Data Blind Spots and Data Opportunities: What Banks Are Missing

Community banks and credit unions operate in an increasingly challenging marketplace. With dynamic interest rates, economic uncertainty and shifting consumer expectations, these financial institutions face increasing pressure to adapt and stay competitive. How they respond can make the difference between outperformance and underperformance.

The challenge is that many financial institutions still operate with management systems and reporting cadences that prevent them from meeting the demands of a dynamic market — mitigating its risks and seizing its opportunities on a timely basis.

Some institutions, especially smaller ones, operate at a slower pace — in effect, synchronizing their internal reporting with quarterly and annual requirements of auditors, investors and regulators. Others may set a high priority on maintaining a pristine balance sheet, but don’t capitalize on the sales signal hiding within their risk management data.

To be sure, most financial services leaders know that data can answer vital questions: How are we tracking against expense, revenue and product plans? What products are “hot” this month? What are the implications of borrowing trends and interest rates for our business as a whole? But these leaders are also, to paraphrase a former U.S. Secretary of Defense, well aware that both known unknowns and unknown unknowns are waiting to trip them up.

The stakes are higher for smaller banks and credit unions. Their largest rivals are already using artificial intelligence-based data analytics to increase sales of smaller loans, cutting into community-based institutions’ bread and butter markets. The latter still have a key advantage in the fact that they are local, and know their accountholders and their markets better than most others. But in a data-driven, digital marketplace, that may not be enough.

The disconnect between financial institutions and the changing conditions of their markets has left many of them with blind spots – aspects of their operations for which they have insufficient or insufficiently timely data. These blind spots can be areas that might benefit from closer risk management as well as overlooked opportunities to seize the competitive or operational advantage.

Here are five critical areas where more effective use of data can make a difference:

Interest Rate Risk: Objects in Mirror Are Larger Than They Appear

Keeping an eye on interest rates is Banking 101. But interest rate risk is one of the most pressing challenges today for community banks and (to a lesser degree) credit unions. Changes in rates have wide-reaching impacts on profitability, as mismatches between the interest earned on loans and the interest paid on deposits can quickly erode margins.

To manage this, institutions need robust systems for asset liability management (ALM) that can simulate various interest rate scenarios and highlight potential vulnerabilities.

By running multiple simulations, ALM software helps institutions accurately assess the impact of rate shocks, allowing them to adjust strategies before problems arise. For example, the system can pinpoint when loans or certificates of deposit are about to mature and flag trends in overdue payments. These insights enable proactive decision-making, allowing institutions to mitigate risks before they materialize into larger issues.

Mitigating the Risks:

Banks need robust systems for asset liability management (ALM) that can simulate various interest rate scenarios and highlight potential vulnerabilities.

Liquidity Risk: Hard Lessons of the World’s First Social Media Bank Run

Liquidity risk became a front-and-center concern after the collapse of Silicon Valley Bank (SVB). The bank failed in March 2023 after suffering large losses from selling long-term bonds at a loss due to rising interest rates, leading to a liquidity crisis. A bank run ensued as depositors— many of them —rushed to withdraw their funds, overwhelming the bank and forcing regulators to step in and take control. Inadequate board oversight and incomplete modeling played a significant role in SVB’s downfall, underscoring the importance of having robust liquidity monitoring systems in place.

Liquidity risk, like interest rate risk, is a subset of asset liability management. Community banks can avoid problems like SVB’s by leveraging ALM tools that integrate data across loans, deposits, investments, and borrowing capacity. The objective is to gain real-time visibility across all holdings to ensure adequate funding to meet obligations. With liquidity properly positioned, a bank can navigate other challenges with confidence.

Credit Risk: What to Watch Out for Amid Economic Turmoil

With economic uncertainty on the rise, managing credit risk is more critical than ever. Community banks and credit unions are particularly vulnerable when major borrowers experience financial distress. Additionally, credit risk management is about to undergo a significant shift with the new Current Expected Credit Loss (CECL) standards. These regulations require financial institutions to calculate loan loss reserves from the moment a loan is issued rather than when a loss becomes probable, which means they must analyze vast amounts of historical data and additional qualitative factors.

Advanced modeling tools, with CECL management functionality, can help FIs flag changes in significant accounts before credit problems worsen. They can also help institutions prepare for coming rule changes by automating much of the analysis, ensuring that they have the necessary capital reserves in place when economic conditions shift.

The sheer speed of business today makes it imperative that institutions optimize how they use data, according to Brad Dahlman, Director of Product Management at financial services technology provider Jack Henry.

“Interest rate risk, liquidity risk, and credit risk are the key risks that cause banks and credit unions to fail,” he said—and they also “remain the likeliest to eat away their margins.” The company recently enhanced its cloud-based Financial Performance Suite (FPS) by releasing Daily Dashboard, a comprehensive offering aimed at delivering higher frequency data-based insights across bank and credit union business lines.

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Sales Diagnostics: Data Isn’t Just for Defense

Financial institutions are accustomed to viewing their management data and key ratios as top-down management tools – but they are also bottom-up business diagnostics. And just as with risk management applications, their diagnostic benefits increase dramatically with increased timeliness and agility of response. For example, data can gauge how a particular promotion aimed at car loans or mortgage originations is working. Armed with better data, FIs can make better use of their teams and ensure they are focused on the right products at the right time.

A recent American Bankers Association paper urged banks and credit unions to focus on boosting small business lending in their communities by leaning more heavily on data in making underwriting decisions. The ABA recommended that FIs adopt new data-driven underwriting processes, harness data and analytics to target their best accountholders more effectively and upgrade data and analytics technology. By leveraging data-driven insights, banks and credit unions can pinpoint promising businesses, customize their services, and improve their lending strategies. Although banks have access to vast amounts of data from their customer base, many have been slow to develop strategies for effectively using this information.

An Ohio community bank with $845 million in assets, is an example of doing what the ABA suggests. During the pandemic, Killbuck Savings replaced its old loan issuing process that had been anchored in spreadsheets, moving to an all-digital, data- and software-driven approach, provided by Jack Henry.

Killbuck Chief Financial Officer Justin Pike said that in 2022 alone, as a direct result of making underwriting decisions more data-driven, the bank’s loan volume grew by 25%, a level of business that would not have been possible without the new efficiencies and shorter turnaround times from taking a more data-driven approach.

Merger Integration: Making Sure That 2+2=5

Financial services leaders understand that getting the deal done is only half the battle. Effective execution of a merger or acquisition is famously difficult: across all industries, between 70% and 90% of mergers and acquisitions fail to achieve their intended goals or create shareholder value, according to research by McKinsey, Harvard Business Review and others. These failures can be due to a range of factors, including poor strategic fit, cultural clashes, integration challenges, or failure to realize projected synergies.

For financial institutions in particular — FDIC data since 2019 indicates that some 4-5% of insured depositories merge annually—M&A can be a way of life and effective integration demands a data-first approach. When management data — such as financial reports, risk assessments, and accountholder information—is consolidated quickly, both institutions can harmonize their strategies, avoid duplicative efforts, and identify risks and synergies earlier. This data integration allows leadership teams to monitor KPIs, streamline operations, and make informed decisions that align with the newly combined FI’s objectives.

The early integration of management data helps mitigate risks associated with regulatory compliance, market competition, and accountholder retention. FIs operate in a highly regulated environment, and a merger magnifies the complexity of ensuring compliance across different systems. Consolidating management data provides a unified view, reducing the chances of regulatory violations or financial discrepancies. Moreover, effective data integration strengthens the institution’s ability to quickly respond to competitive pressures and enhance customer and member service, as a unified management team can access comprehensive insights into both FIs’ accountholder bases, enabling a more cohesive and proactive approach in the post-merger landscape.

In the current market, small banks and credit unions find themselves at a critical juncture. The effective use of data is no longer optional but essential in a competitive and fast-changing financial landscape. From managing risks like interest rate fluctuations and liquidity concerns to enhancing operational efficiencies and accountholder engagement, FIs that leverage data-driven insights will be better positioned to mitigate risks, optimize sales, and adapt to regulatory changes, ultimately driving growth and maintaining their edge in a rapidly evolving market. Failing to address these data blind spots could leave them vulnerable to both external market forces and internal inefficiencies, hindering long-term success.

Mark Egan has held leadership roles at Brookfield Asset Management, Allianz Global Investors, Guggenheim Partners and Bloomberg. Egan began his career at Reuters, where he worked as a journalist for nearly 20 years and won two Reuters Journalist of the Year awards. He has a Masters in economics from Trinity College Dublin and lives in Montclair, New Jersey.