different financial institutions, you know it has been optimized and tested for performance, scalability, security and compliance. You can have confidence the solution was designed to work with your back-end and front-end ecosystem, channels and infrastructure. Only then has it been truly validated and proven to integrate and adhere to many leading banks’ rigorous and challenging regulatory, IT and architecture standards and technologies.

There’s just no way to underscore the value of production deployments as a way to separate the enterprise-ready from the merely POC-tested solutions.

More Than Your Average AI


artificial-intelliegence-6-6-18.pngUSAA was looking for a financial technology firm to tell them they were dead wrong.

They found that candid firm in the summer of 2017, and the resulting partnership has generated one of the first technologies the large financial services provider has rolled out that allows its members, active military personnel, veterans and their families, to interact with USAA on Amazon’s home devices that feature the digital assistant Alexa. USAA wanted to solve a problem: “How do we create a scalable conversation engine that can talk about something as sensitive as personal finances?” says Darrius Jones, assistant vice president for enterprise innovation at USAA, in describing what led them to their partner, Clinc.

Working together, Clinc, an Ann Arbor, Michigan based fintech that has grabbed the attention of national outlets like CNNMoney, and USAA developed a “scalable conversation engine,” as Jones describes it, that goes far beyond a binary question-answer interaction between a human and a “talking silo.” The two companies formally announced their partnership to create a conversational artificial intelligence solution in August 2017. USAA was the first major national bank to partner with Clinc, which had raised nearly $8 million in multiple funding rounds before the announcement.

“From the beginning, our teams worked together to create a very different experience for delivering content that is complex … and trending,” Jones says.

Those interactions propelled the work USAA and Clinc have done to be named in March as a finalist for Bank Director’s FinXTech Innovative Solution of the Year, an award presented at the FinXTech Annual Summit, held this year in Phoenix.

The truth is, several banks have worked with fintechs or internally developed some version of conversation capabilities with in-home devices like the Amazon Echo, Apple HomePod or Google Home. But most of these interactions are basic, limited to rudimentary questions about account balances and other simple, mostly binary, inquiries. But $155 billion asset USAA uses Clinc’s technology to offer broader conversations and analysis than just binary sort of answers. Jones calls it “three-dimensional” because of its ability to infer intent from interrogatory statements based on contextual evidence proposed in the interaction with its human counterpart.

“Our Alexa skill really has the ability to disseminate what you’re saying and, in some cases, answer a question most humans wouldn’t answer without proper context,” Jones says. So instead of just getting simple responses, the engine can analyze spending trends at specific places, for example, and aggregate data across several accounts, making the responses more holistic in nature. The technology can also be predictive at times when the user asks questions in a vague way, according to Jones, and can respond with a suggested prompt with a perceived answer, a capability that is so far rare in other similar AI interfaces.

USAA had wanted to wade into the AI and conversation engine area before signing on with Clinc, Jones says, and had developed a strategy they thought would have been effective, efficient and competitive, but then Clinc’s CEO, University of Michigan Professor Jason Mars, chimed in when they met at a conference. As Jones recalls, he told the team at USAA, “I think you guys have a great idea, but I think you’re doing it wrong.” It was exactly the assessment USAA was looking for. “We love partners who are willing to challenge us and make us better,” Jones says.

The conversational technology is still a ways off from administering payments or other products that might add to the bank’s bottom line, according to Jones. But USAA has already identified opportunities to leverage the technology to increase member loyalty, and potentially work in soft pitches for other products the bank offers and advise members of possible risks.

Jones says USAA has “really struggled with the success” of the pilot programs, so much so that they had to check and recheck the data and reporting to ensure it was accurate. Eventually, he says they hope to continue the scaling of the technology, which he expects to involve additional updates later this year.

“I have a belief that the days of typing or touching as your primary method of interaction are numbered,” Jones says.

FinXTech Annual Summit: Exploring the Power of Collaboration


fintech-5-9-18.pngBanks are increasingly becoming technology companies—not in the eyes of investors, perhaps—but certainly in terms of meeting the expectations of their customers in a rapidly digitizing consumer marketplace. Banks have been heavy users of technology for decades, but the role of technology in virtually every corner of the bank, from operations to distribution, to product design, lending and compliance, is taking on a greater strategic importance.

It was only a few years ago that an emerging fintech sector was viewed by many bankers as a competitive threat, particularly marketplace lenders like Lending Club and SoFi, or new payments options offered by the likes of Apple Pay and Venmo, PayPal’s successful P2P product. While those competitive threats still exist, the focus of most banks today is working with fintech companies in collaborative relationships that benefit both sides. Banks are facing enormous pressure from changing consumer demographics and preferences to develop new products and services that go well beyond what they have traditionally created on their own. The new ideas include more than just new applications that enhance or expand an institution’s mobile banking capability, an area that continues to receive a lot attention. With developments in artificial intelligence (AI) and machine learning, banks are able to bring greater efficiencies and effectiveness to such disparate activities as regulatory compliance and accounts payable.

There are challenges to a partnership approach, however, beginning with the necessity to fully vet the potential fintech partner in a thorough due diligence process. Banks are conservative by nature, while many of the fintech companies developing the systems and applications that enable banks to stay abreast of the rapidly evolving digital economy are quite young and culturally different. Banks that want to work with fintech companies will have to do the necessary due diligence while also bridging the culture gap.

The benefits, and challenges, of working collaboratively with fintech companies will be the focus of Bank Director’s FinXTech Annual Summit, which will take place May 10-11 at The Phoenician resort in Scottsdale, Arizona. The agenda kicks off with back-to-back peer exchange discussions on the dynamics of fintech partnerships and changes in consumer behavior, then provides both general session presentations and case study sessions that examine such topics as innovation, AI, automation in commercial lending, vendor contract management, the digital robotic workforce and the future of the branch in an increasingly digitized world.

Also occurring at the Summit will be the announcement of Bank Director’s 2018 Best of FinXTech Awards, which will be given to banks and their fintech partners for projects where they worked together in a collaborative relationship. From a list 10 finalists, awards will be given a bank and its fintech partner in each of the following award categories: Startup Innovation, Innovative Solution of the Year and Best of FinXTech Partnership.

Why Improving the Customer Experience is the Latest Industry Trend


technology-5-9-18.pngPerhaps you’ve noticed a driving theme across the financial services industry to innovate and improve the customer experience. While the path to achieving the goal varies greatly—from using artificial intelligence to personalize the experience to implementing a single platform—winning the experience and efficiency game comes down to one simple mission: create an enjoyable customer experience.

Everyone watched as this transformation, led by user experience, disrupted industries like e-commerce and entertainment. Companies like Amazon and Netflix have been ahead of the curve in delivering superior experiences to their customers, which often has not just been because they offer a great user experience, but also because of logistical excellence. Today, offering a personalized experience and real-time services across any device is the new normal.

In fact, recent data confirms this expectation even in financial services, according to Barlow Research Associates Inc. Customers cite that a primary driver for working with a bank is often based on how easy the bank is to do business with. Furthermore, customers expect the same seamless and easy-to-use digital interaction with their bank as they do while ordering an Uber, for instance.

The Single Platform Difference
With the rise of financial technology (fintech), there is no shortage of vendors providing an assortment of solutions to help financial institutions offer an improved customer experience. Unfortunately, some banks and credit unions have found themselves with more headaches than enhancements when multiple vendor solutions are implemented across the institution.

Disparate systems often lead to data siloes, expensive integration projects and increased overhead in due diligence and security monitoring. The seamless, multi-channel experience customers want is thrown out the window when multiple, separate systems are implemented and expected to work together, and rarely do.

A single-platform solution has become a strategic imperative to overcome many of the issues associated with disparate systems. With one system managing all channels, banks and credit unions can deliver a unified experience while reducing operational inefficiencies. This is a clear need as more than half of financial institutions customers don’t believe that the digital channel of a bank can service all their needs, recent research data shows.

However, transforming the customer experience doesn’t just mean introducing a slick user interface; back-office processes must also be efficient and meet the real-time demands of customers. There is almost a 50 percent abandonment rate of banking customers starting a process online and then finishing at a branch, according to the 2017 Account Opening and Onboarding Benchmarking Study. This is likely due to another fact: less than 20 percent of financial institutions have implemented an end-to-end process to date.

Streamlining customer and employee interactions within a financial institution to drive increased efficiency, transparency, profitability and regulatory compliance across all lines of business is essential in order to drive a superior customer experience. Regardless of the originating channel, a customer should receive:

  • Transparency into banking processes
  • Convenient access to status updates and document sharing
  • Personalized, seamless customer experience
  • Digital/mobile-enabled access

Where to Begin
Consider these three areas for ensuring a successful transformation.

  1. Plan the journey before you begin. In order to establish a vision to guide the entire organization (or even a line of business), it’s imperative to first understand the journey customers go through when interacting with the institution. This involves considering customers’ emotions, and the cause for those emotions. Dig into these areas while exploring the customer journey to improve the experience.
  2. Pick one product or line of business and take it end-to-end. Many institutions, while taking the correct path of not just implementing a slick user interface, end up trying to take on more digital transformation than they are ready for. Instead of trying to transform the whole bank or department all at once, greater success is often met by starting with a single product, like a secured small business loan, and transforming that experience end-to-end.
  3. Finally, release then iterate. Starting with a single product or a particular line of business provides the opportunity to test and perfect. The iterative process is important not just to improving the customer experience, but also to ensuring that any needed operating model adjustments can be properly vetted.

As technology giants like Amazon continue to push the bounds of customer expectations, it can at times feel daunting to try to make these shifts at your own institution. However, as customer demands for seamless digital experiences grow and become even more a part of the buying decision, the emphasis on a single platform to help deliver both an exceptional experience and logistical excellence is even more pronounced. This growing demand marks the importance and urgency of employing a strategy that focuses on delivering a delightful customer experience from the first interaction all the way to the back office.

What Facebook’s Data Debacle Could Mean for Banking


regulation-5-2-18.pngThere was a particular moment on the second day of his most recent testimony Facebook CEO Mark Zuckerberg struck a rare smile.

Zuckerberg, on Capitol Hill to answer pointed questions about the scraping of company’s data on 87 million of its users by U.K.-based Cambridge Analytica—was asked if Facebook was a financial institution.

The odd inquiry came during a string of questions from Rep. Greg Walden, the Oregon Republican who chairs the House Energy and Commerce Committee that grilled Zuckerberg about the massive company’s complex web of operations, which includes a mechanism for users to make payments to each other using popular apps familiar to bankers like PayPal and Venmo, as well as debit cards.

Facebook is not a financial institution in the traditional sense, of course, but it does have a clear position in the financial services space, even if just by its role in providing a platform for various payment options. It has not disclosed how much has been transferred between its 2 billion users, and it certainly has raised questions about how tech companies—especially those with a much narrower focus on financial technology, or fintech—collect, aggregate, use and share data of its platform’s users.

This relationship could soon change as Washington lawmakers discuss possible legislation that would place a regulatory framework around how data is collected. Virtually any industry today is dependent on customer data to market itself and personalize the customer experience, which is predominantly on mobile devices, with fewer personal interactions.

“I think it’s likely something is going to happen here, because we’ve kind of been behind the curve as it relates to [regulation], especially Western Europe,” says David Wallace, global financial services marketing manager at SAS, a global consulting and analytics firm.

While banks are somewhat like doctors and hospitals in the level of trust that consumers historically have had with them, that confidence is finite, Wallace says.

Survey data from SAS released in March shows consumers want their banks to use data to protect them from fraud and identity theft, but they aren’t crazy about getting sales pitches.

At the same time, payments services like Paypal’s Venmo and Zelle, a competing service that was developed by a consortium of banks, also collect data, but have a lower “score” with consumers in trust, according to Wallace.

Where’s the Rub?
The question from Walden barely registered on the national news radar, but it also highlights new areas of concern as banks begin to adopt emerging technologies like artificial intelligence, and market new products that are often driven by the same kind of data that Facebook collects.

The recent SAS survey also asked respondents about their interactions with banks, and how AI might influence those. Most of the survey respondents say they are generally comfortable with their bank collecting their personal data, but primarily in the context of fraud and identity theft protections. Sixty-nine percent of the respondents say they don’t want banks looking into their credit history to pitch products like credit cards and home mortgages.

As the Cambridge Analytica situation demonstrates, there is a fine balance that must be observed giving all companies the opportunity and space to succeed in an increasingly digital environment while protecting consumers from the misuse of their personal data.

Congress tends to be a hammer that treats every problem like a nail—so don’t be surprised if the use of customer data is eventually regulated. Thus far, the only regulatory framework in existence that’s been suggested as a model of what might be established in the U.S. is the GDPR, or General Data Protection Regulation, currently rolling out in Europe. It essentially requires users to opt-in to allowing their data to be shared with individual apps or companies, and is being phased in across the EU.

How that approach might be applied to U.S. banks, and what the impact might be, is still unclear. It could boil down to a “creepy or cool” factor, says Lisa Loftis, a customer intelligence consultant with SAS.

“If you provide your (health) info to a provider or pharmacy, and they use that information to determine positive outcomes for you, like treatments or new meds you might want to try, that might factor into the cool stage,” Loftis explains.

If you walk by a bank branch, whether you go in it or not, [and] you get a message popping up on your phone suggesting that you consider a certain product or come in to talk to someone about your investments without signing up for it, that’s creepy,” she adds.

Any regulation would likely affect banks in some way, but it could be again viewed as a hammer, especially for those fintech firms who currently have a generally regulation-free workspace as compared to their bank counterparts.