Skip to main content

Looking for Valuant? You are in the right place!

Valuant is now Abrigo, giving you a single source to Manage Risk and Drive Growth

Make yourself at home – we hope you enjoy your new web experience.

Looking for DiCOM? You are in the right place!

DiCOM Software is now part of Abrigo, giving you a single source to Manage Risk and Drive Growth. Make yourself at home – we hope you enjoy your new web experience.

Data-driven strategies for banks and credit unions: Start here

Paula S. King, CPA
July 14, 2023
Read Time: 0 min

Practical advice for using data to develop and support institution goals

Answering three questions ahead of strategic development discussions can ensure data drives your financial institution's efforts.

Would you like other articles like this in your inbox?

Leapfrog competition, reduce risk

How to develop banking strategies using your data

Everywhere bank and credit union leaders look, it seems, someone is talking about how financial institutions should leverage their data and analytics to develop strategies for leapfrogging competition and reducing risk.

So why aren’t more financial institutions already using their abundant data about customers and products to drive new offerings or deals, influence pricing, and serve customers better?

Three areas that are vital for using data to inform and execute winning business strategies often become roadblocks to success:

  • First, clearly identifying what information is needed to support and achieve the institution’s goals is essential.
  • Second, efficiently generating those insights from your data is a requirement for a sustainable process.
  • Finally, periodically assessing performance against institutional strategies creates a method for measuring success so leaders can pivot or seek more insight before plans get off track.

Financial institution leaders can use their answers to the following three questions to clear potential barriers and ensure a data-driven approach to strategic planning.

Make informed decisions faster. See how Abrigo Connect simplifies your financial institution data so you can make better decisions.

Learn More

Remove barriers

3 Questions to foster data-driven strategies


What are your short- and long-term strategies, and what questions must you answer to support and achieve those strategic goals?

Creating data analytics and reports alone are not the strategies; rather, they are the critical inputs to assist decision-makers in developing and executing those strategies.

Staff producing the reports must communicate with management and inquire what management wants to glean or achieve from the data insights. A pertinent question could be: what critical questions do you need to answer? Then, determine which reports/insights can answer those questions and better inform decisions.

The goal isn’t the production of reports but producing insights and information that are critical to developing and executing strategy. It’s providing the decision-makers with meaningful insights so they can execute appropriately.

For example, say your management and board’s strategy for the upcoming year is to grow the CRE loan portfolio by 10%, and the institution needs to consider expanding into new markets to achieve this goal. Before finalizing this goal, management should consider the following:

  • Historical trends
  • Current CRE concentrations
  • Market analysis
  • Loan pricing considerations
  • Real estate industry performance and
  • Peer comparisons.

Reports, insights, and data analytics that will assist management in determining whether this is a viable strategy include:

  • Historical CRE growth trends over the last five years, further segmented by industry, collateral type, and location. Analyzing this data will set the stage for the institution’s expectations for the 10% growth (e.g., is past performance a good indicator of future performance, and what needs to be adjusted if growth has not met expectations historically?)
  • CRE concentration report. Analysis should be performed on concentrations, as a percentage of capital, in terms of:
(1) Collateral type such as multifamily, retail, office, etc.
(2) Owner versus non-owner occupied, and
(3) Individual or related group of borrowers.
This analysis will identify CRE types where the institution may be already bumping up against its in-house policy concentration limits. Management may need to adjust its strategy to grow within areas where there is still room for growth without jeopardizing these limits.
  • CRE geographic heat map. Where are the majority of your borrowers and collateral located, and where should the institution concentrate its marketing efforts?
  • CRE portfolio credit attributes. These should include historical interest rate and credit performance trends (e.g., how has this portfolio performed over time, and has the pricing reflected the risk taken?)
  • Industry borrower data. How has the commercial real estate market performed, and how it is performing today by collateral type in your region/market? Decisions that can be gleaned from industry data include areas in which to focus your growth as well as loan decisioning, such as loan pricing, based upon industry performance and level of risk determined by a review of this information.

It’s not enough to produce the above analyses. The institution should prepare a formal written report that interprets the above insights and compares these insights to the growth strategy. The report should include a conclusion as to if this CRE growth strategy is viable AND how management plans to achieve this growth.

Consider interrelated goals to fine-tune strategy using data

Another constructive approach for ensuring you have the right data insights to identify and support strategic initiatives is to evaluate the financial institution’s goals/issues as a whole by ranking them and considering how they are interrelated. This exercise may reveal the need for bigger-picture data analysis.

Start with an Inventory of your goals/issues and rank them in descending order. Identify any interrelated goals or issues, then determine the data analytics that will provide the insights. For example, your top goal for next year might be to expand your lending geographic footprint. Be very specific regarding this goal, including the targets for particular percentage growth, loan types, industries, and locations. Another goal that should go along with this goal to expand is to identify funding sources (e.g., add the FHLB or focus on certain types of deposits and/or on the depositor base by offering deposit incentives). Obviously, these two goals are interrelated – without excess liquidity, the institution will need to provide additional funding to meet the target to expand the lending footprint.

So, in this situation, the institution will add reports showing the makeup of deposits (e.g., core vs. non-core, migration of deposits from core to transactional accounts, any trend in movement of funds out of the institution, top 10-20 depositors and associated volatility, borrowers without deposit relationships, etc.).

From these reports, the institution can determine whether it makes more sense to gather deposits and how to do it through incentives or better technology, for example, or whether the institution needs to target alternative funding sources.

Do you have the right technology?

The biggest hurdle in gaining data insights and making informed decisions is not having the right technology for efficiently and accurately reporting and monitoring data insights and, ultimately, making better strategic decisions, which will not only impact enterprise risk but can support growth and revenue recognition.

Even today, for example, financial institutions may piecemeal their data insights, typically cobbling data from a variety of data systems and reports (e.g., core-generated, less-than-ideal core report writers, and third parties) and ultimately, transferring the data into Excel for board and management reporting.


Evaluating business intelligence options

Here are several questions to consider when evaluating the technology used for reporting and monitoring data insights:

  • Is it easy to use, or must staff be technically well-versed in order to use it? Ease of use is a must, particularly in banks or credit unions with limited staff and bandwidth. Look for a solution with a natural language slant – one that uses straightforward data field names and keywords to generate data insights. Included artificial intelligence (AI) capabilities allow the data solution to learn a user’s interest in certain types of data insights and can suggest bank or credit union data analytics based on that user’s patterns.
  • Is the majority of your institution's data housed in the solution or data platform? Will you have the option to bring in other data sources to get a fuller, bigger picture, or are insights constrained by the limited data housed with the provider?
  • Are the insights derived from the tool reflective of up-to-date or real-time data? Historical data serves a purpose and can tell a financial Institution where they have been and how they have historically performed (e.g., in CECL calculations and loan performance), but stale information is not the best to use for strategic planning. A solution that can provide insights based on the most recent available data that you can provide is better.
  • Does the solution provide dynamic insights? Is there a drill-down feature to gain a more granular understanding of the data? For example, in analyzing a loan portfolio, can you easily and quickly drill down into a geographic concentration, then further identify the most significant loan type, and then further, the FICO score distribution within that loan type and within that geography? This allows for immediate insight to make better decisions as well as reporting to your board of directors on how to move forward with the loan portfolio focus. Using technology to access in minutes what might have previously taken you hours or days to gather fosters nimble decision-making.
  • Does the solution provide dashboards customized to specific groups within the financial institution (e.g., a lender performance dashboard that automatically updates as the financial institution uploads its data)?
  • In addition to reports, does the solution have the ability to monitor and alert staff when actual data metrics within the financial institution fall out of range with policy thresholds, minimum/maximum KPIs, concentration, or other limits set?
  • Does the solution provide access to peer and industry data for creating visual comparisons and providing loan decisioning insights?
  • Finally, does the solution provide quick and easy options for accessing the data (e.g., emailing, uploading to other documents, creating pdfs, and presentation features that allow for direct presentation to management and board groups)?

 A business intelligence solution that transforms the financial institution’s raw data into the insights leaders need without expensive data scientists or complex technical infrastructure supports timely strategic decisions.

How is your strategy working out based on your performance?

Finally, banks and credit unions should monitor performance against strategy using the reports and insights identified above at least quarterly. Findings should be reported to the board of directors at least quarterly, too. This periodic monitoring can provide the understanding necessary to regroup if falling behind on your strategy or to consider whether a strategy change is needed.


Execute plans

Data-driven strategies in changing times

Effectively planning for a financial institution’s growth, risks, and regulatory exams or reporting depends on quality data and analysis. The importance of data-driven strategies is magnified when circumstances are changing or are bound to change (such as with the eventual shift from higher interest rates to decreasing interest rates).

However, many financial institutions, especially smaller banks and credit unions, lack BI or business intelligence staff and complex technical infrastructures associated with “big data” options. Nevertheless, financial institutions of all sizes can execute plans developed using data (much of it already in their various systems) by identifying the relevant information, utilizing the right technology, and periodically comparing performance with strategic goals.

About the Author

Paula S. King, CPA

Senior Consultant
Paula King, CPA, is Senior Consultant for Abrigo Advisory Services, assisting financial institutions with CECL, credit processes, model validations, and during COVID, the SBA’s Paycheck Protection Program forgiveness process. A former banker and bank co-founder, she has held executive positions (CFO, Chief Risk Officer and Chief Compliance Officer) and has

Full Bio

About Abrigo

Abrigo enables U.S. financial institutions to support their communities through technology that fights financial crime, grows loans and deposits, and optimizes risk. Abrigo's platform centralizes the institution's data, creates a digital user experience, ensures compliance, and delivers efficiency for scale and profitable growth.

Make Big Things Happen.