Five Digital Banking Initiatives for Second Half of 2020

As the calendar nears the midpoint of 2020 and banks continue adjusting to a new normal, it’s more important than ever to keep pace with planned initiatives.

To get a better understanding of what financial institutions are focusing on, MX surveyed more than 400 financial institution clients for their top initiatives this year and beyond. We believe these priorities will gain even more importance across the industry.

1. Enabling Emerging Technologies, Continued Innovation
Nearly 20% of clients see digital and mobile as their top initiatives for the coming years. Digital and mobile initiatives can help banks limit the traffic into physical locations, as well as reduce volume to your call centers. Your employees can focus on more complex cases or on better alternatives for customers.

Data-led digital experiences allow you to promote attractive interest rates, keep customers informed about upcoming payments and empower them to budget and track expenses in simple and intuitive ways. 

2. Improving Analytics, Insights
Knowing how to leverage data to make smarter business decisions is a key focus for financial institutions; 22% of our clients say this is the top initiative for them this year. There are endless ways to leverage data to serve customers better and become a more strategic organization.

Data insights can indicate customers in industries that are at risk of job loss or layoffs or the concentration of customers who are already in financial crisis or will be if their income stops, using key income, spending and savings ratios. Foreseeing who might be at risk financially can help you be proactive in offering solutions to minimize the long-term impact for both your customers and your institution.

3. Increasing Customer Engagement
Improving and increasing customer engagement is a top priority for 14% of our clients. Financial institutions are well positioned to become advocates for their customers by helping them with the right tools and technologies.

Transaction analytics is one foundational tool for understanding customer behavior and patterns. The insights derived from transactions and customer data can show customers how they can reduce unnecessary spending through personal financial management and expert guidance.

But it’s crucial to offer a great user experience in all your customer-facing tools and technologies. Consumers have become savvier in the way they use and interact with digital channels and apps and expect that experience from your organization. Intuitive, simple, and functional applications could be the difference between your customers choosing your financial institution or switching to a different provider.

4. Leveraging Open Banking, API Partnerships
Open banking and application programming interfaces, or APIs, are fast becoming a new norm in financial services. The future of banking may very well depend on it. Our findings show that 15% of clients are considering these types of solutions as their main initiative this year. Third-party relationships can help financial institutions go to market faster with innovative technologies, can strengthen the customer experience and compete more effectively with big banks and challengers.

Financial institutions can leverage third parties for their agile approach and rapid innovation, allowing them to allocate resources more strategically, expand lines of business, and reduce errors in production. These new innovations will help your financial institution compete more effectively and gives customers better, smarter and more advanced tools to manage their financial lives.

But not all partnerships are created equally. The Office of the Comptroller of the Currency recently released changes surrounding third-party relationships, security and use of customers’ data, requiring financial institutions to provide third-party traffic reports of companies that scrape data. Right now, the vast majority of institutions only have scrape-based connections as the means for customers to give access to their data — another reason why financial institutions should be selective and strategic with third-party providers.

5. Strategically Growing Customer Acquisition, Accounts
As banking continues to transform, so will the need to adapt including the way we grow. Nearly 30% of our clients see this as a primary goal for 2020 and beyond. Growth is a foundational part of success for every organization. And financial institutions generally have relied on the same model for growth: customer acquisitions, increasing accounts and deposits and loan origination. However, the methods to accomplish these growth strategies are changing, and they’re changing fast.

Right now, we’re being faced with one of the hardest times in recent history. The pandemic has fundamentally changed how we do business, halting our day-to-day lives. As we continue to navigate this new environment, financial institutions should lean on strategic partnerships to help fill gaps to facilitate greater focus on their customers.

Helping Customers When They Need It Most

Orvin Kimbrough intimately understands the struggles shared by low-to-moderate income consumers. Raised in low-income communities and the foster care system, he also worked at the United Way of Greater St. Louis for over a decade before joining $2.1 billion Midwest BankCentre as CEO in January 2019. “[Poverty costs] more for working people,” he says. “It’s not just the financial cost; it’s the psychological cost of signing over … the one family asset you have to the pawn shop.”

His experiences led him to challenge his team to develop a payday loan alternative that wouldn’t trap people in a never-ending debt cycle. The interest rate ranges from 18.99% to 24.99%, based on the term, amount borrowed (from $100 to $1,000) and the applicant’s credit score. Rates for a payday loan, by comparison, range in the triple digits.

The application process isn’t overly high-tech, as applicants can apply online or over the phone. The St. Louis-based bank examines the customer’s credit score and income in making the loan decision; those with a credit score below 620 must enroll in a financial education class provided by the bank.

Industry research consistently finds that many Americans don’t have money saved for an emergency — a health crisis or home repair, for example. When these small personal crises occur, cash-strapped consumers have limited options. Few banks offer small-dollar loans, dissuaded by profitability and regulatory constraints following the 2008-09 financial crisis.

If the current recession deepens, more consumers could be looking for payday loan alternatives. Regulators recently encouraged financial institutions to offer these products, issuing interagency small-dollar lending principles in May that emphasize consumers’ ability to repay. 

Everybody needs to belong to a financial institution if you’re going to be financially healthy and achieve your financial aspirations,” says Ben Morales, CEO of QCash Financial, a lending platform that helps financial institutions automate the underwriting process for small-dollar loans.

QCash connects to a bank’s core systems to automate the lending process, using data-driven models to efficiently deliver small-dollar loans. The whole process takes “six clicks and 60 seconds, and nobody has to touch it,” Morales says. QCash uses the bank’s customer data to predict ability to repay and incorporates numerous factors — including cash-flow data — into the predictive models it developed with data scientists. It doesn’t pull credit reports.

Credit bureau data doesn’t provide a full picture of the customer, says Kelly Thompson Cochran, deputy director of FinRegLab and a former regulator with the Consumer Financial Protection Bureau. Roughly a fifth of U.S. consumers lack credit history data, she says, which focuses on certain types of credit and expenses. The data is also a lagging indicator since it’s focused on the customer’s financial history.

In contrast, cash flow data can provide tremendous value to the underwriting process. “A transaction account is giving you both a sense of inflows and outflows, and the full spectrum of the kind of recurring expenses that a consumer has,” says Cochran.

U.S. Bancorp blends cash flow data with the applicant’s credit score to underwrite its “Simple Loan” — the only small-dollar loan offered by a major U.S. bank. The entire process occurs through the bank’s online or mobile channels, and takes just seven minutes, according to Mike Shepard, U.S. Bank’s senior vice president, consumer lending product and risk strategy. Applicants need to have a checking account with the bank for at least three months, with recurring deposits, so the bank can establish a relationship and understand the customer’s spending behavior.

“We know that our customers, at any point in time, could be facing short-term, cash-flow liquidity challenges,” says Shepard. U.S. Bank wanted to create a product that was simple to understand, with a clear pricing structure and guidelines. Customers can borrow in $100 increments, from $100 to $1,000, and pay a $6 fee for every $100 borrowed. U.S. Bank lowered the fee in March to better assist customers impacted by the pandemic; prior to that the fee ranged from $12 to $15.

Since the loan is a digital product, it’s convenient for the customer and efficient for the bank.

Ultimately, the Simple Loan places U.S. Bank at the center of its customers’ financial lives, says Shepard. By offering a responsible, transparent solution, customers “have a greater perception of U.S. Bank as a result of the fact that we were able to help them out in that time of need.”

Using Data Platforms to See Customers

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.

Three Financial Institutions, Three Ways to Improve Operations

cinchy.pngIt can be hard for banks to invest in changing their operations, especially when new systems or vendors are involved. Unlike loans or accounts, assigning a price tag to the value banks get from improving their operations is far from straightforward.

With this in mind, Bank Director looked at how three fintechs — Cinchy, Empyrean Solutions and INETCO Systems Limited — have helped three financial institutions streamline data sharing, financial modeling and real time transaction modeling.

Cinchy, a data collaboration platform that manages data as a network and enables banks to build their own business applications, won Best Solution for Improving Operations at Bank Director’s 2020 Best of FinXTech Awards in May. Modeling platform Empyrean Solutions and transactions intelligence firm INETCO Systems Limited were finalists for the category.

Cinchy: A Tool to Build Tools
National Bank of Canada wanted to custom build a tool that would integrate and easily share internal information and documentation with data scientists and business intelligence teams to support its big data efforts. Some solutions on the market were focused more on governance, rather than data pooling and aggregation — which was like “buying a plane to get a bicycle,” says Sebastien Beaulieu, senior manager of data, business intelligence and analytics strategy at the bank.

Enter Cinchy. Cinchy’s customization and flexibility gave various internal stakeholders a way to collaborate within the bank’s ecosystem to build the right tool. Beaulieu calls it “play dough.”

The Cinchy tool sits on top of the bank’s SQL (Structured Query Language) server. Implementation took six months, but Beaulieu says it could’ve taken as little as three.

While he wasn’t authorized to share specific performance metrics, he says there has been “tremendous value” from having a centralized, federated data lake. It used to take up to 200 days to integrate a new data source into the bank’s previous Oracle tool; with Cinchy, it now takes 10 days.

Empyrean Solutions: Modeling Everything
Pinnacle Financial Partners was preparing to grow over $10 billion in assets and knew its existing asset-liability management (ALM) platform wouldn’t meet the heightened regulatory expectations that come with crossing the threshold.

It learned about Empyrean Solutions from the recommendation of several larger peers. Today, the $29 billion bank uses the modeling platform for “just about everything,” says Brian Gilbert, the bank’s asset liability manager.

The Nashville, Tennessee-based bank uses Empyrean to model ALM, credit, funds transfer pricing, non-maturity deposits, contingency liquidity and net interest margin forecast. The bank is also using it to calculate its allowance under the current expected credit loss model, or CECL. Running 17 scenarios on the platform now only takes 17 minutes, he says. Such pulls can take hours to run at other banks; the time saved allows him to focus on the inputs in each situation and create multiple scenarios.

Gilbert couldn’t devote continuous full days to the implementation but estimated that a dedicated team could do it in a week. The platform doesn’t connect to a bank’s core; instead, users load data and financial information directly into it. These data sets can then be made available for any modeling, reducing the amount of raw extracts that Gilbert needs to perform.

“It’s hard for me to say how many more people we would need to have on my team if we didn’t have Empyrean. We would need more than four people to run interest rate forecasting,” he says.

INETCO Systems Limited: ATM Oversight
Boeing Employees’ Credit Union, or BECU, relies on its fleet of ATMs to deliver a large percentage of its financial services to members. It’s not unusual for one of its ATMs to have 14,000 transactions a month. So it’s a big deal when one goes offline and needs to be repaired or serviced, says Shirley Taylor, digital channel manager at the Tukwila, Washington-based credit union.

The $22 billion credit union wasn’t finding out about ATM outages or errors in a reliable or timely manner, in part because machines would occasionally go offline. Its ATM vendor introduced the credit union to INETCO, an operations intelligence platform that monitors transactions.

The credit union liked that the platform created alerts when there was an interruption in an ATM’s normal activity or when transactions failed to process — signs of a problem. Those alerts mean that fewer ATMs stay out of commission, allowing members to use its machines instead of sending them elsewhere. The fintech also generates potential fraud alerts and helps Taylor generate regular usage reports much faster. What used to take four to six weeks and involved reaching out to three other colleagues for data now takes an hour, she says.

Changing bank operations is never easy. But Cinchy, Empyrean Solutions and INETCO Systems Limited show how bankers can improve their institutions, saving employees valuable time and reducing the number needed to conduct essential operations.

Practical AI Considerations for Community Banks

A common misconception among many community bankers is that it isn’t necessary to evaluate (or re-evaluate for some) their use of artificial intelligence – especially in the current market climate.

In reality, these technologies absolutely need a closer look. While the Covid-19 crisis and Paycheck Protection Program difficulties put a recent spotlight on outdated financial technology, slow technology adoption is a long-standing issue that is exacerbating many concerning industry trends.

Over the last decade, community banks have faced massive disruption and consolidation — a progression that is likely to continue. It’s imperative that bank executives take a clear-eyed look at how advanced technologies such as AI can support their business objectives and make them more competitive, while gaining a better understanding of the requirements and risks at play.

Incorporating AI to Elevate Existing Business Processes
This may seem like a contrarian view, but banks do not need a specific, stand-alone AI strategy. The value of AI is its ability to improve upon existing structures and processes. Leadership teams need to be involved in the development process to identify opportunities where AI can tangibly drive business objectives, and manage expectations around the resources necessary to get the project up and running.

For example, community banks should review how AI can automate efficiencies into their existing compliance processes — particularly in the areas of anti-money laundering and Bank Secrecy Act compliance. This application of AI can free up manpower, reduces error rates and help banks make informed decisions while better serving their customers.

It’s necessary to have a strong link between a bank’s digital transformation program and AI program. When properly incorporated, AI helps community financial institutions better meet rising customer expectations and close the gap with large financial institutions that have heavily invested in their digital experiences.

Practical Steps for Incorporating AI
Once a bank decides the best path forward for implementing AI, there are a few technical and organizational steps to keep in mind:

Minimizing Technical Debt and “Dirty Data”: AI requires vast amounts of data to function. “Dirty data,” or information containing errors, is a real possibility. Additionally, developers regularly make trade-offs between speed and quality to keep projects moving, which can result in greater vulnerability to crashes. Managing these deficiencies, “or technical debt,” is crucial to the success of any AI solution. One way to minimize technical debt is to ensure that both the quantity and quality of data taken in by an AI system are carefully monitored. Organizations should also be highly intentional about the data they collect.More isn’t always better.

Minimizing technical debt and dirty data is also key to a smooth digital transformation process. Engineers can add value through new and competitive features rather than spending time and energy addressing errors — or worse, scrapping the existing infrastructure altogether.

Security & Risk Management: Security and risk management needs to be top-of-mind for community bankers any time they are looking to deploy new technologies, including leveraging AI. Most AI technologies are built by third-party vendors rather than in-house. Integrations can and likely will create vulnerabilities. To ensure security and risk management are built into your bank’s operating processes and remain of the highest priority, chief security officers should report directly to the CEO.

Managing risks that arise within AI systems is also crucial to avoid any interruptions. Effective risk management ties back to knowing exactly how and why changes affect the bank’s system. One common challenge is the accidental misuse of sensitive data or data being mistakenly revealed. Access to data should be tightly controlled by your organization.

Ongoing communication with employees is important since they are the front line when it comes to spotting potential issues. The root cause of any errors detected should be clearly tracked and understood so banks can make adjustments to the model and retrain the team as needed.

Resource Management: An O’Reilly Media survey from 2018 found that company culture was the leading impediment to AI adoption in the financial services sector. To address this, leaders should listen to and educate employees within each department as the company explores new applications. Having a robust change management program — not just for AI but for any digital transformation journey — is absolutely critical to success. Ongoing education around AI efforts will help garner support for future initiatives and empower employees to take a proactive role in the success of current projects.

At a glance, implementing AI technologies may seem daunting, but adopting a wait-and-see approach could prove detrimental — particularly for community banks. Smaller banks need to use every tool in their toolkit to survive in a consolidating market. AI poses a huge opportunity for community banks to become more innovative, competitive and prosperous.

Preparing to Be There for Your Community

The fallout from COVID-19 will likely take some time to stabilize. The personal and social costs are already significant, and neither is independent of economic and business disruptions.

Especially impacted are the businesses on Main Streets everywhere that are served by community banks. Community banks will be essential to any recovery, so it is important that they take steps now to ensure they’re positioned to make a difference.

The Challenge Of A “New Normal”
Financial markets were in “price discovery” mode this spring, but that phase is unlikely to last for long. If Treasury rates rise from their current levels, banks are likely to do well with their traditional models. But if they remain low, and spreads eventually stabilize to 2019 levels, nearly every institution will encounter pressure that could undermine their efforts to be a catalyst for Main Street’s recovery.

Bank Director’s recent piece “Uncharted Territory” warned that the experience of past financial crises could mislead bankers into complacency. Last time, dramatic reductions in funding costs boosted net interest margins, which helped banks offset dramatically higher loan losses. The difference today is that funding costs are already very low — leaving little room for similar reductions.

Consider asset yields. Even without significant credit charge-offs, community bank profitability could face headwinds. Community banks entered 2020 with plenty of fuel to support their thriving Main Streets. Their balance sheets had been established for a Treasury rate environment that was 100 basis points higher than today’s. If rates settle here for the next couple of years and existing assets get replaced at “new normal” levels, yields will fall and net interest margins, or NIMs, could take a hit.

Banks could have trouble “being there” for their communities.

Where do the current assets on banks’ balance sheets come from? They were added in 2018, 2019 and the first quarter of 2020. If we assume a fixed rate loan portfolio yields somewhere around 300 basis points over the 5-year swap rate at closing (which averaged about 1.75% over 2019), and floating rates loans yield somewhere around 50 basis points over prime day to day, we can estimate banks’ first quarter loan yields at perhaps 4.75% fixed-rate and 5.25% prime-based.

Prime-based yields have already dropped for the second quarter and beyond: They are now earning 3.75%. Fixed-rate loans continue to earn something like 4.75%, for now.

Banks that can quickly reduce funding costs might, in fact, see a short-term bump in net interest margins. If they can stave off provision expenses, this might even translate into a bump in profitability. But it will not last.

If Treasury rates remain at these historic lows and spreads normalize to 2019 levels, current balance sheets will decay. Adjustments today, before this happens, are the only real defense.

Banks’ fixed rate loans will mature or refinance at much lower rates — around 3.50%, according to our assumptions. Eventually, banks that enjoyed a 3.50% NIM in 2019 will be looking at sustained NIMs closer to 2.50%, even after accounting for reduced funding costs, if they take no corrective steps today. It will be difficult for these banks to “be there” for Main Street, especially if provision expenses begin to emerge.

Every community bank should immediately assess its NIM decay path. How long will it take to get to the bottom? This knowledge will help scale and motivate immediate corrective actions.

For most banks, this is probably a downslope of 18 to 30 months. For some, it will happen much more rapidly. The data required may be in asset and liability management reports. Note that if your bank is using year-end reports, the intervening rate moves mean that the data in the “100 basis points shock” scenario from that report would represent the current rates unchanged “baseline.” Reports that do not run income simulations for four or more years will also likely miss the full NIM contraction, which must be analyzed to incorporate full asset turnover and beyond.

Times are hectic for community banks, but in many cases commissioning a stand-alone analysis, above and beyond standard asset-liability compliance requirements, is warranted.

Then What?
The purpose of analyzing a bank’s NIM timeline is not to determine when to start taking action, but to correctly size and scope the immediate action.  All the levers on the balance sheet— assets, liabilities, maybe even derivatives — must be coordinated to defend long-term NIM and the bank’s ability to assist in Main Street’s recovery.

The Small Business Administration’s Paycheck Protection Program lending is fully aligned with the community bank mission, but it is short term. Banks must also plan for sustainable net interest income for three, four and five years into the future, and that planning and execution should take place now. The devised NIM defense strategy should be subjected to the same NIM decay analysis applied to the current balance sheet; if it’s insufficient, executives should consider even more significant adjustments for immediate action.

The economic environment is out of bankers’ control. Their responses are not, but these require action in advance. Banks can — and should — conduct a disciplined, diagnostic analysis of their NIM decay path and then correct it. This interest rate environment could be with us for some time to come.

Data In The Best, And Worst, Of Times

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.

Banks Ignore Credit Administration at Their Peril

Not long ago, I asked the CEO of a mid-sized bank how he makes funding decisions when looking to add new technology or software systems. He told me that when it comes to spending money on software, he only listens to two people: the CEOs of other banks, and “Whatever my chief lending officer says.”

The question that immediately popped into my head was, “Why doesn’t the chief credit officer have a say?”

This situation is not unique. For a long time, credit administration has taken somewhat of a back seat when it comes to resource prioritization within banks. But this mindset can be dangerous for banks; if executives don’t give credit administrators the budget they need, it can come back to bite their institutions in several ways. Bankers would be wise to take a second look at how they allocate resources and consider three reasons why credit administration should be a bigger priority.

The resource disparity
It’s not hard to understand why credit administration can sometimes get overlooked. The lending side of the house gets the most investment because it brings in the revenue. When push comes to shove, the money makers are the ones who will receive the most attention from management.

It’s not as if credit administrators have been completely forgotten: Bank CEOs usually understand the importance of managing the loan portfolio. But the group often doesn’t receive funding priority, while it often feels like the lending team has a blank check. Credit administration is just as important as loan production; closing the resource gap can decrease risk and increase efficiency for your bank.

Limiting credit risk
The biggest reason banks should invest more in their credit administration department is risk mitigation. When credit administration systems are ignored, spreadsheets can run rampant. This opens the door to numerous risks and errors, including criticism from auditors and examiners.

Outdated systems can open your bank up to risk because administrators are unable to gather and analyze data in a coherent way. They are left using multiple systems and spreadsheets to create a complete view of a loan — a fragmented process that makes it easy to miss important risk indicators. Credit administrators require powerful tools that increase a loan’s visibility, not limit it. To mitigate risk, banks should invest in systems that make it easier for credit administrators to see the complete picture in one place.

Increasing operational efficiency
Investing in credit administration also improves operational efficiency for your bank’s employees. Clunky, outdated systems impede credit administrators from accessing important information in succinct ways. Fumbling through multiple systems and screens to find key data points increases the time it takes to perform even the most routine tasks. Performing a simple data extraction will often involve IT, wasting multiple employees’ time. What could be a simple and straightforward loan review process has turned to a slow, cumbersome and ultimately expensive process.

Outdated software can also negatively impact your team’s overall job satisfaction. Poorly designed systems can be frustrating to use; upgrading other departments’ systems could create a perception that credit administrators aren’t valued by the organization. This could hurt your company culture and lead to costly turnover down the road. Investing in new software and giving credit administrators tools that make their jobs faster and easier is a way that banks can demonstrate their support, keep employees happy and improve the efficiency of their work — potentially improving overall profitability.

Credit administration and loan production are two sides of the same coin, but resources have been weighted significantly toward the lenders for too long. It’s time for a change. Reassessing how your bank can support its credit administration team can mitigate risk, improve operations and ultimately save your bank money.

Turning Compliance From an Exercise Into a Partnership

The Greek philosopher Heraclitus once observed that no one can ever step into the same river twice. If these philosophers tried to define how the financial industry works today, they might say that no bank can ever step into the same technology stream twice.

Twenty-first century innovations, evolving standards and new business requirements keep the landscape fluid — and that’s without factoring in the perpetual challenge of regulatory changes. As you evaluate your institution’s digital strategic plan, consider opportunities to address both technology and compliance transformations with the same solution.

The investments your bank makes in compliance technology will set the stage for how you operate today and in the future. Are you working with a compliance partner who offers the same solution that they did two, five or even 10 years ago? Consider the turnover in consumer electronics in that same period.

Your compliance partner’s reaction time is your bank’s reaction time. If your compliance partner is not integrated with cloud-based systems, does not offer solutions tailored for online banking and does not support an integrated data workflow, then it isn’t likely they can position you for the next technology development, either. If your institution is looking to change core providers, platform providers or extend solutions through application programming interfaces, or APIs, the limitations of a dated compliance solution will pose a multiplying effect on the time and costs associated with these projects.

A compliance partner must also safeguard a bank’s data integrity. Digital data is the backbone of digital banking. You need a compliance partner who doesn’t store personally identifiable information or otherwise expose your institution to risks associated with data breaches. Your compliance data management solution needs to offer secured access tiers while supporting a single system of record.

The best partners know that service is a two-sided coin: providing the support you need while minimizing the support required for their solution. Your compliance partner must understand your business challenges and offering a service model that connects bank staff with legal and technology expertise to address implementation questions. Leading compliance partners also understand that service isn’t just about having seasoned professionals ready to answer questions. It’s also about offering a solution that’s designed to deliver an efficient user experience, is easy to set up and provides training resources that reach across teams and business footprints — minimizing the need to make a support call. Intuitive technology interfaces and asynchronous education delivery can serve as silent accelerators for strategic goals related to digitize lending and deposit operations.

Compliance partners should value and respect a bank’s content control and incorporate configurability into their culture. Your products and terms belong to you. It’s the responsibility of a compliance partner to make sure that your transactions support the configurability needed to service customers. Banks can’t afford a compliance technology approach that restricts their ability to innovate products or permanently chains them to standard products, language or workarounds to achieve the output necessary to serve the customer. Executives can be confident that their banks can competitively adapt today and in the future when configurability is an essential component of their compliance solution.

A compliance partner’s ability to meet a bank’s needs depends on an active feedback loop. Partners never approach their relationship with firms as a once-and-done conversation because they understand that their solution will need to adjust as business demands evolve. Look for partners that cultivate opportunities to learn how they can grow their solution to meet your bank’s challenges.

Compliance solutions shouldn’t be thought of as siloed add-ons to a bank’s digital operations. The right compliance partner aligns their solution with a bank’s overall objectives and helps extend its business reach. Make sure that your compliance technology investment positions your bank for long-term return on investment.

Making Strategic Decisions With The Help of Data Analytics

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.