In an Uncertain Market, Count on Data

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Against a backdrop of challenging macroeconomic risks, including inflation, potential recession and high interest rates, banks are also dealing with volatility connected to the collapse of three regional banks. These are difficult times, especially for financial institutions.

At the same time, banks are struggling to achieve primacy: being the go-to for their customers’ financial needs amid the marketplace of more agile fintechs. To do this, banks need to make smart decisions, fast. This amalgamation of business-impacting factors might seem like an unsolvable puzzle. But in an uncertain market, banks can leverage data to cultivate engagement and drive primacy.

Banks can count on data, with some caveats. The data must be:

  • While there is a massive amount of data available, banks often lack a complete picture of the consumers they serve, particularly as digital banking has made it easier for consumers to initiate multiple financial relationships with different providers to get the best deals. It’s vital that banks get a holistic view of all aspects of a consumer’s financial life, including held away accounts, insurance and tax data.
  • Increased open banking functionality empowers consumers to take charge of their data and use it to be financially fit. Open banking serves that connectivity and makes it more reliable.
  • Banks are flooded with data, the torrent of which makes it difficult to extract value from that data. Up to 73% of data goes unused for analytics. But the right analytics allows banks to reduce the noise from data and glean the necessary insights to make decisions and attract and retain customers.
  • Most transaction data is ambiguous and difficult to identify. Banks need enriched data they can understand and use. Data enrichment leads to contextualized, categorized data that gives banks tangible insights to improve their customer’s journey and inform more meaningful interactions.

Data as a Differentiator
Once banks have high quality data, they can use it to differentiate themselves across three key areas:

1. Make smart, fast customer decisions.
Banks are expected to deliver relevant offers at the right time to customers before rapidly making critical risk decisions. The ability to do this hinges on having a holistic view into the totality of a customer’s bank accounts. Data science algorithms using artificial intelligence and machine learning can then surface insights from that data to engage, retain and cross-sell via personalized, proactive experiences. From there, banks can execute for growth with rapid integrations that help gain wallet share and productivity.

2. Promote financial wellness.
Banks are nothing without their customers. To win and keep customers, banks need to provide tools and products that can enable an intelligent financial life: helping consumers make better financial decisions to balance their financial needs today and building to meet their aspirations for tomorrow. One way to help them with this is to provide a holistic view of their finances with account aggregation and money management tools. According to a recent survey, 96% of consumers who used financial apps and tools powered by their aggregated data were more likely to stay with the financial institution providing these tools. These tools give banks a way to helping their customers and inspire loyalty.

3. Forecast and manage risk.
Uncertainty over recent events in the banking industry has made the need for immediate insights into net deposit flows an imperative. Banks can use aggregated data to identify, forecast and manage their risk exposure. Digital transformation, which has been all the rage for years now, can enable centralized holistic views of a bank’s entire portfolio. Dashboards and alerts make it more practical for bankers to identify risks in the bank as they develop. A platform approach is vital. Banks need an entire ecosystem of data, analytics and experiences to mobilize data-driven actions for engagement, retention, growth and ROI.

Now more than ever, banks rely on data to cultivate engagement and drive primacy. Starting with holistic, high-quality data and applying analytics to derive insights, banks can drive the personalized consumer experiences that are necessary to attract and retain customers. And they can use that same data to better forecast and manage risk within their portfolio.

Fintechs Offer Many Opportunities for Banks. But How Do You Decide?

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Another version of this article was originally published on April 3, 2023, as part of a special report called “Finding Fintechs.” 

As part of his job, Clayton Mitchell once bought a list of global financial technology companies from a data provider. It had 7,000 names on it. 

“I can’t do anything with this,” says the managing principal in the risk consulting practice of Crowe LLP, who advises banks on partnering with fintech companies. “Figuring out the winners and losers is a bit of a needle in a haystack approach.” 

Banks that want to partner with technology companies or buy software from a vendor face the same sort of tsunami of options. On the one hand, fintechs offer real promise for community banks struggling to keep up with bigger institutions, credit unions and other competitors — a chance to cut costs and increase efficiencies, grow deposits and loans, and give customers quicker and easier ways to do business with the bank. 

But in the midst of economic uncertainty, banks face real risks in doing business with early-stage fintechs that might consolidate or even go out of business. So how do you choose? 

The problems banks face making a digital transformation are legion. In Bank Director’s 2022 Technology Survey, 45% of responding CEOs, directors, chief operating officers and senior technology executives said they worried about reliance on outdated technology. Forty-eight percent worried their bank had an inadequate understanding of the impact of emerging technologies. And 35% believed their bank was unable to identify the solutions it needs.

Historically, small and midsized banks have relied on their core processor to identify and vet companies for them. About half of Mitchell’s customers continue to rely on the bank’s core processor exclusively to find and vet technology companies for them. Cornerstone Advisors’ annual “What’s Going On In Banking” survey of community banks found this year that 55% of respondents didn’t partner with a fintech startup in 2022; 20% had partnered with one fintech; 16% with two and the rest with three or more. But Mitchell thinks the opportunities to go beyond the core are better and more feasible for small and community banks than ever, if the bank follows due diligence. “Sometimes you have to solve problems quicker than the core will get it to you,” he says. “There’s a growing appetite to go outside the core.” 

The big three core processors — Fiserv, FIS and Jack Henry & Associates — have started offering newer, cloud-based cores to connect with a greater variety of technology companies, plus there are ways to add additional layers to core systems to connect useful technological tools, using what’s known as application programming interfaces. “There are different layers of technology that you can put in place to relegate the core platform more into the background and let it become less of a focus for your technology stack than it has historically been,” says Neil Hartman, senior partner at the consulting firm West Monroe. 

In combination with technological change, leadership among banks is changing, too. The last three years of the pandemic taught banks and their customers that digital transformation was possible and even desirable. “We’re seeing more progressive bank leadership. Younger generations have grown up in digital environments and with the experiences of Amazon and Apple, those technology behemoths, and are starting to think about their technology partnerships a little more aggressively,” Hartman says. He adds that banks are beginning to reckon with the competition coming from the biggest banks in terms of digital services. “That’s trickling down into the regional and community bank space,” he says. 

Fintechs, likewise, are adjusting to banks’ sizable regulatory compliance obligations, and they’re maturing, too, says Susan Sabo, the managing principal of the financial institutions group for the professional services firm CliftonLarsonAllen LLP. Many fintechs have upgraded their structure around risk management and controls to ensure they’ll get bank customers. “With the onset of the pandemic, I do think it allowed many fintechs to reset and reinvest, and they did start to build some traction with banks,” Sabo says. 

Still, many banks hesitate to use an alternative to the big three core processors or switch the bulk of their lending and deposit gathering capabilities to a fintech, she says. They’re sticking to fintechs that offer what she calls ancillary solutions — treasury management, credit loss modeling and other types of platforms. But even that has been changing, as evidenced by the success of the fintech nCino, which sells a cloud-based operating system and had its initial public offering in 2020. Sabo recommends using proper due diligence to vet fintech companies. It’s also important to consider cybersecurity, data privacy and contractual issues. And last but not least, consider what can go wrong.

One big hurdle for smaller banks is the cost of using third-party solutions. “Nothing about technology is ever cheap,” Sabo says. “Even things as simple as, ‘We need to refresh all of our hardware,’ becomes a massive investment for a [community] bank. And if you’re held to your earnings per share each quarter, or you’re held to your return to your investors each quarter … you may keep putting it off. Many banks are in a situation where they’re anxious about their technology because they haven’t invested along the way.”

Talent is another large obstacle banks face. Small banks, especially those in rural areas, may struggle to find the staff to make the technology a success. Information technology departments often aren’t equipped with strategic decision-making skills to ensure a fintech partner will meet the bank’s big-picture goals.

And banks that want to leverage data analytics to improve their business will have to hire data scientists and data engineers, says Corey Coscioni, director of strategic alliance and business development at West Monroe. “You’re going to need to build some level of internal capabilities,” he says.

Why Attracting and Retaining Talent is No Longer Good Enough

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Every year, Cornerstone Advisors conducts a survey of community-based financial institution CEOs that asks what their top concerns are. The 2022 survey produced the biggest one-year change we have ever seen. A full 63% of executives identified the ability to attract qualified talent as a key concern, up from just 19% the year before.

No doubt this focus on talent is at least partially the result of the sheer number of new topics requiring industry expertise. Think digital currencies. Embedded finance. BaaS. Buy now pay later. Gen 3 core systems. Artificial intelligence and machine learning. How many of those topics would have been on any FI’s training curriculum two years ago? Yet boards now ask about every one of those topics in terms of the financial institution’s strategy.

However, attracting qualified talent won’t be enough. Every financial institution has knowledge and expertise that can only be developed internally, simply because the knowledge build is so unique to the industry, including:

  • Processes unique to a line of business: There is no school or degree for bank processes, front or back office. And they vary by financial institution.
  • Regulations: The practical application of regulations to specific situations at the institution requires deep “inside” knowledge.
  • Vendors and systems: The vendor stack and roadmaps, and the institution’s databases, make its knowledge requirements unique.

In short, there is no university diploma that can be obtained for many areas of the bank – and, in my opinion, the further you get into the back office, the truer this is.

At Cornerstone Advisors, we’re observing that banks need to focus on “build or buy” of key skills and knowledge for the next generation of leaders and managers. Some thoughts about what we see working:

1. Have a clear list of jobs, skills, and knowledge that will need to be developed versus hired. Everybody will have a different list, of course, but four areas where we consistently see the biggest “build” need are:

    • Payments: While there are certainly people that can come to a bank or credit union with a great deal of understanding about payments, there is the entire back-office component – disputes, fraud, reconciliation, vendor configuration options, et al. – that can be learned only on the job.
    • Commercial credit: An institution’s required credit expertise will depend on its business and niches. For example, knowledge of national environmental lending will be unique from that of import/export letter of credit. Unfortunately, peers and competitors don’t have a deep bench to abscond with.
    • Digital marketing: This is simply too new an area for there to be loads of potential applicants with loads of expertise and experience. Even if execs can find candidates with broad digital marketing experience (they’re out there), they will need to understand the nuances of banking and what will constitute meaningful marketing opportunities in particular client segments.
    • Data analytics: There are a growing number of available people with very strong data skills, but even if hired they will need to come to grips with the complexity of the institution’s data structure.

2. Don’t ignore the importance of the apprenticeship model when building talent. Most leaders at FIs can point to on-the-job training they received early in their careers that has been the basis of their success. The apprenticeship model has worked for centuries and still works well at the modern bank.

3. Balance the in-person need for apprenticeship training with the new realities of remote work demands. In a recent Accenture study, over 60% of employees surveyed felt their productivity had increased due to working at home, and only 13% felt it hadn’t. Whether it is a new hire or re-skilling of an existing employee, the message of “five days in the office” won’t sell. Getting the right amount of face time for development while giving the new generation of stars an appealing work-life balance will be a key challenge for HR groups.

A clear, disciplined, focused plan for development of the next generation of talent is more crucial than ever. There are times when buying talent from elsewhere just won’t be an option due to cost, availability, or the risk of retaining those same people. The good news? Some of the best opportunities might be right in front of you in your existing workforce.

7 Ways Banks Can Benefit From Data Analytics

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A version of this article originally appeared on the KlariVis blog.

There is a pervasive data conundrum throughout the financial services industry: Banks have an inordinate amount of data, but antiquated and siloed solutions are suppressing incredible, untapped opportunities to use it.

Data analytics offer banks seven distinct and tangible benefits; it’s essential that they invest adequate time and resources into finding the right solution.

1. Save Valuable Time
Time is money. Investing in data analytics can streamline operations and saves employees time. The right solution organizes data, eliminates spreadsheets, freeing up the gray space in any organization. Employees can quickly locate what they’re looking for, allowing them to focus on the tasks that are most meaningful to the institution. Instead of organizing and sifting through data, they can spend more time analyzing the information, making strategic decisions and communicating with customers.

2. Secure Compliance, Risk Management Features
Data analytics improves overall bank security. The regulatory environment for financial institutions is complex, and regulatory non-compliance can lead to major fines or enforcement actions for banks. Data analytics incorporates technology into the compliance and risk management processes, improving bank security by reducing the likelihood of human error and quickly detecting potential cases of fraud.

3. Increase Visibility
Data silos in banks are often a result of outdated data solutions. Additionally, granting only a few people or departments access to the full set of data can lead to miscommunication or misinformation. Data analytics solutions, such as enterprise dashboards, give financial institutions the ability to see their full institution clearly. Everyone having access to the same information — whether it be individual branch performance or loan reports —improves customer service, internal communication and overall efficiency.

4. Cut Down on Costs
There is a high cost of bad data. Bad data can be inaccurate, duplicative, incomplete, inaccessible or unusable. Banks that aren’t storing or managing collected data appropriately could be wasting valuable company resources. They could also incur bad data costs through inconclusive, expensive marketing campaigns, increased operational costs that distract employees from important initiatives or customer attrition. By comparison, an updated enterprise data solution keeps employees up-to-date and can reveal new growth opportunities.

5. Create Detailed Customer Profiles
All financial institutions want to know their customers better. Data analytics help generate detailed profiles that reveal valuable information, such as spending habits and channel preferences. Banks can create highly specific segments with these profiles and pinpoint timely cross-selling opportunities. The right data solution makes it easier to gather actionable insights that improve customer experience and increase profitability.

6. Empower Employees and Customer Experience
Empowered employees improve the customer experience; happier customers contribute to empowering employees. A powerful part of this cycle is data analytics. Data analytics produce actionable insights that save employees’ time so they can focus on what’s important. Banks can send timely, data-based relevant messaging, based on customer-expressed preferences and interests.

7. Improve Performance
More time spent connecting with customers allows employees to build a deeper understanding of their financial needs and ultimately improve the bank’s performance. The right data analytics solution leads to a more productive and profitable financial institution. In this increasingly competitive financial landscape, employee and customer experience are vital to every financial institution. Customers expect seamless communication and digital experiences that are secure and intuitive; employees appreciate work environments where their work contributes to its overall success. Using data to its fullest potential allows banks to make better strategic decisions, identify and act upon growth opportunities, and focus on their customers.

What Banks Can Learn About Customers from 50,000 Chatbot Searches

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Covid-19 has increased usage of digital banking services and tools, including live chat, video chat and chatbots.

While live chat and video chat offer a one-to-one conversation directly with your customers, chatbots provide 24-hour service, instant answers and the ability to scale without the need for human intervention. Relatively new channel to the banking world, the promise of chatbots seems endless: answering every question and automating related tasks, quickly and efficiently. How can banks best leverage the promise of this opportunities to better and more efficiently serve customers?

To truly answer that question, we need to understand how customers interact with chatbots, how that varies from known digital behavior, like search and navigation, and how can those insights be turned into reality.

So we decided to analyze more than 50,000 banking chatbot interactions. What we uncovered revealed some very interesting insights about customer behavior and what it will take to make that promise a reality.

It turns out that customers interactions with chatbots are very similar to human interactions:

  • They typically typed 11.24 words, on average, compared to with 1.4 words typed into a banking website search bar. Chatbot interactions are conversational. Customers ask questions like “Can I Have My Stimulus Debit Card Balance Deposited to My Account” or making statements like “I need to change my address.”
  • Almost 94% of questions asked were completely unique. While customers may ask the same type of question — “What is my routing number?” versus “What is your routing number?” versus “What is the routing number” —how they phrase the question is almost always unique.
  • A fifth of all interactions started with “I need,” “I want” or “I am” — another indication of the conversational approach that bank customers take with chatbots. Unlike a search function, where typically they would use shorter phrases like “refinance” or “refinance car,” they make statements or ask questions: “I am looking to refinance my auto loan” or “I want to refinance my auto loan.”
  • Fifteen percent of interactions included the word “how.” This is another indication that customers ask chatbots questions or looking for help completing tasks like “How do you use Zelle?” or “How does a home equity loan work?”
  • Fourteen percent of all interactions began with “Hi,” “Hey” or “Hello.” And who said that bots don’t have feelings?

Chatbot adoption and usage will only continue to grow. Like all newer channels, it will require fine-tuning along the way, using insights and analysis to effectively interpret what customers are looking for, and deliver back relevant responses that point them in the right direction.

This starts with analytics and data. As data sets grow with more usage, they will reveal insights on how customers interact with chatbots, what they are looking to do and how that changes over time. This will feed the data set used to power the chatbot’s AI — both natural language processing (the ability to interpret what the customer is asking or looking for) as well as the sentiment analysis (whether the customer is happy or frustrated). Analysis will be required to learn and understand the nuances of what customers are asking when presented with phrases like this actual query from our dataset: “Hi. What is the safest way to prove documents of account balance when applying to living in an apartment complex?” Banks and/or the chatbot vendors will need to monitor the training the chatbot, including recognizing customer frustration and offering up logical next steps — like “It looks like you’re frustrated, can we transfer you to an agent?” as needed.

The analytics and data will also provide the map of the information that needs to be developed and updated to deliver answers that customers need. Given that 93.8% of questions that customers ask are unique, having the right knowledge will be critical. Sometimes this might be a simple answer (“What is my routing number?”) and sometimes it might require decision trees that offer options (understanding if an auto loan is for a new or used vehicle to get the customer one step closer to conversion).

Banks have a great opportunity to make chatbots the 24/7 tool that improves customer experience, reduces support costs and drives digital adoption. But it will take a commitment to the analysis and ongoing optimization of knowledge to truly become a reality. 

Next time you start you interact with a chatbot, start with hello — I’ve heard they appreciate it!

Build Versus Buy Considerations for Data Analytics Projects

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It is the age-old question: buy versus build? How do you know which is the best approach for your institution?

For years, bankers have known their data is a significant untapped asset, but lacked the resources or guidance to solve their data challenges. The coronavirus crisis has made it increasingly apparent that outdated methods of distributing reports and information do not work well in a remote work environment.

As a former banker who has made the recent transition to a “software as a service” company, my answer today differs greatly from the one I would have provided five years ago. I’ve grown in my understanding of the benefits, challenges, roadblocks and costs associated with building a data analytics solution.

How will you solve the data conundrum? Some bank leaders are looking to their IT department while other executives are seeking fintech for a solution. If data analytics is on your strategic roadmap, here are some insights that could aid in your decision-making. A good place to start this decision journey is with a business case analysis that considers:

  • What does the bank want to achieve or solve?
  • Who are the users of the information?
  • Who is currently creating reports, charts and graphs in the institution today? Is this a siloed activity?
  • What is the timeline for the project?
  • How much will this initiative cost?
  • How unique are the bank’s needs and issues to solve?

Assessing how much time is spent creating meaningful reports and whether that is the best use of a specific employee’s time is critical to the evaluation. In many cases, highly compensated individuals spend hours creating reports and dashboards, leaving them with little time for analyzing the information and acting on the conclusions from the reports. In institutions where this reporting is done in silos across multiple departments and business units, a single source of truth is often a primary motivator for expanding data capabilities.

Prebuilt tools typically offer banks a faster deployment time, yielding a quicker readiness for use in the bank’s data strategy, along with a lower upfront cost compared to hiring developers. Vendors often employ specialized technical resources, minimizing ongoing system administration and eliminating internal turnover risk that can plague “in house” development. Many of these providers use secure cloud technology that is faster and cheaper, and takes responsibility for integration issues.  

Purchased software is updated regularly with ongoing maintenance, functionality and new features to remain competitive, using feedback and experiences gained from working with institutions of varying size and complexity. Engaging a vendor can also free up the internal team’s resources so they can focus on the data use strategy and analyzing data following implementation. Purchased solutions typically promotes accessibility throughout the institution, allowing for broad usage.

But selecting the criteria is a critical and potentially time-consuming endeavor. Vendors may also offer limited customization options and pose potential for integration issues. Additionally, time-based subscriptions and licenses may experience cost growth over time; pricing based on users could make adoption across the institution more costly, lessening the overall effectiveness.

Building a data analytics tool offers the ability to customize and prioritize development efforts based on a bank’s specific needs; controllable data security, depending on what tools the bank uses for the build and warehousing; and a more readily modifiable budget.

But software development is not your bank’s core business. Building a solution could incur significant upfront and ongoing cost to develop; purchased tools appear to have a large price tag, but building a tool incurs often-overlooked costs like the cost of internal subject matter experts to guide development efforts, ongoing maintenance costs and the unknowns associated with software development. These project may require business intelligence and software development expertise, which can carry turnover risk if institutional knowledge leaves the bank.

Projects of this magnitude require continuous engagement from management subject matter experts. Bankers needed to provide the vision and banking content for the product — diverting management’s focus from other responsibilities. This can have a negative impact on company productivity.

Additionally, “in-house” created tools tend to continue to operate in data silos whereby the tool is accessible only to data team. Ongoing development and releases may be difficult for an internal team to manage, given their limited time and resources along with changing business priorities and staff turnover.

The question remains: Do you have the bandwidth and talent at your bank to take on a build project? These projects typically take longer than expected, experience budget overruns and often do not result in the desired business result. Your bank will need to make the choice that is best for your institution.

Using Data Platforms to See Customers

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Customers leave behind valuable breadcrumbs about their interests, needs and intentions across their financial lives.

What’s their current financial health? Are they shopping for a new credit card? Even: Are they considering switching to a competitor?

Unfortunately, this wealth of insights is more-than-likely locked away across a series of legacy, on-site systems, stuck in siloed data warehouses and generally difficult to access due to antiquated reporting systems. Understanding and acting on customer signals has become more important in recent months as customers seek financial partners that understand their unique needs. What does it take for a bank to unlock this treasure trove of data and insights? More often than not, a customer data platform (or CDP) can help banks take an important step in making this a reality and craft a 360-degree view of their customers.

I spoke with Brian Knollenberg, vice president of digital marketing and analytics at Tukwila, Washington-based BECU, about his recent experience of setting up a CDP for one of the country’s largest credit unions in the country. 

The Need for CDP
When Knollenberg joined the $22 billion credit union, he saw that creating a marketing performance dashboard using slow-batch processing across multiple systems took 12 manual hours to produce. As a result, the data stakeholders needed to make key decisions was a week out of date by the time they received it — much less take action on it.

This speed-to-value lag wasn’t limited to just marketing dashboards; it was just one example teams encountered when trying to access timely customer data across legacy systems. His team recognized that the organization needed current data, individualized for each customer, to make timely decisions. They also needed a way to easily syndicate this across critical customer and stakeholder touchpoints. 

Knollenberg also recognized his team’s expertise was better suited to modifying processes rather than building a robust enterprise-grade tool that could ingest and process terabytes of data in near-real time. He needed a solution to transform this data hindrance into an asset, and looked for a partner with direct experience in tackling these challenges to streamline implementation.

CDP Benefits
Implementing a CDP has extended the BECU team’s ability to tackle more difficult data challenges. This included building out performance dashboards that update every 24 hours, personalized customer communications and the ability to modeling member financial health.

This last use case empowers BECU to aggregate a score based on behaviors, transactions, and trends to identify which members could benefit from proactive outreach or help. He said financial health scoring has been extremely helpful during the coronavirus pandemic to identify potential recipients of proactive outreach and assistance. Having this information readily available enables marketing, customer service and even product teams to create bespoke experiences for their members and make informed business decisions — like offering a lower rate card to a member showing large carried balances with an outside card provider.

Lessons Learned
Before tackling any new data program, Knollenberg recommends companies first identify the overall effort versus impact. He finds that while companies often invest ample time and effort into developing comprehensive strategy and goals, they often miss when planning for the execution realities to properly implement them. Spend time scaling up your bank’s execution capabilities, determine how you’ll realistically measure potential impact and test-drive product solutions via a robust proof of concept.

The best financial brands know that putting their customers first will result in returns. Building out a customer data platform for your bank can unlock powerful new insights and opportunities to engage with your customers, if done right. As you start on this journey, make sure to identify what specific use cases are most impactful for your business, and find the right software partner that will work with you to execute it properly. Once unlocked, your bank will be able to service customers at a truly personalized level and drive a greater share of wallet.

Data In The Best, And Worst, Of Times

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Helping their community and delivering personalized service is the foundational differentiation of every community bank. Now more than ever, customers expect that their community bank understands them and is looking out for their best interests.

Customers are communicating with their banks every day through their transactions — regardless if they are mobile, in person or online, each interaction tells a story. Are you listening to what they’re telling you? Whether your bank is navigating through today’s COVID-19 crisis or operating in the best of times, data will be key to success today and in the future.

Business intelligence to navigate daily operations is hard to come by on a good day, much less when things are in a pandemic disarray. Many bankers are working remotely for the first time and find themselves crippled by the lack of access to actionable data. A robust data analytics tool enables employees at all levels to efficiently access the massive amounts of customer, market, product, trend and service data that resides in your core and ancillary systems. Actionable data analytics can empower front-line bankers and risk managers to make data-driven decisions by improving and leveraging insight into the components that affect loan, deposit and revenue growth. Additionally, these tools often do the heavy lifting, resulting in organizational efficiencies that allow your bankers and executives to focus on strategic decision-making — not managing cumbersome data and reporting processes.

A tool that aggregates transformative data points from various siloed systems and makes them readily available and easy to interpret allows your management team to be better prepared to proactively manage and anticipate the potential impact of a crisis. This positions your bank to offer products and services that your customers need, when they need them.

But most community banks have not implemented a data analytics solution and as such, they  must consider how to manually generate the information needed to monitor and track customer behaviors to assist them in navigating this crisis. Below are a few potential early warning indicators to monitor and track as your bank navigates the current coronavirus crisis so you can proactively reach out to customers:

  • Overdrafts, particularly for customers who have never overdrawn.
  • Missing regular ACH deposits.
  • Past due loans, particularly customers who are past due for the first time.
  • Line of credit advances maxing out.
  • Lines of credit that cannot meet the 30-day pay-down requirement.
  • Declining deposit balances.
  • Large deposit withdrawals.
  • Businesses in industries that are suffering the most.

If your community bank is one of the many that are proactively assisting customers during this pandemic, make sure you are tracking data in a manner that allows you to clearly understand the impact this crisis is having on your bank and share with your community how you were able to help your customers during this critical time. Some examples include:

  • Paycheck Protection Loan Program details: number of applications received, processed and funded; amount forgiven; cost of participating for the bank; customer versus non-customer participation, impact on lending team, performance.
  • Customer assistance with online banking: How did you help those who are unfamiliar with online banking services? How many did you assist?
  • Loan modifications, including extensions, deferments, payment relief, interest-only payments and payment deferrals.
  • Waived fees and late charges.
  • Emergency line of credits for small business customers.

Having easy access to critical customer information and insights has never been more important than it is today, with the move to remote work for many bankers and rapidly changing customer behaviors due to the economic shutdown. Customers are making tough choices; with the right data in your bankers’ hands, you will have the ability to step up and serve them in ways that may just make them customers for life.

Making Strategic Decisions With The Help of Data Analytics

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Banks capture a variety of data about their customers, loans and deposits that they can harness in visually effective ways to support strategic decision-making. But to do this successfully, they must have leadership commit to provide the funding and human resources to improve data collection and management.

“Bad data or poor data quality costs U.S. businesses about $3 trillion annually, and breeds bad decisions made from having data that is just incorrect, unclean, and ungoverned,” said Ollie East, consulting director of advanced analytics and data engineering at Baker Tilly.

Companies generally have two types of data: structured and unstructured. Structured data is information that can be organized in tables or a database: customer names, age, loan balances and interest rates. Unstructured data is information that exists in written reports, online customer reviews or notes from sales people. It does not fit into a standard database and is not easily relatable to other data.

“If data analytics is the engine, then data is the gasoline that powers it,” East said. “Everything starts with data management: getting and cleaning data and putting it into a format where it can be used, governed, controlled and treated as an asset.”

A maturity model for data analytics progresses from descriptive to prescriptive uses for the information. The descriptive level answers questions like, “What happened?” The diagnostic level answers, “Why did it happen?” The predictive level looks at “What will happen?” Finally, at the prescriptive level, a company can apply artificial intelligence, machine learning or robotics on large sets of structured and unstructured data to answer “How can I make it happen?”

Existing cloud-based computing technology is inexpensive. Companies can import basic data and overlay a Tableau or similar dashboard that creates a compelling visual representation of data easily understood by different management teams. Sean Statz, senior manager of financial services, noted that data visualization tools like Tableau allows banks to create practical visual insights into their loan and deposit portfolios, which in turn will support specific strategic initiatives.

To do a loan portfolio analysis, a simple extraction of a bank’s data at a point in time can generate a variety of visual displays that demonstrate the credit and concentration risks. Repetitive reporting allows the bank to analyze trends like the distribution of credit risk among different time periods and identify new pricing strategies that may be appropriate. Tableau can create a heat map of loans by balance, so bankers can quickly observe the interest rates on different loans. Another view could display loss rates by risk rating, which can help a bank determine the real return or actual yield it is earning on its loans.

Statz said sophisticated analytics of deposit characteristics will help banks understand customer demographics, and adjust their strategies to grow and retain different types of customers. Bank can use this information in their branch opening and closing decisions, or prepare for CD maturities with questions like, “When CDs roll over, what products will we offer? If we retain all or only half of CD customers, but at higher interest rates, how does that affect cost of funds and budget planning?”

Data analytics can help banks undergo more sophisticated key performance indicator comparisons with their peers, not just at an aggregate national or statewide level, but even a more narrow comparison into specific asset sizes.

Banks face many challenges in effective data analytics, including tracking the right data, storing and extracting it, validating it and assigning resources to it correctly. But the biggest challenge banks need to tackle is determining if they have the necessary data to tackle specific problems. For example, the Financial Accounting Standards Board’s new current expected credit loss (CECL) standards require banks to report lifetime credit losses. If banks do not already track the credit quality characteristics they will need for CECL, they need to start capturing that data now.

Banks often store data on different systems: residential real estate loans on one system, commercial loans on another. This makes extracting the data in a way that supports data visualization like Tableau difficult. They must also validate the data for accuracy and identify any gaps in either data collection or inputting through the system. They also need to ensure they have the human resources and tools to extract, scrub and manipulate essential data to build out a meaningful analytic based on each data type.

The key to any successful data analytics undertaking is a leadership team that is committed to developing this data maturity mindset, whether internally or with help from a third party.

Getting your Digital Growth Strategy Right from the Start

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Digital growth is only as good as the metrics used to measure it.

Growth is one of an executives’ most important responsibilities, whether that comes from the branch, through mergers and acquisitions or digital channels. Digital growth can be a scalable and predictable way for a bank to grow, if executives can effectively and accurately measure and execute their efforts. By using Net Present Value as the lens to evaluate digital marketing, a bank’s leadership team can make informed decisions on the future of the organization.

Banks need a well-thought-out digital growth strategy because of the changing role of the branch and big bank competition. The branch used to spearhead an institution’s growth efforts, but that is changing as branch sales decline. At the same time, the three biggest banks in the country rang up 50% of the new deposit account openings last year (even though they have only 24% of branches) as they lure depositors away from community banks, given regulators’ prohibition on acquisition.

Physical Branch Decline chart.pngImage courtesy of Ron Shevlin of Cornerstone Advisors

Even in the face of these changes, many institutions are nervous about adopting an aggressive digital growth plan or falter in their execution.

A typical bank’s digital marketing efforts frequently rely on analytics that have been designed for another business altogether. They may want to place a series of ads on digital channels or social media sites, but how will they know if those work? They may use data points such as clicks or views to gauge the effectiveness of a campaign, even if those metrics don’t speak to the conversion process. They will also track metrics such as the number of new accounts opened after the start of a campaign or relate the number of clicks placed in new accounts.

But this approach assumes a direct link between the campaign and the new customers. In addition, acquisition and data teams will spend valuable time creating reports from disparate data sources to get the proper measurement, instead of analyzing generated reports to come up with better strategies.

Additionally, a bank’s CFO can’t really measure the effectiveness of an acquisition campaign if they aren’t able to see how the relationships with these new customers flourish and provides value to the institution. The conversion is not over with a click — it’s continuous.

This leads to another obstacle to measuring digital growth efforts: communication. Banks use three internal teams to generate growth: finance to fund the efforts; marketing to execute and measure it; and operations to provide the workflow to fulfill it.

Each team measures and expresses success differently, and each has its inherent shortcomings. Finance would like to know the cost and profitability of the new deposits generated, to assess the efficiency of the spend. Marketing might consider clicks or views. Operations will report on the number of accounts opened, but do not know definitively if existing workflows support the market segmentation that the bank seeks.

There is not a single group of metrics shared by the teams. However, the CEO will be most interested in cost of acquisition, the long-term profitability of the accounts and the return on investment of the total efforts.

But it’s now possible for banks to see the full measurement of their digital campaigns, from the disbursement of funds provided by the finance group to the success of these campaigns, in terms of deposits raised and net present value generated. These ads entice prospects into the account origination funnel, managed by operations, who open accounts and deposit initial funds. Those new customers then go through an onboarding process to switch their direct deposits and bill pay accounts. The new customer’s engagement can be measured six to 12 months later for value, and tied back to the original investment that brought them in the first place.

Bank leadership needs to be able to make decisions for the long-term health of their organizations. CEOs tell us they have a “data problem” when it comes to empowering their decisions. For this to work, the core system, the account origination funnel and the marketing channels all need to be tied together. This is true Integrated Value Measurement.