Commercial lending is increasingly seen as a critical growth opportunity. Yet even credit unions with available capacity under their member business lending caps often struggle to expand. Omar Shute explained why and how to evolve in a recent Abrigo webinar.
Key topics covered in this post:
Credit unions have long relied on commercial real estate (CRE) lending as a cornerstone of their commercial portfolios. It’s familiar, collateral-backed, and often viewed as a lower-risk entry point into business lending. But that comfort can also limit how broadly institutions expand their commercial programs.
As a result, commercial lending remains underpenetrated across credit unions overall, with most growth concentrated among a small subset of institutions. In a recent Abrigo webinar, Omar Shute shared the following statistics to illustrate the significant opportunity for credit unions to expand—especially those looking to stay competitive as industry consolidation continues.
Those credit unions that want to survive will need to explore the realm of commercial lending beyond CRE.
There are clear reasons why CRE dominates many portfolios. These loans are tangible, relatively straightforward to underwrite, and align well with the risk frameworks many institutions already understand. For teams transitioning from consumer lending, they offer a natural starting point.
Because of this, CRE has become what Shute calls the “sweet spot” for many institutions entering commercial lending. But what makes it accessible also makes it limiting.
“The drawback is that they are more transactional in nature,” he said. “Your borrowers are going to be very, very sensitive to interest rates, and they will drop you for another lender for ten basis points better rate.”
In the CRE space, even strong relationships can be disrupted by small pricing differences. At the same time, shifting market conditions (rising rates, tighter underwriting, and changes in property demand) are adding new layers of complexity and concentration risk. The result is a portfolio that may grow in size, but not necessarily in strength. And for many institutions, growth slows not because of market conditions or regulatory limits, but because their commercial lending programs were never designed to scale efficiently.
The biggest challenge with a CRE-heavy strategy is not the asset class itself but those transactional relationships. Too often, Shute says, credit unions book large real estate loans but fail to capture the customer in other areas. Borrowers may finance properties with the credit union while maintaining operating accounts, treasury services, and day-to-day financial activity elsewhere. This disconnect leaves significant value on the table.
In addition, lean teams, manual processes, and concerns about risk oversight make it difficult to increase volume without adding complexity. In many cases, credit unions hesitate to expand not because they lack opportunity, but because scaling feels operationally out of reach. Without the right commercial lending infrastructure, growth becomes difficult to sustain. Institutions find themselves balancing loan demand with internal capacity, rather than building a program designed for consistent, repeatable expansion.
To move beyond transactional lending, credit unions are looking at commercial and industrial (C&I) lending and business lines of credit as the next step. Unlike CRE, these products are tied directly to a business’s operations—how it generates revenue, manages expenses, and navigates day-to-day cash flow. That connection naturally opens the door to broader engagement. When a credit union supports a business’s working capital needs, it is far more likely to capture operating accounts, treasury services, and payment activity. It also creates opportunities to serve employees, expand membership, and build relationships that extend well beyond a single loan.
Equally important, these lending segments can be structured to be efficient and scalable. By prioritizing the right mix of loan types, member segments, and operating models, credit unions can grow their portfolios in ways that align with both member needs and internal capacity.
One of the main reasons credit unions hesitate to expand beyond CRE is risk perception. Real estate offers something tangible—a physical asset that can be appraised, monitored, and, if necessary, liquidated.
But in practice, said Shute, commercial loans are repaid through cash flow, not collateral. A business with strong, consistent cash flow, healthy debt service coverage, and an active deposit relationship can present a more stable repayment profile than a marginal CRE deal supported primarily by property value. Shifting the focus from collateral to cash flow allows credit unions to evaluate risk more holistically and identify opportunities that may otherwise be overlooked.
Just as importantly, aligning credit policy, governance, and risk management frameworks to support these types of loans is key to scaling safely. Growth doesn’t require loosening standards—it requires designing processes that make disciplined lending repeatable.
This blog was developed with the assistance of ChatGPT, an AI large language model. It was reviewed and revised by Abrigo's subject-matter expert for accuracy and additional insight.
Financial institutions conduct one as first-line risk management to identify emerging risk tied to a borrower. The other is an independent function evaluating portfolio credit risk "from the ground up."
Key topics covered in this post:
In credit risk management, few distinctions are more frequently blurred—or more consequential —than the difference between the processes known as annual review and loan review. The terms are often used interchangeably at financial institutions, leaving many people confused.
But words matter. Annual reviews of loans and the loan review process represent fundamentally different concepts, performed by different parties, for different purposes.
An annual review is a first-line risk management activity conducted by lenders or portfolio managers on a borrower with one or more outstanding credit facilities. The goal of an annual review of a borrower should be to identify emerging risk factors as part of ongoing monitoring of lending and assess whether mitigation is possible.
An annual loan review may coincide with a renewal or new extension of credit. In other cases, the review is a standalone activity. An example of the latter is a review in the second year of a multi-year term loan where no financial transaction (i.e., renewal, new loan, etc.) transpires.
When tied to a loan renewal or new debt, the annual review process is typically a full underwriting of the new exposure, including an evaluation of existing facilities. When an annual review stands alone, however, re-underwriting is neither necessary nor efficient.
This is where confusion and wasted effort often arise. Unless a loan is in default, the bank or credit union’s ability to materially change the credit during an annual review is limited so long as the borrower is performing as agreed. The purpose of an annual review, therefore, is not to recreate underwriting. Again, lenders or portfolio managers should be trying to detect any emerging risk factors and, if found, determine whether mitigation is possible.
That means the yearly review process should be risk-based:
In all cases except renewals or extensions, the annual review remains a review of the current condition; it is not a full underwriting. There is nothing new to underwrite.
Once a credit is placed on the problem loan report (“Special Mention” or worse), the annual review process should be suspended. These credits are already subject to far more intensive and more frequent monitoring. When and if the credit returns to pass status, the annual review process can resume.
Loan review, also known as credit risk review, is something entirely different from the annual review.
It is a second-line, independent function of risk management at a financial institution. Typically reporting to the board, loan review is performed by trained specialists who were not involved in underwriting or approval. The loan review program or team assesses the portfolio “from the ground up” by sampling portfolio segments and aggregating the results of the review of individual borrowers in those segments to find patterns and trends that deserve management and board attention.
Loan review’s purpose is clearly articulated in the FFIEC’s Interagency Guidance on Credit Risk Review Systems (May 2020), including:
Loan review may be conducted through periodic, point-in-time examinations or continuous monitoring of targeted segments, such as recent originations. The best practice is to employ both methods, with continuous monitoring augmented by the deeper dive of the examinations. Most financial institutions tend to focus on their commercial lending activities, but the institution’s investment portfolio and Wealth Management group (i.e., the credit risk associated with fiduciary activities) may also be subject to credit risk review.
Conducting loan review at a bank or credit union typically involves:
As part of its review of individual credit, loan review should not involve re-underwriting credit. Rather, its focus is on the adequacy of the underwriting process undertaken at the time the credit was originated or renewed, including any updates identified through the annual review. Further, loan review is not the simple acceptance of the institution’s credit risk management framework (policies, guidance, risk rating framework, etc.) and only focusing on the adherence of the underwriting process to that framework. Rather, loan review must assess that the framework itself is adequate and recommend changes to ensure the institution operates in a safe and sound manner. Remember, at the end of the day, effective loan review is portfolio review from the ground up.
To summarize:
Annual review of a loan is:
Loan review is:
Both functions are essential. They are complementary, not interchangeable.
Sound credit risk management depends on clarity of roles, discipline of purpose, and proportionality of effort. When each function does the job it was designed to do, the institution is far better positioned to spot trouble early, respond intelligently, and preserve both capital and credibility.
Getting the words right is not semantics. It is risk management.
Even if financial regulatory agencies evolve or priorities shift, banks and credit unions must maintain these loan review fundamentals to maintain institutional safety and soundness.
Change has finally arrived in the regulatory world. For example, the OCC and FDIC have rescinded the 2013 Leveraged Lending Guidance, updated capital requirements are out for comment, and “tailoring” of supervision is gaining momentum.
In the practice of independent loan review, it’s tempting to assume these shifts will lighten our load. That’s probably wishful thinking.
Regulatory shifts may change the mechanics of supervision, but they don’t lessen the responsibility of financial institutions’ credit risk review teams. If anything, they make our role more essential in helping institutions navigate evolving expectations and maintain discipline.
Still, now is an ideal moment to revisit what loan review is fundamentally about. A good starting point is the 2020 guidance on credit risk review systems. It outlines several key characteristics of an effective credit risk review framework, which fall neatly into three categories:
Credit
Portfolio
Results
From this, our core mandates for loan review become clear:
Are loans rated accurately, and does the rating system itself allow the institution to understand the level and direction of risk? A simple litmus test: if more than 20–25% of portfolio exposure concentrates in a single risk grade, the framework isn’t properly calibrated. In my experience, when a single grade carries more than a quarter of the exposure, it’s usually a sign that the scale isn’t distinguishing risk effectively. The loan review function must call that out and insist on a structure that distinguishes risk, not hides it.
Loan review teams often fixate on exceptions—sometimes obsessively. But the more important question is whether credit policies and guidelines are adequate for how the institution actually operates. If there’s a disconnect between the stated guardrails and day-to-day practice, that’s a policy adequacy problem, and no amount of exception tracking compensates for it.
For example, from a policy perspective, unsecured lending is considered undesirable and should only be made on an exception basis. Yet, a review of an institution’s portfolio indicates that a third of loans are unsecured. Further analysis indicates 95% of exposure is risk rated “4” (assuming a six-point pass scale with 4 as Average) or better, so there is no real concern on credit quality. Yet, this institution has created a rash of exceptions (a third of the portfolio!), which are unnecessary and do nothing more than create noise. The policy needs to be revisited.
If anything, labeling unsecured lending as an exception should only apply to loans risk rated 5 or worse. More appropriately however, it’s not a policy issue at all. It’s simply a portfolio segment to monitor and perhaps apply a capital allocation. It’s harder to make this kind of a call, but it’s the one that truly matters.
Years ago, I reviewed a small market where the four largest loans were nonaccrual with partial charge-offs. Management had been replaced, and the new folks had worked tirelessly to stabilize them. By our mechanical scoring system, however, the overall examination rating would have been “Unsatisfactory.” That was unfair. I overrode the score and argued for a more reasonable rating.
Too often, examinations elevate process over substance. The real question is whether people—lenders, managers, and the oversight function (i.e. credit)—are doing their jobs. Today, as more institutions adopt automated scoring and workflow tools, this principle matters even more. Technology can support judgment, but it can’t replace the need to understand when people are doing the right work for the right reasons. As financial institutions’ loan review software tools get more sophisticated, the discipline to challenge them—not just follow them—will be what separates effective institutions from merely compliant ones.
Your audience does not want a 25-page tome of charts and grids that says nothing. Keep the credit risk review report to 3–5 pages and make sure to:
Everything else—scorecards, detail schedules, loan-level grids—belongs in an appendix.
In my early career, a boss distinguished between the “regulatory” and the “righteous.” Regulatory meant required. Righteous meant it made sense. Everything above falls into the righteous category. Even if agencies evolve or priorities shift, these fundamentals remain essential to institutional safety and soundness.
While the core mandates of credit risk review aren’t changing, there are areas where standing still means falling behind:
Automation was once limited to large institutions, but now it has become critical for community banks as well. Portfolios are more complex. Leadership talent from larger banks is migrating into smaller organizations, often bringing higher risk appetites. Borrowers themselves are more sophisticated, managing supply chains aggressively and pursuing opportunities with greater confidence.
If you are still relying on Word and Excel, and your view of portfolio risk is constrained by data built for operations rather than risk discovery, it is time to adopt a workflow platform. Automation not only accelerates the review cycle, it surfaces the “hidden mickeys” of risk that manual processes continually miss.
AI may straddle the line between fad and transformative force, but the “righteous” use case is clear: eliminating rote, time-consuming work.
Even as a speed reader, it takes me an hour or more to scan a loan agreement. An AI-powered loan review solution like Abrigo’s can do the first pass in minutes—accurately flagging issues, freeing humans to focus on judgment, decisions, and action. Of course, like any powerful tool, AI works best with reasonable oversight. Validating outputs and keeping humans in the loop ensure technology enhances judgment rather than dilutes it. AI can draft exam results, summarize trends, and spotlight previously invisible risk pockets. This isn’t science fiction; it’s here, and it’s practical. It can speed loan reviews by 30%.
The more things change, the more the fundamentals of loan review stand out. Regulatory priorities will evolve, guidance will come and go, and supervisory approaches will shift. But the responsibility of loan review does not. At its core, the function exists to ensure that credit risk is accurately understood, clearly communicated, and actively managed.
That’s the standard that matters—not whether every box is checked, but whether the work leads to sound judgment and better decisions. Institutions that navigate change successfully will be those that stay grounded in these principles while embracing tools and approaches that strengthen them. In the end, safety and soundness is driven by how well we execute on the fundamentals, regardless of the environment.
Loan review software is a credit risk review system that helps banks and credit unions evaluate loan quality, validate risk ratings, identify emerging weaknesses, and report findings to management and the board. In this context, loan review software supports a more structured, scalable alternative to manual reviews in Word and Excel.
Loan review software helps teams spot actual or potential loan weaknesses, assess portfolio trends, evaluate policy adequacy, and produce objective results for leadership. It is designed to support the core responsibilities of independent credit risk review.
Loan review software improves risk visibility by making it easier to surface concentrations, weak ratings, policy issues, and pockets of emerging credit risk. Manual processes often miss “hidden” risks that automation can surface faster.
Loan review software supports regulatory readiness by helping institutions maintain consistent reviews, timely credit-quality information, and clearer reporting for management and financial or regulatory purposes.
Choose Abrigo because its loan review software helps banks and credit unions strengthen credit risk review with automation, better risk visibility, and clearer reporting for management and the board. Abrigo’s approach is built around core loan review fundamentals—accurate risk ratings, policy adequacy, emerging risk identification, and practical workflow efficiency—so institutions can move beyond spreadsheets and manual processes without losing judgment or oversight.
Financial institutions strengthen their portfolio stress testing, satisfy examiners and auditors, and gain real-world risk insights by connecting portfolio-level results to loan-level behavior and CECL assumptions.
Over the past few weeks, I’ve had three separate conversations about stress testing. One followed an exam. Another came during a CECL validation. The third happened in a working session with an Abrigo bank customer reviewing allowance results.
Three different settings. Three different stakeholders. The same types of concern surfaced each time:
“We’re getting a lot more questions about stress testing.”
“They want to understand how we’re stress testing our portfolios.”
“We have stress testing… but I’m not sure it’s at the level they’re expecting anymore.”
If one of those sentences sounds familiar, you’re not alone. I’ve yet to leave a conversation like this without someone eventually pausing and saying quietly, “We should probably revisit this.”
Variations of this same feedback are showing up across a wide array of institutions. Regulators, auditors, and financial institutions themselves are all pointing toward the same conclusion: stress testing expectations are evolving, and many programs haven’t fully evolved with them yet.
Top-down, portfolio-level stress testing still plays an important role and is often the starting point for institutions trying to understand aggregate exposure. It helps leadership frame the big picture and supports board-level discussions around earnings sensitivity and capital adequacy.
On its own, however, that view can feel incomplete.
Increasingly, examiners and auditors are looking for a clearer connection between portfolio-level results and what’s actually happening within the loan portfolio. They want insight into where losses originate, which segments are most sensitive to changing conditions, and how risk migrates as the environment deteriorates.
As a result, when presentations of portfolio-level stress testing results start to sound a little too high level, you might see an examiner, auditor, or even someone on the institution’s own risk team lean forward and ask, “Okay… but which loans actually move first?” That’s not a trick question. The question is less about the numbers themselves and more about whether the institution truly understands how its portfolio behaves under stress.
Stress testing is not new. It’s been emphasized for years, and I’ve been one of many voices making that case along the way. What has changed is the context around it.
With the adoption of CECL, regulators have been explicit that stress testing and allowance methodologies should no longer operate as separate, parallel exercises. Both rely on forward-looking views of credit loss, and both depend on an institution’s understanding of how economic conditions influence portfolio performance.
In a statement on CECL and stress testing, the Federal Reserve noted that “the current approach is conceptually similar to the CECL standard, as both methods are based on a forward-looking estimate of losses.”
That framing matters. When stress testing and allowance estimates are both grounded in forward-looking assumptions, the bar naturally rises for how results are constructed, interpreted, and explained.
In practice, that linkage tends to raise three questions during examinations and validations:
When institutions can clearly walk through those points, conversations with examiners tend to move quickly toward implications. When they cannot, the discussion often returns to how the stress test was constructed in the first place.
For a long time, stress testing occupied an odd space for many institutions. It existed largely as an annual exercise because it had to. And it followed this pattern: Run the scenario. Present results to leadership. Leave the analysis unchanged until the next cycle. I’ve literally seen stress testing decks that come out once a year, usually right before a meeting, and then disappear until the next cycle – untouched, unquestioned, and slightly out of date.
That approach was easier to defend when stress testing served primarily as documentation. It’s much harder to defend when regulators expect the analysis to demonstrate how the portfolio behaves under stress.
Risk can build quietly. Conditions can shift faster than portfolios adjust. In that context, stress testing is less about satisfying a requirement and more about understanding how exposed a balance sheet really is, especially when allowance estimates themselves are already grounded in forward-looking assumptions.
Portfolio-level stress results can show that losses increase under a recession scenario. The next question is where those losses originate.
Consider a commercial real estate portfolio as an example. A CRE stress scenario might indicate higher losses overall. The follow-up discussion often centers on which exposures drive that change. Office properties may react differently than owner-occupied commercial real estate. Construction loans may respond differently than stabilized properties. Loans with higher loan-to-value ratios may show earlier deterioration than those with stronger collateral positions.
Institutions that can trace stress results to those types of characteristics usually have a much easier time explaining their analysis. Those that cannot often find themselves defending assumptions rather than discussing the implications of the results.
Connecting broader assumptions about portfolio-level results with loan-level behavior aligns closely with how CECL works. Allowance models already force institutions to think more deeply about segmentation, economic loss drivers, and the timing of credit losses. When stress testing doesn’t reflect that same level of insight, the disconnect becomes obvious – especially under examination.
Institutions with more developed stress testing practices tend to bridge the gap between broad assumptions and loan-level behavior more effectively. Some characteristics that seem to appear consistently, in my experience:
Most importantly, management can explain the results in practical terms. Instead of simply stating that losses increase under stress, they can describe why those losses increase and where the pressure in the portfolio is likely to emerge.
When those explanations exist, conversations with examiners and boards tend to shift from debating methodology to understanding implications.
In many cases, the issue was never a lack of awareness around risk, but a lack of depth in how that was being demonstrated.
Another shift becoming more common is how stress testing is used outside of exam season.
When stress tests are flexible enough to run multiple degrees of severity and explore different risk drivers, they naturally begin informing strategic planning discussions. Institutions often use stress analysis to evaluate questions such as:
Interestingly, this is often where teams realize stress testing becomes less stressful. When it’s used regularly, it stops feeling like a fire drill and starts functioning like a planning tool.
Capital decisions feel more grounded. Concentration risk becomes easier to visualize. Even underwriting and pricing conversations benefit from a clearer understanding of how loans behave under pressure.
Well-designed stress testing also creates a shared framework across credit, finance, capital planning, and allowance discussions. Instead of siloed views of risk, teams begin responding to a consistent narrative about how the portfolio performs under different conditions.
The questions being asked by auditors, examiners, and management in risk discussions today reflect that shift. They go well beyond whether a scenario was run and focus instead on how scenarios were designed, how assumptions were selected, and how results influence decisions. And the institutions that can clearly walk through that logic tend to have very different conversations than those relying on surface-level stress tests.
It’s worth emphasizing that stress testing doesn’t need to be overly complex or academic to meet those expectations. It does, however, need to be intentional and designed to surface risk rather than obscure it.
Stress testing, I’d argue, is experiencing a quiet resurgence because institutions, boards, and regulators all recognize the value of understanding stress risk in a deeper, more actionable way.
For institutions willing to reassess how they approach stress testing, the payoff goes well beyond regulatory comfort. It’s better insight, stronger preparedness, and a clearer view of how the portfolio might behave when conditions don’t follow the plan.
For many risk teams right now, the most important question isn’t whether stress testing exists, but whether it’s telling them what they actually need to know: If economic stress began affecting the most vulnerable borrowers tomorrow, would the stress test identify where losses emerge and how they affect earnings and capital?
Institutions that can answer that question clearly tend to have different conversations with examiners, auditors, and boards than those presenting stress results that remain disconnected from the portfolio itself.
Portfolio risk stress testing is a process banks and credit unions use to estimate how loan losses, earnings, or capital could change under adverse economic scenarios. Stress testing is a way to connect portfolio-level results to segment- and loan-level behavior so institutions can better understand where losses may emerge
Stress testing expectations are increasing because examiners, auditors, and institutions want more than a high-level portfolio result. Reviewers now expect institutions to show how stress affects specific segments, how risk migrates, and how management understands the portfolio under changing conditions.
Stress testing and CECL are increasingly connected because both rely on forward-looking estimates of credit loss. Institutions are being asked whether their stress assumptions align with the drivers, segmentation, and logic used in their allowance methodology.
Stress testing is more valuable when it is used as an ongoing planning tool instead of a once-a-year compliance exercise. Regular stress testing can improve decisions around concentrations, growth, underwriting, earnings, and capital by giving teams a clearer view of how the portfolio behaves under pressure.
Abrigo helps banks and credit unions move beyond surface-level stress testing by connecting portfolio-level results to loan-level behavior, CECL assumptions, and practical management decisions.
Loan pricing models shape portfolio composition, capital deployment, and an institution’s ability to absorb stress in changing economic conditions. Loan origination systems that offer loan pricing can streamline decisions.
This blog was updated from a 2022 version to include more details and newer information.
A loan pricing model is a structured framework used to determine the appropriate interest rate and fees for a credit facility based on cost of funds, operating expenses, expected credit losses, capital allocation, and target return metrics such as return on equity (ROE) or risk-adjusted return on capital (RAROC).
A methodical approach to pricing loans can help ensure the best loan and terms are matched to the borrower so that the financial institution makes the sale and keeps the customer or member.
Loan pricing models, sometimes referred to as loan profitability models, allow banks and credit unions to set prices that are consistent with other institutional objectives, such as portfolio composition goals, return on equity targets, and capital optimization strategies.
Interest rates on loans reflect more than competitive dynamics. As the Federal Reserve Bank of Minneapolis explains in What Drives Consumer Interest Rates?, loan pricing reflects funding costs, operating expenses, credit risk compensation, and required return expectations. While the article focuses on consumer credit, the same core components apply to commercial loan pricing frameworks.
Institutions that introduce more structured pricing methodologies often discover incremental improvements in yield. In talking with banks and credit unions, Abrigo has learned that these institutions estimated conservatively that they could pick up an additional 5 to 10 basis points in interest with more structured pricing methodologies in place. Even modest basis point gains, when applied across a portfolio, can materially affect earnings and capital flexibility.
Optimizing capital through disciplined pricing provides institutions the flexibility to deploy capital into new products, new markets, or strategic growth initiatives while maintaining appropriate risk-adjusted returns.
Another benefit of having a loan-pricing policy or model is that it provides the institution with defensible measures for justifying pricing changes and for avoiding charges of discriminatory pricing, which some lenders have faced in recent years. Officials with the banking regulatory agencies have outlined best practices for assessing fair lending risk, and one of those practices is for institutions to document pricing and other underwriting criteria, including exceptions.
What are some considerations related to loan-pricing models for an institution's loan origination system (LOS)?
At a basic level, the model should help institutions evaluate the economics and risks of the loans they are making. But loan pricing involves more than simply setting an interest rate. In practice, it requires understanding how a loan generates income, what the loan costs to produce, and what risks it adds to the balance sheet.
Most loan pricing models begin with a straightforward framework: a loan has revenue and expenses. Revenue comes from the interest charged on the loan and any associated fees. The costs or expenses of the loan include:
However, lenders must also understand how the loan will actually behave over time. For example, a line of credit may have a limit, but the amount of interest earned depends on how much the borrower actually uses. Some loan categories may have utilization rates around 40%, while others may be closer to 85%. Those differences can significantly affect the income a loan produces.
For that reason, a useful model will incorporate how cash flows behave over the life of the loan. The terms of the loan are less important than the timing and amount of expected payments.
Another critical consideration for loan pricing models is that pricing is fundamentally about pricing risk. When lenders add a credit spread to a base rate, that spread often reflects several different risks embedded in the loan. These include:
Credit risk. The most obvious example of risk embedded in loan pricing is the risk that some loans will not perform as expected, and some portion of principal and interest may not be repaid. Pricing models, therefore, include assumptions about expected losses. Indeed, expected credit risk is a primary driver of loan pricing decisions and is embedded into interest rate setting at the loan level, according to Federal Reserve research.
Prepayments. Loan models must also account for other uncertain cash flows. Borrowers may repay loans early, refinance, or otherwise change the expected payment schedule. These prepayments affect the income that the loan ultimately produces.
Interest rate risk. The cost of funding a loan can change as market rates move. Because of this, evaluating a new loan based solely on the institution’s current cost of funds can be misleading. Some pricing approaches instead look to market funding curves to estimate how funding costs may change in the future.
Taken together, these factors illustrate why pricing loans is not simply about rate and term. Risks can vary so much across borrowers and loan types that setting the loan’s risk premium for default can be one of the most difficult aspects of loan pricing.
Loan pricing models help price the risks and can take into account the financial institution's interest rate forecasts, its appetite for risk, required collateral, loan terms, and other factors.
Loan-pricing models also provide objective measures to evaluate lending decisions. Modern systems commonly rely on several approaches. One method compares the loan with an alternative investment, asking whether the institution would earn more by making the loan or by leaving the funds in the investment portfolio. Another common approach is an income method that calculates expected returns, such as return on assets or return on equity.
Importantly, a loan-pricing model does not tell a lender exactly what rate to charge. Market conditions, competitive pressures, and customer or member relationships still influence the final pricing decision. The model provides a clearer view of the relative value and risk of different loans, helping institutions make better-informed lending decisions.
Reviewing and adjusting loan pricing
Loan pricing models should also make it easier for financial institutions to take into account changing environments and adjust rates as needed, something regulators have encouraged.
“Management should continuously evaluate and adjust rates in response to changes in costs, competitive factors, or risks of a particular product type,” according to James L. Adams, supervising examiner at the Federal Reserve Bank of Philadelphia.
In a newsletter for community banks, Adams cited the Fed's Commercial Bank Examination Manual, which says, “Periodic review allows rates to be adjusted in response to changes in costs, competitive factors, or risks of a particular type of extension of credit.”
Regulatory guidance, such as Federal Reserve SR 11-7 and the OCC’s Model Risk Management Handbook, emphasizes the need for model governance and performance monitoring. Loan pricing models in a loan origination system should include:
Abrigo’s Loan Pricing has automation that supports defensible documentation and reporting for financial institutions with lean staffing.
Other benefits of integrating loan pricing models into systems like Abrigo LOS are that institutions can:
Integration with core systems can also allow cost-of-funds assumptions to be updated regularly based on benchmark indices.
Risk-based loan pricing allows financial institutions to offer competitive pricing on the best loans across borrower groups while either rejecting or pricing at a premium the loans that pose the highest risks. Market conditions, competitive pressures, and customer or member relationships still influence the final pricing decision. But the model provides a clearer view of the relative value and risk of different loans, helping institutions make better-informed lending decisions.
Providing systematic loan pricing for new loan origination and for annual reviews helps financial institutions calculate a defensible and consistent price. Abrigo’s loan pricing software integrates with its loan origination software to transform pricing from a bottleneck to a quick exercise. Banks and credit unions can intelligently set prices based on risk and profitability targets, configuring the model to fit their pricing sophistication, from simple to complex as needed.
A loan pricing model is a structured framework for setting loan rates and fees based on cost of funds, operating expenses, expected credit losses, capital allocation, and target returns. In loan origination software, it helps banks and credit unions make pricing decisions that are more consistent, risk-based, and defensible
Banks and credit unions use loan pricing software to align loan pricing with risk, profitability, portfolio goals, and capital strategy. It also helps institutions match terms to borrowers more effectively and improve pricing discipline during origination.
Loan pricing software should reflect expected cash flows, incorporate credit risk and interest rate risk, support scenario comparisons, and help solve for target rates or profitability metrics. Strong systems also track priced loans and generate reporting by borrower, risk rating, officer, or date.
Loan pricing software supports governance and fair lending by documenting assumptions, defining profitability targets, tracking overrides, and preserving pricing records. That creates a more consistent and defensible process for explaining pricing decisions and exceptions.
Integrating loan pricing into loan origination software makes pricing faster, more consistent, and easier to use within the lending workflow. It can automatically pull in risk ratings and collateral inputs, compare scenarios, and reduce manual steps for lenders.
Tax season always brings opportunity. Unfortunately, it also brings urgency, emotion, and a spike in tax season fraud.
A recent case shows just how devastating these scams can be. A 78-year-old Michigan retiree lost nearly $1 million after fraudsters posing as IRS agents convinced him he was part of a criminal investigation. The callers told him his Social Security number had been tied to drug trafficking and money laundering and that his assets could be seized. Over time, they persuaded him to liquidate investments and move funds into accounts they controlled, all under the pretense of “protecting” his money.
This was not a single impulsive transaction. It was a sustained social engineering effort built on fear, authority, and isolation. It is also a reminder that tax season fraud is not only a consumer awareness issue. It is a financial crime risk event that directly affects financial institutions.
The IRS reports billions of dollars lost each year to tax-related scams and identity theft. Impersonation schemes remain one of the most common tactics. As in the Michigan case, criminals often claim to represent the IRS and threaten arrest, license suspension, or asset seizure if immediate payment is not made.
Tax season fraud works because it taps into predictable human reactions:
Fraudsters understand this psychology, and financial institutions need to understand it as well.
Tax season fraud does not discriminate. However, certain groups are targeted more frequently because of financial position, life stage, or perceived vulnerability.
Older adults remain a primary target, particularly in impersonation scams. Many retirees have accumulated savings across deposit, brokerage, and retirement accounts. That makes them attractive to criminals looking for larger balances.
Equally important, some older individuals may be more inclined to trust a caller who claims to represent the IRS, the Treasury Department, or law enforcement. Fraudsters use official language, badge numbers, and fabricated case IDs to reinforce credibility. They often spoof phone numbers to make the call appear legitimate.
For banks and credit unions, behavior can be as important as dollar amount. Warning signs may include:
Frontline staff trained to recognize elder financial exploitation and fraud tactics in general can stop tax season fraud before funds leave the institution.
Young adults and first-time filers often lack experience with IRS processes. They may assume that email, text, or phone outreach is regular. Fraudsters rely on that uncertainty.
Many scams aimed at this group focus on refunds. Messages that claim a refund is delayed or requires verification can feel believable. When someone is expecting money, urgency does not seem suspicious.
Younger consumers may also be more likely to click links sent via text or social media. That increases exposure to credential harvesting and identity theft tied to tax season fraud.
From an institutional perspective, this activity can look different:
Connecting these signals across channels is essential.
Fraudsters frequently tailor tax season fraud narratives to immigrants and non-native English speakers. Threats may include deportation, visa revocation, or immediate legal action.
These scams are heavily intimidation-based. Victims may be hesitant to question someone claiming to be a government official. Language barriers can make it harder to verify information or seek help.
Financial institutions serving diverse communities should consider:
Across all groups, transaction context matters. Significant or unusual withdrawals tied to urgent stories about government investigations should raise red flags. Empowering employees to pause a transaction and escalate concerns can significantly reduce losses.
While tactics evolve, several schemes appear year after year during filing season.
IRS impersonation remains one of the most damaging forms of tax season fraud. Criminals claim to be IRS agents and demand immediate payment for alleged back taxes, penalties, or criminal investigations.
Common elements include:
The IRS generally initiates contact by mail. It does not demand immediate payment over the phone or request gift cards or cryptocurrency.
Financial institutions should watch for customers who appear coached, reference badge numbers or case IDs, or express fear of immediate legal consequences. Those behavioral cues often accompany tax season fraud.
Phishing campaigns increase sharply during filing season. Messages commonly claim:
Victims are directed to websites that closely resemble IRS pages. Once credentials and Social Security numbers are entered, criminals can file fraudulent returns, redirect refunds, or attempt to take over accounts.
Downstream effects may include:
Layered authentication and anomaly detection help reduce exposure to tax season fraud initiated through phishing.
In refund theft schemes, criminals file fraudulent tax returns using stolen Social Security numbers before the legitimate taxpayer files. Victims often discover the problem only when their return is rejected.
Filing early may reduce the risk, but identity theft frequently begins months earlier through unrelated data breaches or phishing incidents. Continuous monitoring remains critical.
Institutions should pay attention to:
These patterns can signal mule activity connected to tax season fraud.
Some tax season fraud originates with individuals posing as legitimate tax preparers. Red flags include:
Social media has also amplified fabricated refund strategies. Advice that encourages falsifying income or inventing credits not only creates fraud exposure but also serious compliance risk for the taxpayer.
These schemes can result in financial loss, penalties, audits, and long-term identity misuse.
Financial institutions can reinforce consumer protection by encouraging customers to verify preparer credentials such as CPA, EA, or attorney designations, sharing IRS guidance on legitimate filing procedures, and monitoring for unusual refund activity.
Preventing tax season fraud requires steady, practical vigilance. Consumers should protect personal information year-round, shred tax documents before discarding them, and store prior-year returns securely in both physical and digital formats.
Strengthening digital security is also essential in combating fraud and identity theft. Use strong, unique passwords for financial and tax accounts and enable multi-factor authentication. Avoid filing taxes on public Wi-Fi.
Instead of reacting immediately to unexpected tax communication, slow down and verify:
Monitor accounts regularly. Review bank, brokerage, and credit card statements. Early detection can significantly limit loss.
Tax season fraud affects both fraud and anti-money laundering (AML) teams and frontline staff. Institutions should expect increased alert volume and unusual transaction activity during filing season. Practical steps include:
Cross-channel visibility is critical. Fraudsters often coach victims on precisely what to say. Employees who feel supported in pausing transactions and escalating concerns can prevent significant losses.
Proactive customer education also matters. Clear communication about tax season fraud reinforces trust and demonstrates a commitment to protecting the community.
Tax season will continue to attract criminal activity. The pressure of tax obligations and the anticipation of refunds create an environment where social engineering can thrive. Awareness, sound controls, behavioral monitoring, and consistent education materially reduce exposure to tax season fraud.
For consumers, skepticism and verification are robust safeguards. For financial institutions, vigilance and timely intervention make a measurable difference. Preparedness, not panic, remains the most effective defense.
Tax season fraud is financial fraud that exploits tax filing deadlines, refund expectations, and fear of government enforcement to steal money or personal information. Common schemes include IRS impersonation, phishing, refund theft, and fraudulent tax preparers.
Tax season fraud can affect anyone, but older adults, first-time filers, young adults, immigrants, and non-native English speakers are often targeted more aggressively. Fraudsters tailor their approach to fear, inexperience, or language barriers to make scams feel urgent and believable.
The most common tax season fraud tactics are IRS impersonation calls, phishing emails or texts, refund theft using stolen identities, and scams involving fake tax preparers or online “tax hacks.” Many of these schemes use urgency, threats, or fake refund messages to pressure victims into acting quickly.
Consumers can reduce tax season fraud risk by using strong passwords and multi-factor authentication, avoiding public Wi-Fi for tax filing, and verifying unexpected tax messages directly through trusted sources. Monitoring bank, brokerage, and credit card accounts regularly also helps catch fraud earlier.
Financial institutions can help prevent tax season fraud by monitoring for unusual withdrawals, rapid liquidation followed by outbound wires, suspicious refund activity, and fear-based customer behavior. Training frontline staff, improving cross-channel visibility, and establishing clear escalation paths can help stop fraud before funds leave the institution.
ACH payments continue to grow, and so does the attention around them. As volumes increase and fraud schemes evolve, the 2026 Nacha Rule changes in risk management are raising expectations for how financial institutions monitor activity, use transaction data, and document risk management practices.
Industry data shows ACH transactions rising year over year, with Network payment volume increasing nearly 4.9% from 2024 to 35.2 billion payments in 2025. Attempted fraud is growing even faster. At the same time, the mix of credits and debits is shifting. While transaction counts are becoming more balanced, credits now account for roughly two-thirds of the dollar volume moving through the Network. That shift alone changes risk exposure.
Larger dollar credit transactions create different fraud dynamics than what traditional debit-focused monitoring programs were built around. Add in authorized push payment scams and transactions authorized under pretenses, and it becomes clear why the 2026 Nacha Rule changes expand expectations.
The Nacha Rule changes acknowledge this reality. They emphasize that monitoring should be risk-based, adaptable, and applied across both credits and debits, not narrowly focused on one side of the ledger.
Historically, some institutions focused more on debit monitoring due to unauthorized return thresholds. Under the 2026 Nacha Rule changes, that limited approach will not be sufficient.
Financial Institutions are expected to apply risk-based monitoring to both ACH credits and debits, including identifying transactions that may be unauthorized or authorized under pretenses.
Authorized under pretenses, transactions require different detection strategies. In these scenarios, a customer is manipulated into initiating a payment. The transaction appears authorized on its face, but the authorization was obtained through deception. Traditional unauthorized return metrics alone will not capture these events.
Institutions should evaluate whether their current systems, alert thresholds, and review processes account for behavioral red flags, unusual transaction patterns, and changes in customer activity that may signal elevated risk.
Another critical component of the Nacha Rule changes involves mandatory Company Entry Descriptions, such as PAYROLL and PURCHASE.
At first glance, this may appear minor. In practice, it supports more transparent communication across institutions and improves downstream monitoring. Standardization reduces ambiguity, enhances data quality, and supports more effective analytics.
The Nacha Rule changes recognize that better data supports better fraud detection. When descriptions are consistent and controlled, institutions are better positioned to identify anomalies and emerging risk patterns.
Governance over how these fields are assigned, validated, and monitored will become increasingly important.
Detection alone is no longer enough. The Nacha Rule changes elevate documentation from a supporting function to a central component of compliance.
Examiners will expect institutions to demonstrate how ACH risks are assessed, how monitoring rules are developed and tuned, and how alerts are reviewed and escalated.
Institutions should be prepared to articulate clearly:
Strong governance does not require excessive complexity. It requires clarity, consistency, and the ability to evidence your approach.
The Nacha Rule changes apply broadly across the ACH ecosystem, including:
Even institutions with lower ACH volumes are expected to maintain oversight and controls appropriate to their role. Volume alone does not eliminate risk. Governance, monitoring, and documentation should align with the institution’s size, complexity, and risk profile.
Financial Institutions can begin preparing now by taking practical steps that strengthen both compliance and fraud resilience.
Institutions that act early will be better positioned for exams, audits, and evolving fraud schemes. More importantly, they will build a stronger and more resilient ACH program.
The 2026 Nacha Rule changes raise expectations, but they also reinforce flexibility. Nacha continues to emphasize a risk-based approach tailored to each institution.
These changes underscore that financial institutions must understand their ACH activity, monitor it appropriately, and document how they manage risks. Those who approach the Nacha Rule changes strategically rather than reactively will strengthen both their compliance posture and their fraud resilience.
Preparing for the 2026 Nacha Rule changes does not need to be manual or fragmented. Abrigo supports banks and credit unions with fraud detection technology, ACH risk assessments, and Advisory Services designed to strengthen monitoring frameworks and maintain examiner-ready documentation.
Our approach respects the complexity of your work. We partner with financial institutions to enhance existing programs, align monitoring with evolving fraud risks, and support sustainable, risk-based compliance.
The regulatory landscape will continue to evolve. A proactive approach today will position your institution to manage ACH risk with confidence tomorrow.
The 2026 Nacha Rule changes raise expectations for ACH risk management by requiring financial institutions to apply risk-based monitoring across both ACH credits and debits. They also place greater emphasis on documenting how ACH risks are assessed, monitored, reviewed, and escalated.
The changes matter because ACH volumes are growing, fraud is evolving, and larger-dollar credit transactions create different risks than many debit-focused monitoring programs were built to detect. They also reflect the need to identify transactions that may be unauthorized or authorized under pretenses.
Financial institutions are expected to monitor both ACH credits and debits using a risk-based approach tailored to their activity and risk profile. Monitoring should account for behavioral red flags, unusual transaction patterns, and customer activity changes that may indicate elevated fraud risk.
The rules introduce mandatory standardized Company Entry Descriptions such as PAYROLL and PURCHASE. Standardization improves transparency, reduces ambiguity, supports better analytics, and helps institutions detect anomalies more effectively.
Financial institutions should refresh their ACH risk assessments, review monitoring coverage for credits and debits, evaluate alert thresholds and case workflows, update policies for current fraud typologies, and strengthen controls over Company Entry Descriptions. Targeted training for operations, fraud, and compliance teams is also important.
This article was originally posted on CUInsight.com on March 5, 2026.
Stablecoins are a category of digital assets designed to keep a steady value, typically pegged to a fiat currency such as the U.S. dollar. By minimizing the price volatility often associated with other cryptocurrencies, stablecoins aim to offer a digital payment medium that blends the efficiency of blockchain technology with the dependability of traditional money.
Stablecoins come in several forms:
Other models to be aware of:
What makes stablecoins relevant to credit unions is their potential to enable near-instant payments, reduce transaction costs, and provide 24/7 settlement, enhancing member services for remittances, merchant payments, payroll, and more.
An important development for credit unions is NCUA’s proposed rule under the Guiding and Establishing National Innovation for U.S. Stablecoins Act (GENIUS Act). On February 11, 2026, the NCUA issued a Notice of Proposed Rulemaking to create a framework for credit union-related stablecoin issuers known as “permitted payment stablecoin issuers” (PPSIs). Credit unions may issue stablecoins only through an NCUA-licensed subsidiary, rather than directly as a product of the credit union.
Key aspects of the proposal include:
In addition, NCUA’s Financial Technology and Digital Assets resource page confirms that digital assets—including stablecoins—are being studied as part of broader fintech and blockchain developments, and that NCUA remains committed to balancing innovation with safety and soundness.
While stablecoins offer potential benefits, they also introduce specific risks that credit unions must manage:
Stablecoins are still coming into defined supervisory territory, but the NCUA proposal signals that regulators expect strong oversight of any issuance. Credit unions should also remember that any stablecoin holdings or transactions do not carry federal share insurance, and members should be informed of this distinction.
Stablecoins can be redeemed rapidly—a positive for members, but a potential strain on liquidity if redemption pressure spikes unexpectedly. Institutions need to model stress scenarios that include high-volume stablecoin redemptions.
If credit unions offer stablecoin access through third-party providers or fintech partners, they must implement robust due diligence, contract governance, and ongoing oversight. NCUA guidance on third-party relationships for digital assets underscores the need for strong risk measurement and due diligence.
Blockchain transactions and stablecoin flows can frustrate traditional AML monitoring systems. Compliance programs should be assessed and updated to detect and report suspicious activity effectively.
Stablecoins may not be right for every institution right now, but there are certainly benefits to engaging in the stablecoin space. If your credit union wants to bolster member payment services, faster, low-cost payment rails could enhance member satisfaction, especially for remittances and P2P transfers. Joint initiatives with trusted partners can enable credit unions to offer digital asset access while appropriately managing risk.
To safeguard member interests and prepare for digital-asset-related developments:
Stablecoins are moving from theoretical to regulated reality. For credit unions, the path forward will likely be measured and member-centric, prioritizing safety and soundness to maintain member trust.
Stablecoins are digital assets designed to maintain a stable value, usually by being pegged to a fiat currency such as the U.S. dollar. For credit unions, they matter because they can support faster payments, lower transaction costs, and 24/7 settlement.
Credit unions should pay attention to stablecoins because they may expand member payment options for remittances, merchant payments, payroll, and peer-to-peer transfers. They also introduce new operational, compliance, and third-party oversight considerations that require active governance.
A credit union should update enterprise risk assessments, strengthen AML and fraud monitoring, educate the board, monitor NCUA and GENIUS Act developments, and apply enhanced due diligence to fintech partners. A measured, member-centric approach helps balance innovation with safety and soundness.
Stablecoins are moving into a more defined regulatory framework for credit unions. The article notes that under NCUA’s proposed rule tied to the GENIUS Act, credit unions would issue stablecoins only through an NCUA-licensed subsidiary rather than directly.
Stablecoins create regulatory, liquidity, operational, third-party, AML, and fraud risk for credit unions. Institutions need to consider rapid redemption pressure, partner oversight, updated suspicious activity monitoring, and the fact that stablecoin holdings or transactions do not carry federal share insurance.
Consulting firms specializing in credit union valuation services bring specialized expertise in financial modeling and valuation techniques ro help institutions navigate the maze of uncertainty in merger discussions.
The landscape of credit unions is constantly in flux, with consolidation trends continuing to accelerate. Mergers and acquisitions offer potential for growth but also bring complexities, especially in determining the fair value of assets and liabilities.
For credit union CEOs and CFOs evaluating merger opportunities, purchase accounting and fair value measurement under GAAP have become critical strategic considerations rather than technical afterthoughts. Consulting firms specializing in credit union valuation services can be crucial partners as institutions navigate the maze of uncertainty in merger discussions.
It’s important to understand the advantages of fair value assistance in mergers and to seek a third-party specialist with the qualities outlined below. Doing so can help credit unions choose the best partner possible for credit union merger accounting and enterprise value analysis in the evolving accounting and regulatory environment.
The following benefits of using a third-party fair value specialist can help credit unions weigh their options and understand what to look for in a partner:
Accuracy and transparency. Firms specializing in fair value bring specialized expertise in financial modeling and valuation techniques. Their work guarantees that valuation during the merger process is accurate, transparent, and adheres to accounting and regulatory standards. These qualities will help avoid overvaluation or undervaluation during the credit union fair value accounting process and support defensible conclusions under increased audit and regulatory scrutiny.
Objectivity. Fair value specialists provide independent, objective opinions. This objectivity is especially critical when emotions run high during credit union mergers, providing a neutral baseline for negotiations.
Risk mitigation. Identifying and quantifying potential economic risks associated with financial assets and liabilities becomes much easier with a fair value expert on your team. This helps mitigate financial risks and ensure the long-term success of the merged credit union.
Regulatory and GAAP alignment. Recent accounting developments reinforce the importance of disciplined application of purchase accounting in credit union mergers. Issued by the Financial Accounting Standards Board, ASU 2025-08 amends guidance under ASC 326 and directly affects how acquired financial assets are accounted for in business combinations under ASC 805.
The update reinforces the need for:
ASU 2025-08 (Topic 326) revised accounting for purchased-credit-deteriorated, or PCD, loans and eliminated the “double count” of the credit mark and CECL provision (while reducing the amount of accretable discount).
Because PCD treatment directly affects the purchase price allocation, a qualified specialist helps ensure merger-related accounting aligns with current GAAP and evolving disclosure expectations. ASU 2025-08 applies to fiscal years beginning after Dec. 15, 2026, but early adoption is permitted.
Why is enterprise value important? As mutually owned entities, credit unions do not exchange financial consideration in a merger transaction; hence, there is no “purchase price.” The “purchase price” is the cornerstone of determining resulting goodwill (or bargain purchase gain). Enterprise value, or the fair value of the acquired credit union, becomes the imputed “purchase price” of the transaction for purposes of business combination accounting under ASC 805 and acts as the baseline in the purchase price allocation exercise for goodwill determination.
Enterprise value considers more than just the book value of a credit union's assets and liabilities. It incorporates the entity's earning potential, market position, intangible assets, and prospects. This results in a more comprehensive and realistic valuation.
A thorough understanding of the to-be-acquired credit union’s enterprise value guides credit unions in making well-informed merger decisions. It clarifies the combined entity's pro forma capitalization and value proposition, ensuring the merger aligns with strategic goals.
A fair value firm will provide the following support measures during the credit union merger:
Due diligence support. Fair value firms review the target credit union's financial statements, loan portfolios, deposit relationships, and other vital metrics to support a defensible fair value measurement under GAAP. This in-depth due diligence uncovers critical information and potential red flags and clearly indicates fair value before entering into a transaction. In a dynamic rate environment, this includes disciplined credit mark analysis, rate sensitivity modeling, and core deposit intangible studies.
Negotiation power. Fair value assessments can be the foundation for strong negotiation positions. Executives can enter credit union merger accounting discussions with accurate data and justifications for value, potentially leading to more favorable terms.
Post-merger integration. Fair value specialists also support a smooth integration process at closing by assisting with purchase price allocation, goodwill or bargain purchase gain calculations, and documentation required under ASC 805. Post-closing, fair value firms can assist with ensuring appropriate accounting entries and can support the ongoing accretion, amortization, and income recognition required under GAAP. Income recognition software can also streamline Day 2 accounting, taking in the purchase marks, handles both base and accelerated accretion, and ties back to CECL where needed. Everything is transparent and auditable at the loan level.
Once you have made the decision to use a fair value firm to help facilitate your credit union merger, look for the following qualities as green flags for selecting assistance:
Fair value analysis, CECL alignment, and day two income recognition operate as an interconnected framework within credit union merger accounting in the current regulatory environment. Partnering with a valuation firm that understands business combination accounting in its entirety helps credit union leaders make informed strategic decisions. It ensures accurate valuations and smoother transitions.
By incorporating enterprise value analysis, credit unions gain a holistic understanding of their potential partners, maximizing the chances of a successful merger.
Fair value in credit union merger accounting is the estimated value of acquired assets, liabilities, and the overall institution for business combination accounting under GAAP. It creates the baseline for purchase price allocation, goodwill or bargain purchase gain analysis, and Day 1 merger accounting.
Fair value matters because credit union mergers do not involve a traditional purchase price, so enterprise value becomes the imputed price used for accounting purposes. A strong fair value analysis helps leaders evaluate the transaction more accurately and supports better strategic, financial, and reporting decisions.
A fair value partner brings specialized valuation expertise, objectivity, and support for defensible conclusions under GAAP and audit scrutiny. That helps credit unions improve accuracy, quantify risk, and reduce the chance of overvaluation, undervaluation, or incomplete merger accounting.
A fair value firm supports due diligence, enterprise value analysis, purchase price allocation, goodwill or bargain purchase gain calculations, and Day 2 accounting needs such as accretion, amortization, and income recognition. It also helps institutions document assumptions and measurements in a way that is transparent and auditable.
Credit unions should look for experience in credit union mergers, strong valuation credentials, clear communication, and a full understanding of how valuation, CECL, income recognition, and business combination accounting work together. A partner with that integrated perspective is better equipped to support merger accounting from due diligence through post-close reporting.
Small businesses are the lifeblood of local communities. They represent 99.9% of American firms, yet only 42% report that their financing needs are fully met. Small business lending is an ideal opportunity for growth.
For many credit unions, however, small business lending feels inherently riskier than consumer lending. The reality is more nuanced. With the right tools and processes in place, small business lending can become a powerful driver of member loyalty.
Key topics covered in this post:
Compared to consumer lending, small business lending introduces more moving parts. Cash flows fluctuate, financial statements vary in quality, and collateral can be complex. Underwriting small business loans often requires judgment calls based on projections, management experience, and local market conditions.
On top of that, regulatory expectations continue to evolve. The CFPB’s Section 1071 rule, for example, requires extensive data collection and reporting for covered lenders. This level of transparency can heighten scrutiny and make credit unions feel that every small business credit decision carries added compliance and reputational risk.
In other lending segments, such as commercial real estate (CRE), regulators have emphasized the importance of monitoring concentrations and conducting stress testing to evaluate vulnerabilities. That same discipline increasingly applies to broader small business portfolios. When expectations rise, perceived risk often rises with them.
All of this can make small business lending for credit unions feel harder to control and therefore riskier than standardized consumer products.
Three common realities often amplify risk perception:
Relationship-driven decisions. Credit unions pride themselves on knowing their members. That strength can also introduce subjectivity when underwriting relies heavily on personal knowledge rather than consistent, documented analysis.
Less standardized data. Unlike consumer lending, small business financials are not uniform. Tax returns, projections, and internally prepared statements vary widely in format and reliability.
Inconsistent underwriting approaches. When lenders rely on individual styles or legacy processes, risk ratings, exceptions, and documentation may lack consistency across the portfolio.
None of these factors make small business lending inherently unsafe. But without a small-business-specific framework, they can create uncertainty and undermine confidence in credit union risk management.
In practice, risk tends to build not because a borrower is a small business, but because the credit union lacks:
Without these guardrails, even a well-intentioned, community-focused lending strategy can create blind spots. Strong credit union risk management requires data-driven insight. For example, regulators have long encouraged institutions with significant CRE exposure to monitor concentration levels and use stress testing to understand how changes in rates or collateral values could affect capital. The same principle applies to small business lending.
Confidence grows when lending and risk teams share a common, transparent view of both individual loans and the portfolio as a whole.
Better insight enables:
Improving credit union risk management is not just about protecting capital. It directly impacts member experience, happiness, and retention.
When underwriting processes are standardized, decisions are faster, and expectations are communicated more transparently, so relationship managers can spend more time advising and less time chasing documents. Technology can support this shift by standardizing spreading, risk rating, documentation, and portfolio reporting. Automating some small-business lending processes can free up experienced staff to focus on relationship-building and sound decision-making.
Small businesses value certainty. A frictionless digital application combined with disciplined credit analysis reduces back-and-forth and builds trust. When a credit union can say “yes” confidently (or even “not yet” with clear next steps for a business to take), it strengthens the relationship.
When small businesses feel understood and supported, they are more likely to keep deposits, seek additional services, and refer peers.
The bottom line
Small business lending is not inherently riskier than consumer lending, but it can be more complex. Successful small business lending requires intentional structure, consistent processes, and portfolio-level visibility.
Small business lending feels riskier because borrower cash flows are less predictable, financial statements are less standardized, collateral can be more complex, and underwriting often requires more judgment. Compliance expectations and portfolio monitoring requirements can also make the risk feel harder to control.
No. Small business lending is more complex than consumer lending, but it is not inherently riskier when a credit union has strong underwriting standards, updated risk ratings, clear exception tracking, and portfolio-level visibility.
Risk increases when underwriting is inconsistent, decisions rely too heavily on relationship judgment, borrower data is hard to compare, and policy exceptions are not tracked clearly. Limited visibility into concentrations, borrower stress, and economic scenario impacts can create additional blind spots.
Credit unions can manage small business lending risk more effectively by standardizing underwriting, credit memos, risk ratings, exception tracking, and portfolio reporting. Scenario analysis and concentration monitoring also help teams identify emerging issues earlier and make more informed lending decisions.
Better risk management improves the member experience by making decisions faster, setting clearer expectations, and reducing unnecessary back-and-forth. Standardized processes and lending technology give relationship managers more time to advise members while helping small businesses get more consistent answers.
