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Nontraditional credit pathways are the new competition 

While credit unions continue to post solid lending results, a growing share of borrowing activity is occurring outside credit union channels. Younger consumers are actively using credit, but they are increasingly choosing nontraditional pathways to access it. As a result, institutions may need to look beyond funded loan volume to understand whether they are capturing future borrowers.

This article covers these key topics: 

Consumer lending still looks strong on paper

Credit unions continue to report healthy lending performance across several core measures. According to recent NCUA data, federally insured credit unions have continued to grow loans, assets, and membership while maintaining relatively stable credit performance. For many institutions, these results reinforce confidence that consumer lending remains healthy and resilient.

Those numbers primarily measure activity that has already reached the institution. They do not show how many consumers considered borrowing but chose another provider. They do not reveal which financing decisions occurred before a member ever visited a credit union website. And they do not capture borrowing activity that happens through channels outside the traditional application process.

In other words, traditional lending metrics can only tell us what entered the funnel, not what bypassed it.

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Younger borrowers are active but borrowing differently

Research from TransUnion shows Gen Z consumers are becoming credit active earlier and at higher rates than previous generations at similar ages. At the same time, the Consumer Financial Protection Bureau has documented the rapid growth of Buy Now, Pay Later financing and other point-of-sale credit options. Taken together, these trends suggest that younger consumers are not avoiding borrowing, but are increasingly encountering credit in new places.

A consumer shopping online may be offered financing at checkout. A large purchase may come with installment-payment options embedded directly into the buying experience. In many cases, the financing decision is made before the consumer actively shops for a loan. This creates a challenge for credit unions.

Historically, lenders competed when a borrower decided they needed credit. Today, that decision often occurs within a retail, digital, or fintech environment where the credit union may never be considered.

Relationships still matter, but they require time

The growth of alternative lending channels does not necessarily mean younger consumers no longer value financial guidance. Many borrowers still seek trusted advice when making major financial decisions, comparing financing options, or evaluating the long-term impact of borrowing. That has long been one of the credit union movement's strengths.

As borrowing channels become more fragmented, that strength may become even more important. While adopting digital lending can be a draw for younger members, credit unions are unlikely to out-fintech every fintech or outspend every digital lender. What they can offer is a combination of trusted relationships, financial guidance, and personalized service that many alternative lenders cannot easily replicate.

Automating can help free up lenders

Many lenders continue to spend significant portions of their day gathering documents, tracking down information, managing workflows, and completing other administrative tasks. But every hour spent on manual processes is an hour not spent engaging members, answering questions, or identifying borrowing needs before those needs are met elsewhere.

Investing in the right technology can help free lenders from routine administrative work, allowing them to spend more time on business development and customer relationships.

For credit unions seeking to engage younger consumer lending borrowers, lending efficiency can create capacity for the conversations and guidance that strengthen member relationships.

Get curious about new generations of members

Current lending performance is supported in part by long-established member relationships. Many credit unions continue to benefit from strong member engagement, with members maintaining borrowing relationships for years or decades. These consumers are often more likely to return to familiar institutions when financing needs arise. If younger consumers increasingly encounter credit through alternative channels, credit unions may need to find ways to engage them earlier in the borrowing journey. This starts with asking a different set of questions:
  • Where are members borrowing when they do not come to us?
  • What percentage of borrowing decisions never enter our application funnel?
  • Are lenders spending enough time building relationships before borrowing needs arise?
  • How would we know if younger consumer lending borrowers were disengaging before loan volume begins to decline?
These questions may provide a more forward-looking view of lending performance than funded volume alone. Combining streamlined lending operations with the relationship-focused approach that has always differentiated credit unions may better equip credit unions to reach younger consumer lending borrowers before financing decisions are made elsewhere.
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.

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FAQ

Why should credit unions be concerned about borrowing activity outside their institution?

Borrowing activity that occurs outside the credit union may signal changing consumer preferences and decision-making habits. If members increasingly choose financing options offered through retailers, fintechs, or digital platforms, credit unions may have fewer opportunities to build lending relationships that can lead to future products and services.

Why are younger consumers using alternative credit options?

Younger consumers increasingly encounter credit at the point of sale through Buy Now, Pay Later programs, embedded financing offers, and digital lending platforms. These options are often integrated directly into the purchasing experience, making them convenient and immediately accessible. While traditional loans remain important, many younger borrowers are exploring multiple credit channels depending on the purchase and situation.

Does strong loan growth mean a credit union is successfully reaching younger borrowers?

Not necessarily. Loan growth is an important measure of performance, but it only captures activity that reaches the institution's lending funnel. A credit union can experience healthy loan growth while still missing opportunities to engage younger consumers who are obtaining credit through alternative providers before ever considering a traditional loan application.

How can credit unions strengthen relationships with younger borrowers?

Building relationships with younger borrowers often starts before a loan application is submitted. Financial education, personalized guidance, proactive outreach, and convenient lending experiences can help credit unions remain relevant throughout the borrowing journey. Establishing trust early may increase the likelihood that consumers consider the credit union when future financing needs arise.

What role does lending automation play in member engagement?

Lending automation can help reduce the time lenders spend on administrative and manual tasks. By streamlining workflows, gathering information more efficiently, and accelerating decision-making, credit unions can create more capacity for lenders to focus on member conversations, financial guidance, and relationship-building activities that differentiate the institution from many alternative lending providers.

Shift toward trade-based business is a good thing for CFIs

AI is starting to influence career choices, and recent reporting suggests a growing number of young adults are moving away from white-collar tracks and toward skilled trades they see as more resilient. This shift could lead to more startups, more independent contractors, and more equipment-heavy Main Street businesses. For community financial institutions, that is a signal to look more closely at trade-based business lending.

Simpler processes for greater performance.

Equipment leasing software

The new generation of business owners

A Harvard Kennedy School survey found 59% of 18- to 29-year-olds view AI as a threat to their careers, while employment for young adults in AI-exposed jobs has fallen 16%. The same report said vocational-based community college enrollment has risen nearly 20% since 2020. NPR reporting has pointed in the same direction, describing a “toolbelt generation” and rising interest in vocational paths tied to HVAC, electrical, and wind-turbine work.

When more electricians, plumbers, HVAC technicians, welders, and contractors enter the market, one of their first steps is often equipment financing: a truck, a trailer, a compressor, a lift, or a set of specialized tools that allows them to take on jobs and bill customers. The bank or credit union that can engage a trade-based business customer is financing the machinery behind a revenue stream.

The Bureau of Labor Statistics projects that electricians will grow 9% from 2024 to 2034, heating, air conditioning, and refrigeration mechanics and installers will grow 8%, and plumbers, pipefitters, and steamfitters will grow 4%. Overall employment in installation, maintenance, and repair occupations is projected to grow faster than average over the decade.

Why equipment finance fits the borrower profile

For banks, trade-based business lending is especially attractive because equipment finance ties the credit decision to a tangible, income-producing asset. A truck, trailer, skid steer, or commercial HVAC unit does more than sit on a balance sheet; it helps the borrower generate the revenue that supports repayment. That gives lenders a financing structure that matches the way the business actually operates.

Equipment lending is often a better fit than a generic unsecured loan. Many newer trade businesses do not need broad corporate borrowing capacity on day one, but they do need the specific asset that helps them complete jobs, take on larger contracts, and move faster than their competition. A financing program built around the equipment purchase can meet that need without forcing the borrower into the wrong product.

Moving early to stay ahead

Banks that update underwriting, documentation, and product design to support this growing pool of borrowers will be a step ahead of their competitors. The first institution to build trust with a new contractor or small trade owner is often the one that gets the next request for a line of credit, a deposit account, treasury services, or a second piece of equipment.

Instead of chasing a trend, trade-based business lending is a strategic way to align the balance sheet with where the next generation of business owners is likely to emerge. As more young workers choose trades that feel stable in an AI-shaped economy, banks that understand the borrower’s tools, cash flow, and growth path will be better positioned to serve them.

Next steps for community financial institutions

The moral of the story is that AI is changing where people see opportunity. Some of that opportunity is moving into the trades, creating a pipeline of borrowers who are more asset-dependent, more local, and more relationship-driven than many banks and credit unions may expect.

AI is also speeding up financial institutions' workflows and changing borrowers' expectations regarding speed and digital capabilities. Modernizing their processes can keep community financial institutions competitive and help them allocate more time to personal relationships with members and customers. 

In addition to saving time and creating happier customers and partners, an automated equipment finance operation also helps the organization with:

  • Risk reduction: Automated audit trails and compliance checks reduce manual errors and documentation gaps.
  • Improved analytics: Integrated platforms centralize data across contracts, assets, and vendors—giving executives better insight into profitability, risk exposure, and performance trends.

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Improve monitoring for emerging credit risks

AI improves credit risk monitoring by analyzing portfolio data in real time and helping teams quickly identify trends, exceptions, and potential risk exposures. Learn why traditional monitoring falls short late in the cycle and why modernizing processes helps with AI adoption.

This article covers these key topics: 

A new phase for credit risk monitoring

Credit risk monitoring is entering a new phase. The fundamentals haven’t changed; sound judgment, defensible assumptions, and clear communication still matter.

But late-cycle conditions are exposing the limits of periodic, backward-looking reporting. The combination of modern data visualization and artificial intelligence (AI) offers a practical way to see emerging risk sooner, ask better questions in real time, and connect allowance work to day-to-day credit monitoring.

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Why traditional monitoring late cycle falls short

Credit risk has always been a science and an art. Institutions vary widely in their approach to credit risk modeling and monitoring. But many traditional credit risk models and processes share a common limitation: they rely on periodic data pulls, “black box” third-party models, and static assumptions. And in many cases, analysis is limited to retrospective/historical review.

Processes that rely entirely on past loss rates, monthly delinquency positions, and/or instrument-level probabilities of default (PDs) from a third party that haven’t been backtested against your own experience or that of named peers are increasingly insufficient this late into a credit cycle.

There are real advantages to evaluating or re-evaluating your approach to credit monitoring and adjacent process in the current environment. AI and modern visualization tools can help leadership charged with managing and monitoring credit risk by providing real-time data and trends, relevant industry data, and consolidating inputs and outputs from critical models such as allowance, stress testing, ALM, and deposit-related tools.

 

Moving beyond allowance in a vacuum

The allowance is the one area of credit modeling that directly impacts financial statements. The process is subject to external audit and examination. For these reasons alone, it’s common for institutions to modernize the process. As we all know, some choices can have a cascading effect throughout an organization, and this area is one of them.

When the allowance is managed in a spreadsheet or in a vacuum, the exercise becomes one of data entry, simple historical loss rates, storage, filed away spreadsheets, and canned reports. It becomes challenging to communicate inputs, assumptions, and results to anyone within the organization removed from the actual creation of the “answer.” To simply view output trends becomes a time-consuming exercise for everyone involved.

There are also approaches that may seem advanced, such as some third-party provided PD and LGD that haven’t even been backtested against your own experience, but they can’t be audited/reviewed. Nor can the default rates be explained by leadership. This severely limits the value of the entire process and leadership’s ability to let the allowance process become an integral part of credit risk monitoring.

Modernizing processes yields data accessibility

As leadership thinks about AI, it’s important to consider that one critical step in realizing the benefits is to begin modernizing processes in such a way that the inputs and outputs to key processes are accessible. For example, if the allowance is designed thoughtfully and not isolated to a spreadsheet environment or the result of a black box model, anyone in management could, at any moment and without request, observe through real-time visualization tools the following key allowance and credit monitoring trends:

  • Segment-level allowance level trends (obvious)
  • Segment-level realized default rate trends relative to default rate assumptions used in the allowance
  • Various economic scenarios and resulting segment-level allowance levels and underlying default rate expectations
  • Allowance change attribution (drivers of change – balance, forecast, qualitative, etc.)
  • Input and assumption trends
  • Qualitative factor allocation trends
  • Relevant industry data and trends for relativity (coverage ratios, default rates, loss rates, loan growth, etc.)

That visibility can turn the allowance from a quarterly (or monthly) output into an always-on monitoring lens—one that leadership can review, discuss, and challenge without waiting on a report run.

On top of yielding real-time visualization, communication, and quality of the output, the organization of inputs, assumptions, and underlying data enables financial institutions to now experience real benefits from AI. It is no longer a difficult lift and paves the way to move beyond theory and into tangible benefits.

What AI looks like in day-to-day credit risk management

two people reviewing financials on a tabletLet’s take the above example one step further. While viewing the real-time data, anyone in management may see something that stands out to them and prompt AI to “list all of the loans that have downgraded between December and March” or “summarize all delinquencies in Commercial by industry code.” The point: you can now react to what the data is showing with instant answers, without data pulls, spreadsheets, or difficult-to-communicate requests to others in the organization.

Once that foundation is in place, AI stops being theoretical and becomes usable, starting with simple, high-value questions that connect what you’re seeing to what needs attention.

 

Real-time portfolio and concentration monitoring

CRE exposures, relationship concentrations, geographic risks, loan-structure anomalies, exception tracking, and borrower-level stress are just a few examples of rapidly evolving items that may require frequent threshold mapping, tracking, and monitoring.

Traditional reporting can be time-bound (periodic) and relatively rigid, often proving difficult or requiring custom work to drill down into the details. AI-powered monitoring systems can not only track concentrations continuously but also allow user interaction in a way that wasn’t possible without report-writing skills or specific requests of those with report-writing skills. They allow users not only to drill down into the underlying data, but also to ask questions beyond the data shown.

For risk and finance teams, AI-powered environments offer new time-saving abilities and avenues of understanding. Imagine you’re reviewing your daily dashboard, specifically, utilization, and you notice it’s increasing beyond historical trends. You prompt, “list the loans with the largest increase in utilization with a 6-month trend of their respective days past due.” You notice that a few loans with increasing utilization have gone from zero to 5,10, or 15 days past due. AI then asks you, “Would you like this to be included in your dashboard in the future?”

You’ve avoided pulling 6 months of loan files, organizing data, and writing formulas in a spreadsheet (or requesting that someone else do this). Instead, you get immediate information and have improved the shared dashboard for others in your organization. Just as important, this approach helps teams move upstream—spotting patterns that often show up before delinquency forces the conversation.

When it comes to borrower-level stress, it generally, doesn’t appear overnight. Often, there are subtle changes early on:

  • Slower prepayment patterns
  • Higher/increasing utilization
  • Industry performance decline (external data)
  • Economic pressures
  • Deposit/cash depletion

Instead of waiting for delinquency metrics to materialize, AI provides efficient ways to identify potential trends and research their specifics before taking action. Ultimately, this type of monitoring strategy improves mitigation options.

Judgment remains central; AI strengthens it

Mature woman and young man reviewing documentsCredit risk management still requires experienced practitioners to interpret results, challenge assumptions/recommendations, and to consider qualitative information in decision-making. AI can efficiently provide information in a way that offers visibility, clarity, and insight into current and emerging risk patterns.

Institutions that choose to silo important credit risk functions into spreadsheets, black box third-party tools, and/or stagnant software risk falling behind. There must be a credible path for AI to, in real time, access inputs, outputs, and peripheral data in order to realize tangible benefits.

For leadership, the objective remains similar. Lead your teams with vision. Produce reliable and defensible channels of information. Efficiently (or autonomously) distribute the information so that everyone is making decisions with similar and sound knowledge. AI simply provides a more efficient and powerful set of tools to achieve that objective.

Adopt AI with confidence and control. Abrigo Advisory Services can help.

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FAQs

What is AI in credit risk management?

AI in credit risk management uses advanced analytics and natural language interaction to help financial institutions monitor portfolio performance, identify emerging risks, and analyze large volumes of credit data more efficiently. It supports decision-making by providing faster access to insights while keeping human judgment at the center.

How does AI improve credit risk monitoring?

AI improves credit risk monitoring by analyzing portfolio data in real time and helping teams quickly identify trends, exceptions, and potential risk exposures. This allows institutions to investigate issues sooner instead of relying solely on periodic reports and historical performance reviews.

Why are traditional credit risk monitoring methods becoming less effective?

Traditional monitoring approaches often rely on static reports, historical loss data, and periodic reviews that may not capture changing risk conditions quickly enough. In a late-cycle environment, emerging risks can develop between reporting periods, reducing visibility and delaying response times.

How can AI help identify emerging borrower stress?

AI can help detect early warning indicators such as increasing credit utilization, declining industry performance, reduced deposit balances, and changing payment behavior. Identifying these signals before delinquency occurs gives institutions more time to evaluate and mitigate potential credit risks.

What role does data accessibility play in successful AI adoption?

Data accessibility is a foundational requirement for effective AI implementation. When credit risk, allowance, and portfolio data are centralized and readily available, AI tools can generate meaningful insights, answer questions quickly, and support real-time monitoring across the organization.

Can AI replace human judgment in credit risk decisions?

No. AI is designed to enhance, not replace, human expertise. Credit professionals remain responsible for interpreting results, challenging assumptions, incorporating qualitative factors, and making sound risk management decisions based on a complete understanding of the institution's portfolio.

Rising losses after a credit stress lull

Recent industry data shows rising delinquencies, increasing charge-offs, and higher provision expense across multiple loan categories. While today's conditions are nowhere near the levels experienced during the Great Recession, the direction of the trends is noteworthy. More importantly, the pressure is not concentrated in one segment of the portfolio. Instead, signs of stress are appearing across consumer, commercial, and real estate lending.

During a recent Abrigo webinar,  Dean Rohne, principal in the Financial Institutions Group at Doeren Mayhew, examined industry trends and discussed how credit unions can better understand and monitor emerging risk. His message was straightforward: The industry is seeing a meaningful shift in credit conditions, and institutions that understand where risk is building will be better positioned to manage it.

Credit performance is moving away from recent norms

One challenge in evaluating current credit risk is that many credit union leaders naturally compare today's performance to the unusually strong years that followed the pandemic. However, Rohne suggested taking a longer view. Looking beyond the pandemic helps remove the distortion created by stimulus programs, elevated savings balances, and excess liquidity that temporarily suppressed delinquency and losses.

The same pattern appears in charge-off data. Net charge-offs across credit unions have increased from historical levels that generally hovered around 40 to 50 basis points to levels closer to 80 basis points today. Provision expense has also increased significantly as institutions adjust reserves to reflect changing portfolio performance.

These trends matter because they directly affect profitability. Higher losses require larger provisions, which put pressure on earnings and increase uncertainty around forecasting and budgeting. More importantly, they signal that the industry is operating in a different credit environment than it was just a few years ago.

You might also like this resource: “A banker’s guide for CECL compliance and backtesting.”

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Credit cards are often the first place stress appears

Among consumer lending products, credit cards are showing some of the clearest signs of pressure. Credit card delinquency has climbed to levels not seen for several years, with losses following a similar trajectory. While every institution's portfolio will differ, the trend is significant because credit cards frequently serve as an early indicator of borrower stress.

When household budgets tighten, consumers often rely more heavily on revolving debt. Eventually, higher balances, elevated interest costs, and competing financial obligations can begin to affect repayment performance.

Because of this dynamic, credit card portfolios often deteriorate before other segments of the consumer portfolio show meaningful weakness. Rising credit card delinquency may be providing an early signal about broader pressures affecting members.

For credit unions, monitoring these trends can offer valuable insight into how consumer financial health is evolving across the membership base.

Used auto, commercial, and real estate portfolio challenges

Industry delinquency rates for used vehicles have remained elevated, generally hovering around 1.0% to 1.1% in recent years. While rising delinquency is concerning on its own, many institutions are also facing a second challenge: increased loss severity.

Vehicle values surged during and immediately after the pandemic, creating unusually favorable conditions for lenders. In many cases, repossessed vehicles retained enough value to significantly reduce losses. That dynamic has shifted.

As used vehicle values normalize, some borrowers who are deeply underwater on their loans are choosing to surrender vehicles. When collateral values have declined, the resulting loss can be substantially larger than what institutions experienced just a few years ago.

For credit unions with significant auto concentrations, understanding both delinquency trends and collateral value trends is becoming increasingly important. A portfolio may appear manageable based solely on delinquency metrics while still producing larger-than-expected losses when defaults occur.

Commercial lending

Commercial loan delinquency across credit unions has approached or exceeded 1% in recent periods, while business bankruptcy filings have increased nationally. Taken together, these indicators suggest that some business borrowers are facing growing financial strain.

The challenge with commercial portfolios is that deterioration can develop gradually before becoming visible through traditional delinquency reporting. That makes proactive monitoring especially important. Credit unions should evaluate risk ratings, industry concentrations, geographic exposure, borrower performance, and emerging trends that could affect repayment capacity.

As Rohne noted during the webinar, effective credit risk management requires more than reviewing delinquency reports. It requires identifying potential weaknesses before they become actual losses.

Real estate

Real estate lending presents a different story. Delinquencies are increasing across the industry, yet many credit unions have not experienced a corresponding increase in losses. In many cases, strong property values and borrower equity have helped limit loss severity. The question is whether those conditions will continue.

Future performance will depend heavily on local market conditions, housing supply, and property values. Markets with persistent housing shortages may continue to support collateral values even if delinquencies rise. Other markets could experience greater pressure if economic conditions weaken or home prices soften.

This is one reason why broad national statistics only tell part of the story. Credit unions should understand how local economic conditions influence the specific risks within their own real estate portfolios.

Understanding the story behind the numbers

Delinquency rates and charge-offs are important, but they rarely tell the complete story. Credit unions should examine portfolio performance through multiple lenses, including credit score migration, vintage analysis, concentration analysis, loan-to-value trends, debt-to-income ratios, and changes in underwriting quality. These tools help institutions identify where risk is changing and why.

For example, a portfolio may show stable delinquency today while credit scores across the borrower base are steadily deteriorating. Likewise, a particular loan vintage may be driving losses while newer originations perform well. Understanding those distinctions can lead to more informed lending decisions and stronger reserve estimates.

This analysis becomes especially valuable when supporting CECL assumptions. Institutions that can clearly explain where losses are occurring, what is driving them, and how conditions are changing are often better positioned to support their allowance methodology.

As Rohne described it, credit unions should focus on telling their "credit risk story" using data that explains both current performance and future expectations. The objective is to build the monitoring, reporting, and governance processes necessary to identify emerging risks early and respond effectively. Institutions that understand where risk is building today will be in a stronger position to manage tomorrow's challenges.

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.

 

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What financial institutions need to know about illegal cannabis

Illegal cannabis activity is no longer confined to hidden grow operations. Increasingly, it is embedded in otherwise legitimate businesses such as vape shops, CBD retailers, and even licensed cannabis dispensaries.

This article covers these key topics:

Why this risk is increasing

For financial institutions, the cannabis gray area creates a nuanced and growing risk. Recent cannabis enforcement actions across the United States make one thing clear. Illegal activity is evolving, and traditional due diligence alone may not be sufficient to detect it. 

Businesses that appear compliant on the surface may be operating outside regulatory boundaries in ways that are not immediately visible through standard onboarding or monitoring processes.While cannabis policy continues to shift, marijuana remains illegal at the federal level, and state regulations vary significantly. This inconsistency creates an opportunity for both compliant businesses and those operating outside the law. It also creates complexity for financial institutions that must navigate conflicting legal structures while maintaining effective compliance programs.

Recent enforcement actions highlight connections between illegal cannabis activity and broader financial crime risks, including:

  • Organized crime networks
    • Illicit trafficking involving drugs, weapons, and cash
    • Unlicensed or non-compliant retail operations

For banks and credit unions, the risk is not always obvious. Many of these businesses maintain professional storefronts, active websites, and seemingly legitimate operations. This makes it easier for illicit activity to move through the financial system undetected, particularly when controls rely heavily on initial customer due diligence rather than ongoing monitoring.

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The evolving hemp landscape

Recent changes and clarifications around hemp laws are adding another layer of complexity for financial institutions. The Agriculture Improvement Act of 2018, commonly known as the 2018 Farm Bill, legalized hemp by removing it from the Controlled Substances Act and defining it as cannabis containing no more than 0.3 percent tetrahydrocannabinol (THC) on a dry weight basis. While this created a legal pathway for hemp products, it also introduced a regulatory gap that has been widely exploited.

Hemp-derived cannabinoids such as delta-8 THC, delta-10 THC, and other synthetic or semi-synthetic variants have rapidly expanded in the market. These products are often marketed as legal alternatives to marijuana, yet their legal status remains inconsistent. The DEA’s 2020 Interim Final Rule clarified that synthetically derived tetrahydrocannabinols remain illegal as a controlled substance. However, inconsistent enforcement and differing interpretations across jurisdictions have created uncertainty for businesses and financial institutions alike.

At the state level, regulators are increasingly moving to address these gaps. Throughout 2024 and 2025, multiple states have enacted or proposed restrictions on intoxicating hemp products, including bans on certain THC variants, potency limits, and stricter licensing requirements. This evolving patchwork of rules increases the likelihood that businesses may, intentionally or unintentionally, sell products that are not compliant with their jurisdiction's laws.

For financial institutions, this means the risk is no longer limited to traditional cannabis businesses. Hemp, CBD, and vape retailers may present similar or even heightened risk, particularly when product lines include intoxicating or ambiguously regulated compounds. Without a clear understanding of what is being sold, institutions may unknowingly provide services to businesses operating outside legal boundaries.

 

Identifying risk

Financial institutions can enhance existing due diligence processes with relatively simple but effective steps.

Leverage open-source intelligence

  • Review Google listings and customer photos
    • Examine business websites for product inconsistencies
    • Monitor social media for product promotion and branding

Monitor enforcement activity

  • Track local and national news
    • Cross-reference business names and ownership with your customer base
    • Identify geographic areas or industries with increased enforcement activity

Apply enhanced due diligence when warranted

  • Conduct additional verification for higher-risk customers
    • Consider site visits where appropriate
    • Implement ongoing monitoring rather than relying solely on onboarding reviews

Importantly, illegal products are not always visible. Some businesses intentionally limit access or visibility, underscoring the need for layered, continuous due diligence.

 

Key red flags

Incorporate these indicators into monitoring and investigation workflows:

  • Products that appear inconsistent with state cannabis laws
    • Elevated or unexplained cash activity
    • Rapid growth that does not align with the business model
    • Customer feedback referencing high-potency or questionable products
    • Connections to multiple entities, states, or prior enforcement actions

Individually, these indicators may not be conclusive. However, when combined, they can signal increased risk and warrant further review.

 

Strengthening risk management

To mitigate exposure, financial institutions should take a proactive and risk-based approach.

Reassess risk ratings
Businesses adjacent to cannabis, such as vape and CBD retailers, may warrant higher risk classification based on evolving enforcement trends.

Validate licensing and compliance
Use state resources to confirm licensing status and understand product limitations. Do not assume that licensing alone indicates full compliance.

Enhance transaction monitoring
Look for patterns that may indicate illicit sales or distribution, including unusual cash activity or inconsistencies with the stated business purpose.

Incorporate ongoing review processes
Risk in this space is dynamic. Periodic reviews are essential to maintaining an accurate, up-to-date risk profile.

 

The bottom line

Cannabis enforcement actions are signaling a clear shift. Illegal activity is becoming both more sophisticated and more visible. For financial institutions, the challenge is not only identifying licensed cannabis businesses but recognizing when otherwise legitimate customers may be operating outside regulatory boundaries.

A proactive, risk-based approach grounded in enhanced due diligence and ongoing monitoring can help institutions identify exposure earlier, strengthen compliance programs, and stay ahead of this evolving threat.

 

 

FAQs

Why should financial institutions be concerned about illegal cannabis?

Illegal cannabis-related activity is increasingly being found in businesses that appear unrelated to the cannabis industry, including vape shops, smoke shops, CBD retailers, and hemp product sellers. These businesses may market or sell products containing intoxicating cannabinoids or THC levels that exceed legal limits, creating compliance and reputational risks for financial institutions. As a result, institutions should evaluate cannabis-related risk beyond licensed dispensaries and apply appropriate due diligence to adjacent industries.

Does a state-issued cannabis or hemp license guarantee that a business is operating compliantly?

No. Recent enforcement actions have demonstrated that some licensed businesses continue to sell non-compliant or illegal products despite holding valid licenses. Licensing should be viewed as one component of a broader risk assessment rather than proof of ongoing compliance. Financial institutions should verify licensing status, understand applicable product restrictions, and conduct ongoing monitoring to identify potential violations that may arise after onboarding.

What are some practical ways financial institutions can identify potential illegal cannabis activity?

Financial institutions can strengthen detection efforts by combining traditional due diligence with ongoing monitoring and open-source intelligence. This may include reviewing business websites and social media pages, monitoring customer reviews, tracking local enforcement actions, and analyzing transaction activity for unusual patterns. Red flags such as unexplained cash volume, rapid growth inconsistent with the business model, sales of potentially non-compliant products, or links to multiple entities and jurisdictions may warrant additional review.

How often should financial institutions review customers operating in cannabis-adjacent industries?

Risk associated with cannabis, hemp, CBD, and vape-related businesses can change quickly as regulations evolve and enforcement priorities shift. Rather than relying solely on customer due diligence performed during onboarding, financial institutions should conduct periodic reviews based on the customer's risk profile. Ongoing monitoring of licensing status, product offerings, transaction activity, and enforcement developments can help institutions identify emerging risks early and maintain a more accurate assessment of potential exposure.

New AML/CFT program rule: What it requires & how to prepare

What the FinCEN proposal means for financial institutions

FinCEN emphasizes risk-based programs and redefines program effectiveness in the proposed rule to combat money laundering and terrorism financing.

 

This article covers these key topics: 

 

NPRM updating AML/CFT program requirements

Financial institutions have been anticipating meaningful Bank Secrecy Act reform for years. With the Financial Crimes Enforcement Network’s (FinCEN’s) proposed new anti-money laundering/countering the financing of terrorism (AML/CFT) program rule, the changes expected of banks and credit unions are now taking shape and will affect how institutions design, resource, and defend their AML/CFT programs.

While the proposed rule is not final, it signals a clear direction to focus on the highest-risk areas. Institutions that begin aligning now will be better positioned to manage risk, demonstrate effectiveness, and respond confidently during exams.

Staying on top of fraud is a full-time job. Let our Advisory Services team help when you need it.

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An intentional focus on risk-based programs

The AML Act of 2020, which amended the Bank Secrecy Act, directed FinCEN to reevaluate and update AML/CFT requirements to enhance program effectiveness, efficiency, and flexibility. It also required that FinCEN integrate its AML/CFT policy priorities into financial institutions’ risk assessments.

The FinCEN proposed AML rule would amend existing regulations and supersede the 2024 Program NPRM, with an effective date of 12 months after the final rule is issued.

At its core, the new AML/CFT program rule reinforces something many institutions already strive for but have struggled to operationalize consistently. Programs must be risk-based, dynamic, and aligned with FinCEN’s national priorities.

FinCEN makes it clear that financial institutions are expected to focus more attention and resources on higher-risk customers and activities while de-emphasizing lower-risk areas. This is a philosophical and structural shift that places the risk assessment process at the center of the entire program.

The risk-based approach gives institutions flexibility, but it also raises expectations. A program that is not clearly tied to identified risks or that cannot demonstrate how resources align with those risks may face increased scrutiny.

 

Required AML risk assessment processes

One of the most significant elements of the new AML/CFT program rule is the formalization of risk assessment processes as a required and ongoing component of the program.

Rather than relying on a static annual risk assessment, institutions are expected to use multiple processes to identify, assess, and document money laundering and terrorist financing risks across:

  • products
  • services
  • customers
  • geographies
  • distribution channels.

These risk assessment processes must also incorporate evolving inputs such as:

Best practices suggest updates every 12-18 months or when risk changes, not only on a fixed schedule.

For many institutions, this will require a more integrated approach to data, analytics, and internal communication. It may also require revisiting how risk assessment outputs are documented and used to drive decisions across the program.

 

Redefining program effectiveness

The proposed rule introduces an important distinction between establishing a program and maintaining it. Financial institutions must first establish an AML/CFT program that includes required components. Required components of an AML/CFT program include internal controls, independent testing, a designated compliance officer, and training. Once a program is established, the supervisory focus shifts to how financial institutions maintain and implement that program in all material respects.

This distinction matters because it changes how regulators may approach supervisory and enforcement actions. Under the new AML/CFT program rule, significant compliance actions are more likely to focus on systemic or material failures rather than isolated implementation issues, assuming the program is fundamentally sound.

At the same time, institutions are expected to identify and address warning signs such as:

  • backlogs
  • monitoring gaps
  • data issues.

Ignoring these indicators could still lead to supervisory action.

 

Aligning with FinCEN’s AML/CFT priorities

Another central component of the changes regulators are making to the AML/CFT program rule is the requirement that financial institutions review and incorporate FinCEN’s AML/CFT priorities into their programs. These priorities are designed to ensure that institutions address the risks that matter most at the national level.

However, FinCEN emphasizes that this is not a check-the-box exercise. A superficial review of priorities will not meet expectations. Institutions must evaluate how each priority could realistically manifest within their own risk profile and determine where to focus.

Institutions must balance FinCEN’s national priorities with their own risk exposure and clearly document the rationale behind those decisions. Incorporating these priorities into AML/CFT programs strengthens the value of information provided to law enforcement, particularly around threats to the U.S. financial system and national security. In turn, this supports more effective investigations, prosecutions, analytics, and policy decisions tied to AML/CFT priorities.

 

Practical implications of AML/CFT rules for financial institutions

While the final AML/CFT program rule is expected in the near term, implementation timelines may extend several years beyond issuance. Even so, the regulatory direction is clear. Financial institutions should begin preparing now by evaluating whether their current programs:

  • Clearly link risk assessments to controls and resource allocation

  • Incorporate relevant internal and external data sources

  • Address AML/CFT priorities in a meaningful, documented way

  • Identify and respond to operational weaknesses proactively

  • Leverage technology where it enhances effectiveness

As with other major regulatory changes, waiting until final rules are issued can create unnecessary pressure. Early assessment and incremental adjustments can make the transition more manageable.

Preparing for what comes next

The new AML/CFT program rule represents more than a regulatory update. It reflects a broader expectation that AML/CFT programs be demonstrably effective, adaptable, and aligned with real-world risks.

Financial institutions already manage complex compliance responsibilities alongside evolving fraud threats, staffing challenges, and technology decisions. This proposed rule does not reduce that complexity, but it does provide a clearer framework for prioritizing efforts.

Institutions that take a proactive, risk-based approach today will be better positioned to meet those expectations tomorrow while continuing to protect their organizations and the communities they serve.

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Document and be able to defend qualitative factors under CECL 

Financial institutions need to be able to explain and show how they developed Q factors for their allowance for credit losses. Modifications to the CECL calculation should be reasonable, supportable, and audit-ready.

Key topics covered in this post: 

Documenting & defending Q factors: Don't play a guessing game.

If you’ve been tasked with completing a CECL calculation, you know that qualitative factors (Q factors) can be one of the most challenging parts of the process. They require a mix of judgment, data, and defensibility—without clear instructions from regulators on exactly how to apply them.

But documenting and defending Q factors doesn’t have to be a guessing game. With the right approach, you can ensure your adjustments are well supported, transparent, and easy to explain to auditors and examiners.

Emerging tools such as AskAbrigo, an AI-powered banking agent, can help institutions streamline the qualitative factor process by surfacing relevant historical loan-level data, internal policies, economic support, and prior analyses to support more consistent and defensible decisions.

Here's a brief walkthrough of the key steps to identifying, documenting, and defending CECL Q factors.

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What are CECL qualitative factors?

CECL qualitative factors are adjustments made to credit loss estimates to account for risks that historical loss data and quantitative allowance models alone don’t fully capture. These CECL adjustments could encompass economic shifts, changes in your loan portfolio, or even adjustments in underwriting standards. Essentially, Q factors help ensure your reserve levels reflect not just past performance but also what’s happening now and what’s expected in the future.

Why is documenting Q factors so important?

The CECL standard (ASC 326) gives financial institutions significant latitude in how they apply qualitative factors. But that latitude makes it even more critical to document and justify their approach.

A well-structured CECL qualitative framework for the allowance for credit losses (ACL) enhances transparency, consistency, and defensibility. Generative AI tools like AskAbrigo can also support documentation efforts by helping institutions quickly retrieve historical analyses, lending policies, economic commentary, and loan-level data used in prior reserve decisions. This can improve consistency across reporting periods while reducing the manual effort involved in assembling supporting documentation for auditors and examiners.

Proper documentation is key for:

  • Regulatory and audit expectations – Examiners and auditors will expect clear, well-supported explanations for your CECL adjustments.
  • Transparency and consistency – Having a structured approach that is documented ensures your CECL methodology holds up over time and can be easily reviewed.
  • Defensibility – If you ever need to justify your reserve levels for credit losses, strong documentation will be your best friend.

How to identify CECL Q factors

Taking the time to understand how to identify CECL qualitative factors makes it easier to document and defend them later on.

Step 1: Start with your historical loss data

Your financial institution’s historical loss experience forms the foundation of your CECL estimate. Before making adjustments, understand what your baseline numbers look like and what trends they show.

Step 2: Consider current and expected conditions

This is where Q factors come into play. Ask yourself:

  • Are economic conditions improving or deteriorating (GDP growth, unemployment, inflation)?
  • Have there been any major industry shifts that could impact our borrowers?
  • Have we changed our lending practices (new products, risk appetite)?

AI-assisted workflows may also help institutions monitor economic indicators, summarize relevant market developments, and identify potential portfolio impacts that could influence qualitative adjustments.

Step 3: Align Q factors with risk categories

To keep things structured, organize your qualitative factors into key categories:

  • Economic environment – National, regional, and local economic trends.
  • Industry conditions – Market shifts, regulatory changes, sector-specific risks.
  • Portfolio changes – Credit mix, loan concentrations, underwriting adjustments.
  • Borrower-specific factors – Changes in creditworthiness, collateral values.
  • Regulatory/legal factors – Compliance changes, litigation risks.

Step 4: Develop a structured framework for Q factor adjustments

One of the most effective ways to ensure consistency and defensibility in your qualitative factor adjustments is to implement a structured Q factor framework, such as Abrigo’s Qualitative Adjustment Scorecard. The scorecard includes identification of high-water economic scenarios to define appropriate risk brackets and enables tailoring to each allowance pool.

This approach provides a reliable mechanism for measuring and benchmarking qualitative factors across your institution. Some benefits of a structured CECL qualitative framework include:

  • Consistency – Ensures Q factor adjustments are applied systematically across reporting periods.
  • Measurability – Establishes standards and benchmarks for assessment.
  • Back-testing capability – Allows comparison of past predictions with actual performance.
  • Comparability – Aligns your methodology with peer institutions for greater credibility.

CECL Q factor documentation checklist


To make documenting Q factors easier, here's a simple checklist to follow:

1. Identify and define relevant Q factors.

2. Support each factor with data from reliable sources.

3. Document the assumptions behind each adjustment.

4. Clearly explain any changes from previous periods.

5. Ensure consistency and maintain internal approvals.

6. Keep records organized for audit and regulatory review.

Consider adding a similar checklist to any CECL procedures your financial institution develops.

Documenting qualitative factors under CECL

Once you’ve identified your Q factors, documentation is the next step. A good approach includes:

  • Leveraging AI-assisted documentation tools - Solutions such as AskAbrigo can help institutions compile supporting evidence, summarize relevant portfolio trends, and draft consistent qualitative factor narratives for internal review.
  • Describing each factor – Be specific about what’s changing and why it matters.
  • Backing it up with data – Use economic reports, industry benchmarks, and internal analyses.
  • Explaining your adjustments – Show how each factor translates into an impact on your CECL estimate.
  • Tracking changes over time – Document shifts in methodology and why they occurred.

Tips for backing up your Q factors

Qualitative factors remain a hot-button issue during audits and exams because they can lead to wide swings in the allowance for credit losses if left unchecked. Follow these recommendations to ensure your bank or credit union can defend qualitative factors and their role in the allowance calculation.

  • Be consistent – Apply your methodology the same way each period unless justified otherwise.
  • Use hard data where possible – While Q factors involve judgment, tie them to real metrics whenever you can.
  • Involve the right people – Your finance, credit, and risk teams should all have a say in setting qualitative factors.
  • Anticipate questions – Auditors and regulators will ask about your methodology. Be ready with clear, well-organized documentation.
  • Enhance judgment  – Use AI to enhance (not replace) expert judgment. Generative AI tools can improve efficiency by surfacing relevant data and documentation, but management oversight and governance remain essential to ensuring adjustments are reasonable and supportable.

How often should financial institutions update Q factors?

At a minimum, review and update your Q factors quarterly. However, if there are significant changes in economic conditions, regulatory requirements, or your loan portfolio, you may need to adjust them more frequently.

Taking the time to document your CECL Q factors properly will make life easier during audits. More importantly, it will also ensure that your reserve levels reflect the real risks in your portfolio.

Need help getting started? Contact Abrigo today to learn how CECL solutions and AskAbrigo’s generative AI capabilities can help strengthen your Q factor methodology, streamline documentation, improve reporting visibility, and enhance audit readiness. Abrigo’s AI advisory services can also help banks and credit unions with compliant AI governance and strategy.

This blog was written with the assistance of ChatGPT, an AI large language model, and was reviewed and revised by Abrigo's subject-matter experts.

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FAQs

What are qualitative factors for CECL?

Qualitative factors for CECL are adjustments made to expected credit loss estimates when historical loss data does not fully reflect current or future credit risk. These changes to the allowance for credit loss help financial institutions account for changes in economic conditions, portfolio composition, underwriting practices, and other risks that may affect expected losses.

Why are CECL qualitative factors important?

CECL qualitative factors help ensure that allowance for credit losses estimates reflect real-world conditions rather than relying solely on historical performance, and auditors and examiners emphasize understanding Q factors’ influence. Qualitative factors allow banks and credit unions to incorporate reasonable and supportable forecasts, emerging risks, and portfolio changes that may influence future credit losses.

How should financial institutions document CECL qualitative factors?

Financial institutions should document the rationale, supporting data, assumptions, and methodology behind each qualitative adjustment. Q factor documentation should clearly explain why a factor was applied, how it affected the allowance estimate, and any changes made from prior reporting periods to support audit and regulatory review.

What are common examples of CECL qualitative factors?

Common qualitative factors used for CECL include changes in economic conditions, unemployment trends, loan portfolio concentrations, underwriting standards, collateral values, delinquency levels, and borrower credit quality. Institutions may also consider staffing experience, regulatory developments, and local market conditions when evaluating credit risk.

How often should CECL qualitative factors be reviewed?

Financial institutions should review CECL qualitative factors at least quarterly as part of the allowance estimation process. More frequent Q factor reviews may be necessary when significant economic events, portfolio shifts, or regulatory developments create new risks that could affect expected credit losses.

This article covers these key topics: 

A new framework for interest rate risk

For years, interest rate risk was treated as a directional problem. Rates moved up or down, and balance sheets responded in predictable ways, shaping how many institutions built their models, set their policies, and evaluated performance.

That framework no longer holds. Since 2022, the rate environment has been defined by volatility rather than direction. Tightening cycles, pauses, inversion, and shifting expectations have all played a role. What matters now is not just where rates land, but how they move along the way and how members respond to that movement.

Deposit behavior is driving funding outcomes

Many institutions still report results that fall within policy limits under standard assumptions. The issue is that those assumptions often rely on static balance sheets and simplified behavioral inputs. When you introduce more realistic conditions, exposure tends to widen. That gap between reported comfort and actual sensitivity is where directors need to focus.

The deposit base most institutions relied on a few years ago has changed. During 2020 and 2021, excess liquidity pushed balances into noninterest-bearing and savings accounts. Those funds were treated as stable and low-cost, supported by historical studies showing long-tenured relationships and consistent behavior.

As rates moved higher, those balances moved. Funds shifted into time deposits offering materially higher yields. In many cases, institutions held pricing steady on savings and checking products, so the increase in funding costs came from mix changes rather than rate adjustments.

Pricing assumptions alone don’t explain what happened. If reporting focuses only on beta and lag, it misses the effect of reallocation. At the board level, the question is straightforward: do you know what is driving your funding costs today? If migration is doing most of the work, then traditional beta measures are only telling part of the story.

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Beta assumptions can hide structural risk

Beta analysis has its place. It shows how pricing moves relative to the market. What it doesn’t show is how balances move.

Averages can give a sense of control while masking concentration risk. A blended beta may look reasonable even when a small group of rate-sensitive customers is driving most of the movement. When pricing changes and balances shift at the same time, the resulting cost impact can exceed what the model suggests.

That becomes more pronounced in concentrated portfolios. If a relatively small segment of customers holds a large percentage of balances, their behavior will outweigh any average assumption. Understanding that structure is more important than refining the average.

Shock size changes the outcome

Interest rate sensitivity does not scale in a straight line. Under smaller shocks, exposure often appears manageable. Increase the magnitude of the scenario, and different risk factors begin to show up. Migration accelerates. Betas behave differently. Optionality becomes more relevant.

This is where many institutions are surprised. The model produced acceptable results under a narrow range, and those results carried into planning decisions. When conditions moved outside that range, the underlying assumptions no longer held. Running larger and more varied scenarios is not about adding complexity. It is about understanding how the balance sheet behaves under less stable conditions.

Demographics introduce timing risk

Deposit stability is often evaluated in aggregate. That approach misses an important variable: who holds the funds.

In many institutions, a relatively small percentage of depositors holds a large share of balances, and those balances are concentrated in older demographics. In one example, depositors over age 60 represent the majority of non-maturity balances while accounting for less than half of accounts. Deposit stability is often evaluated in aggregate. That approach misses an important variable: who holds the funds.

That introduces a different type of risk. Historical decay studies look backward. Demographic trends point forward.

Estate transitions, advisor-driven reallocations, and liquidity events can change balances quickly. Those events do not follow the patterns captured in traditional decay assumptions. Directors should expect to see that segmentation reflected in reporting. If concentration and age are not part of the analysis, a key driver of funding stability is missing.

Stress testing needs to connect to decisions

Most credit unions have no problem running stress tests and sensitivity tests. They can adjust assumptions, generate scenarios, and show exposure under different conditions. But how are they using the results? If a scenario shows faster funding cost acceleration, does anything change in the deposit strategy? If a runoff scenario indicates pressure on liquidity, does that influence funding plans or asset duration? If the answers are unclear, the testing process is disconnected from decision-making.

Assumptions do not move independently. Deposit pricing, migration, and customer behavior tend to shift together. When those factors are combined, the impact on earnings and capital is often greater than what isolated adjustments suggest.

From a governance perspective, the focus should be on how sensitive the current strategy is to those combined effects and what conditions would lead to a different course of action.

Moving from compliance to strategy

Supervisory guidance continues to emphasize assumption sensitivity and model transparency. Most institutions can demonstrate that they meet those expectations. The distinction is in the way that work is used. Some institutions treat sensitivity analysis as part of the reporting process. Others use it to inform planning decisions, and this can quickly impact how they respond to changing conditions.

When assumption changes are incorporated into strategy, ALM becomes part of how the institution manages growth, pricing, and funding. It helps define limits that reflect actual behavior and identifies conditions that warrant adjustment. Without that connection, the process confirms what is already in place.

What directors should focus on

The current environment places more weight on behavior than on rates alone. Directors should be asking:

  • What portion of funding cost changes is driven by pricing versus migration?
  • Where are deposits concentrated, and how is that reflected in runoff scenarios?
  • How do results change under larger or more volatile rate paths?
  • What specific outcomes would lead management to adjust strategy?

Clear answers to those questions provide a better view of exposure than policy compliance alone. Credit unions that incorporate behavioral dynamics into planning will have a clearer understanding of their position, whereas those that rely on static assumptions will be reacting to changes after they occur. The difference will become apparent in margins, liquidity, and flexibility when conditions shift again.

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Bank deals impacted by lower rates & regulatory tailwinds 

How have shifting rates and policy changes affected fair value and reshaped financial institution deals?

Key topics covered in this post: 

The changing environment affects deal pricing and fair value outcomes

Over the past two years, the M&A landscape for financial institutions has undergone a meaningful transition. A previously constrained environment was defined by rapidly rising interest rates, widening valuation discounts, muted deal activity, and a lengthy regulatory process. That environment has now become more constructive and more active, creating a wider window for transactions and changing the assumptions that drive fair value. 

As rates, funding costs, approval timelines, and purchase accounting have shifted, fair value outcomes have become less punitive in some areas and easier to model with greater confidence.

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Factors affecting fair value marks

Rising and elevated market rates drove significant pressure on loan fair value marks while also increasing the value of core deposits in recent years. But several more recent key developments altered that dynamic and accelerated the announcement of merger transactions in 2025:

  • The Federal Reserve has shifted policy direction, implementing rate cuts following peak tightening
  • Market expectations for future rate paths have stabilized
  • Bank loan portfolios have repriced upward
  • Bank equity valuations have improved
  • Regulatory processes have become more efficient
  • Deal flow has begun to reaccelerate, with a broader mix of transaction structures

Although deal activity has slowed into 2026, this updated environment is changing not only how deals are priced, but also how fair value is measured and interpreted. Transaction-specific analysis remains critical for financial institutions, even in a more constructive environment, because portfolio composition, borrower concentration, collateral trends, and embedded risks can still materially affect deal economics.

A reopening M&A window

During the rising rate cycle, deal activity slowed considerably due to:

  • Depressed bank stock valuations
  • Increased uncertainty around credit quality
  • Larger fair value discounts, particularly on fixed-rate loan portfolios
  • Tangible book value dilution concerns

Today, many of those handicaps have partially reversed. Lower rates, improved market confidence, and the regulatory backdrop have:

  • Reduced the severity of loan discounts
  • Improved buyer currency (stock)
  • Increased alignment between buyer and seller expectations
  • Reduced execution risk through shorter approval timelines

As a result, we are seeing more traditional acquisitions returning, including the re-emergence of large transactions. We are also seeing increased competition for attractive franchises, particularly those with strong deposit bases. Shorter deal timelines further reduce the risk that fair value estimates will change materially between announcement and closing.

Loan portfolio valuations: From peak discounts to normalization

At the peak of the rate cycle, loan portfolios experienced significant fair value discounts driven by:

  • Market discount rates far exceeding portfolio yields
  • Limited ability for legacy loans to reprice
  • Elevated uncertainty around borrower performance

The Federal funds rate reached a peak in mid-2023 and held flat for much of 2024. Beginning in September 2024, the Federal Reserve reduced rates, bringing the year-end 2024 federal funds rate to 4.33%. Rates then stabilized through most of 2025 until the Federal Reserve resumed easing in September 2025, cutting rates by another 75 basis points over three moves by year-end. As the yield curve began to normalize, loan portfolio yield discounts also compressed meaningfully.

While market rates remained elevated relative to 2020 and 2021 levels, cumulative Fed easing since the third quarter of 2024, combined with continued repricing of loan portfolios toward current market levels, reduced the severity of yield-driven discounts reflected in the accounting for loans.

With rate stabilization and selective rate cuts:

  • The spread between market rates and portfolio yields has generally narrowed
  • New loan production and variable rate loan repricings have lifted portfolio yields overall
  • Discount severity has moderated meaningfully

Financial institutions with older fixed-rate loans with longer maturities, however, remain more challenged from a fair value perspective.

Credit marks: Stability, with continued need for diligence

Credit marks remained relatively stable through much of the rate cycle, as macroeconomic conditions held up better than expected. That stability, however, should not be read as a reason for less rigorous diligence.

Aggregate credit marks may appear steady, but portfolio composition, borrower concentration, collateral trends, and pockets of embedded risk can still materially affect deal economics.

Core deposit intangible values: Peak value from higher rates has passed

In a rising rate environment, core deposits became more valuable as the spread between low-cost deposits and alternative funding sources widened. That dynamic increased the value of core deposit intangibles and made strong deposit franchises more attractive in M&A transactions.

As rates have declined, the economic advantage of core deposits has compressed modestly because the cost of alternative funding sources has also decreased. The spread between the all-in cost of core deposits and wholesale funds has narrowed, reducing some of the premium that higher-rate conditions created.

Core deposits remain an important source of franchise value, but the tradeoff in valuation is worth noting. Higher CDI values reduce goodwill, which does not amortize, while also increasing future noninterest expense through CDI amortization. As CDI values moderate, that future amortization burden becomes somewhat less of a concern for buyers evaluating transaction economics.

CECL and purchase accounting: Complexity has been simplified

The interaction between fair value marks and CECL remains a central issue in deal modeling. In November of 2025, the FASB adopted ASU 2025-08 (Topic 326) for the accounting of purchased credit deteriorated (PCD) loans and non-PCD loans (now referred to as purchased seasoned loans or PSL). The change eliminates the prior “double count” impact and simplifies how institutions model the effect of acquired loan marks.

Under the prior standard, an institution determined the fair value of the acquired loan portfolio and recorded the discount on Day 1, which increases goodwill. After closing, the institution then immediately recorded an allowance on non-PCD loans through provision expense. In effect, a portion of the credit mark was counted twice, once in fair value and again through the allowance.

Under the new standard, PSL loans receive the same accounting treatment as PCD loans, eliminating the double count. The change improves Day 1 capital because an immediate provision expense no longer reduces retained earnings. Instead, a portion of the mark is reallocated to the allowance at closing.

That benefit comes with an important tradeoff. Because a portion of the discount related to non-PCD (PSL) loans is now allocated to the allowance rather than accreted into income over time, income accretion will be lower than under the prior model. For acquirers, the result is a cleaner, more transparent framework for evaluating capital, goodwill, and post-close earnings impact.

Faster approvals: Implications for fair value

One of the more meaningful shifts in today’s environment is the acceleration of regulatory approvals.

Historically, transactions often took four to six months (and longer for more complex deals) from announcement to closing. As such, there was market risk that fair values could change materially before close, particularly in volatile rate environments.

The risk was especially evident in 2023, when rising rates widened loan fair value discounts rapidly while portfolio yields adjusted more slowly. In some cases, the estimated loan discount at due diligence differed significantly from the amount ultimately booked at closing, resulting in higher goodwill than initially anticipated, all else equal.

Shorter windows from announcement to closing reduce some of this risk in today’s market. For buyers and sellers, that means greater confidence that the fair value assumptions used to evaluate a transaction will remain relevant through closing. It also improves the reliability of early-state deal modeling and reduces the likelihood that transaction economics will shift materially late in the process.

A changing M&A market  requires discipline

The current M&A environment represents a transition from constraint to opportunity.

Many of the pressures from the rising rate cycle have eased. Loan discount severity has moderated, bank valuations have improved, and approval timelines have shortened. At the same time, changes in purchase accounting have simplified one of the more complex elements of deal modeling.

Even so, fair value remains highly sensitive to assumptions for both the interest rate component and the credit component. Core deposit intangible values have decreased and stabilized, reducing concern for buyers’ future amortization expense levels, but they remain an important part of franchise value in many transactions.

The result is a more active deal environment than a few years ago, but not a simpler one. Evaluating transaction economics in financial institution acquisitions or mergers requires disciplined valuation frameworks, forward-looking macro assumptions, and granular portfolio analytics. Early-stage due diligence fair value analysis also remains essential to gauge deal economics. Finally, the interaction between valuation and CECL continues to require careful consideration under the new FASB treatment for PCD and PSL loans.

FAQ

What is fair value in bank M&A?

Fair value in bank M&A is the estimated market-based value of acquired assets and liabilities at the transaction date. For banks, fair value analysis often focuses on acquired loan portfolios, core deposit intangibles, credit marks, interest rate marks, goodwill, and post-close earnings impact. Abrigo supports financial institutions with CECL, purchase accounting, and portfolio risk tools that help make bank M&A fair value analysis more structured and defensible.

How have interest rates changed fair value outcomes in bank acquisitions?

Interest rates have changed fair value outcomes by reducing some of the severe loan discounts seen during the rising-rate cycle. As market rates stabilized and portfolios repriced closer to current yields, loan fair value discounts became less punitive, although older fixed-rate loans with longer maturities may still face valuation pressure. Abrigo helps banks evaluate these assumptions through fair value, CECL, and portfolio risk analysis.

Why do credit marks still matter in bank M&A?

Credit marks still matter in bank M&A because aggregate credit conditions can look stable while specific borrower concentrations, collateral trends, or embedded risks affect deal economics. A disciplined fair value process reviews both interest rate marks and credit marks before closing. Abrigo’s credit risk and purchase accounting solutions help banks evaluate acquired loan portfolios with greater consistency.

How do core deposit intangibles affect bank acquisition pricing?

Core deposit intangibles affect bank acquisition pricing by assigning value to stable, low-cost deposit relationships acquired in a transaction. As rates have declined, the advantage of core deposits over alternative funding has narrowed, which can reduce CDI values and future amortization expense. Abrigo helps banks analyze deposit franchise value as part of broader bank M&A fair value modeling.

How did CECL changes simplify purchase accounting for bank M&A?

CECL changes simplified purchase accounting by reducing the prior “double count” effect for acquired loans. Under FASB ASU 2025-08, purchased seasoned loans receive accounting treatment similar to purchased credit deteriorated loans, which improves Day 1 capital treatment but may reduce future income accretion. Abrigo supports CECL software for banks and purchase accounting workflows that help finance teams model these tradeoffs.

Abrigo has deep valuation and bank purchase accounting expertise. We provide accurate and timely fair value and income recognition services for financial institutions.

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