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Marijuana safe banking in 2025: Will rescheduling bring relief for financial institutions?

Financial institutions have monitored the progress of federal marijuana legislation for years, yet meaningful change has remained out of reach. While speculation around marijuana legislation continues to grow, financial institutions remain in the challenging position of navigating the gap between expanding state-level legalization and ongoing federal prohibition. This regulatory gray area presents heightened AML/CFT, reputational, and operational risk. With a decision on federal rescheduling expected soon, financial institutions are asking: Will it bring marijuana safe banking?

Rescheduling marijuana: Progress, but not a green light

In May 2024, after a review by the U.S. Department of Health and Human Services, the Drug Enforcement Agency issued a proposed rule that would reclassify marijuana from a Schedule I narcotic to Schedule III under the Controlled Substances Act (CSA). This historic shift would recognize its medical use and remove its classification alongside drugs like heroin and LSD.

In August 2025, the Trump administration confirmed reviewing the rescheduling proposal, with a final decision expected soon. President Trump acknowledged conflicting perspectives, stating, “I’ve heard great things having to do with medical [use]... and bad things having to do with just about everything else.”

But even if marijuana is rescheduled, it will remain federally regulated. For financial institutions, this means the 2014 FinCEN guidance remains in effect. Rescheduling may reduce stigma, but it does not equate to legalization and doesn’t resolve the core banking challenges.

 

What this means for financial institutions and their clients

The conflict between state legalization and federal prohibition puts banks and credit unions in a difficult position. Many serve communities with growing marijuana-related businesses (MRBs) that need access to basic financial services. Yet without federal protection, offering those services remains risky and complex.

The compliance burden is significant for institutions choosing to bank MRBs or exposed indirectly through ancillary clients, like landlords, vendors, or service providers. Enhanced due diligence and robust ongoing monitoring remain critical components of a sound marijuana safe banking program.

Clients, meanwhile, face limited access to financial services. The result is often increased reliance on cash, which raises fraud, theft, and money laundering risks. To stay compliant and prepared, institutions must take a risk-based approach that addresses marijuana exposure through policies, staffing, and controls tailored to current and emerging threats.

 

Has the SAFE Banking Act passed?

Amid these challenges, the Secure and Fair Enforcement (SAFE) Banking Act continues to generate attention and bipartisan support but remains stalled, primarily due to a packed Congressional schedule. Intended to give banks and credit unions safe harbor when serving state-legal MRBs, the Act has passed the U.S. House seven times but has never cleared the Senate.

With its most recent iteration, the SAFER Banking Act (S.2860) remains pending in the Senate. In July 2025, a majority of state attorneys general sent a letter to Congressional leaders in support of passing federal protections for banks that do business with marijuana companies. “We write today in support of the SAFER Banking Act of 2025,” the letter read. “It is increasingly critical to move cannabis commerce into the regulated banking system.”

What the Act would mean for financial institutions

The SAFE Banking Act does not legalize marijuana or remove it from Schedule I. However, it would change the operational risk landscape by protecting financial institutions that serve compliant MRBs from federal penalties, asset forfeiture, or loss of deposit insurance.

The Act would also support AML/CFT efforts by reducing cash-only business models and enabling better transaction monitoring. Senator Jeff Merkley (D-Oregon) described the issue clearly in his Senate Committee testimony: “There is nothing like a cash economy to facilitate money laundering.”

Cash-heavy operations are more vulnerable to violent crime and harder for law enforcement to monitor. Without auditable financial records, marijuana-related activity remains in the shadows. Allowing electronic transactions would enable institutions to detect suspicious patterns better, file more accurate SARs, and bring marijuana-related funds into the oversight of the financial system.

Marijuana safe banking today

Despite legalization in most states, the vast majority of MRBs still lack access to traditional financial services. FinCEN SAR data shows that only about 830 U.S. banks and credit unions currently serve this market.

This forces many MRBs to operate in cash, limiting their ability to secure loans, build credit, or expand. For financial institutions, even those not intentionally serving the marijuana industry, this gap increases the risk of unknowingly onboarding or servicing indirectly connected customers.

Supporters of the SAFE Banking Act emphasize that the issue isn’t just about access but also about public safety. The Act would not only help institutions manage risk but also enhance community safety by integrating more marijuana funds into transparent, monitored systems. It would also protect the ecosystem of businesses that support MRBs, like landlords, law firms, and payroll providers.

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A regulatory turning point

The Act remains a pivotal opportunity to bring federal alignment to a rapidly growing state-legal market. While past efforts have failed, increasing public support and the burden placed on financial institutions may eventually force action.

Still, banks and credit unions cannot afford to wait. Whether an institution chooses to serve MRBs or not, updating AML/CFT programs to reflect marijuana-related risks is a regulatory expectation—not a future suggestion. That includes documenting board-approved positions on MRBs in risk appetite statements.

Marijuana exposure

Even institutions not actively banking marijuana businesses may have exposure through third-party relationships. Property managers, security firms, consultants, and others may be closely tied to MRBs—making strong customer due diligence (CDD) and ongoing monitoring critical.

Recommended next steps:

  • Perform a staffing assessment to ensure your teams can meet marijuana-related compliance demands.
  • Update CDD and enhanced due diligence (EDD) processes to identify high-risk accounts.
  • Revisit your BSA/AML risk assessment to include scenarios related to rescheduling, legalization, and continued regulatory ambiguity.

Prepare for what’s next

Despite growing support, marijuana legislation remains uncertain. Financial institutions can’t afford to wait for clarity. Instead, they must take a proactive, risk-based approach to marijuana-related compliance today.

Whether your institution plans to bank MRBs, avoid them entirely, or prepare for future opportunities, strong internal controls, updated policies, and a mature AML/CFT program are essential. Regulators expect institutions to identify and mitigate marijuana-related risk—regardless of what happens on Capitol Hill.

The marijuana industry isn’t waiting for Congress to act, and neither should your institution. By planning and documenting your approach, you can stay compliant, protect your reputation, and remain ready for whatever comes next.

 

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Discounted cash flow or WARM for the allowance? 

Two commonly deployed approaches for the allowance for credit losses under CECL are the discounted cash flow model and the remaining life methodology, also called WARM. How do you know when to select which?

This article covers these key topics: 

CECL's flexibility is both a strength and a responsibility

Since its adoption in ASU 2016-13, the current expected credit loss (CECL) model has introduced a forward-looking approach to estimating credit losses. The guidance intentionally avoids prescriptive formulas, empowering financial institutions to choose from a variety of acceptable methodologies for calculating the allowance for credit losses:

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However, with this flexibility comes the responsibility for financial institutions to align their chosen methodology with portfolio characteristics, data capabilities, and risk management practices.

Two of the most commonly deployed approaches to CECL are the discounted cash flow model and the remaining life methodology. This guide unpacks the strengths and challenges of both. It offers a practical decision framework to help community financial institutions make defensible, scalable choices as they select, or transition to, an approach that aligns with their unique situation and supports defensibility and operational efficiency.

 

What is the discounted cash flow method?

The DCF method estimates credit losses by projecting future contractual cash flows, applying assumptions for prepayments, defaults, and recoveries, and discounting those expected loan-level cash flows back to present value using the effective interest rate (EIR) as defined by the FASB.

 As ASC 326-20-30-4 says, “The allowance for credit losses shall reflect the difference between the amortized cost basis and the present value of expected cash flows.” 

How it works

The discounted cash flow methodology, in essence, uses contractual schedules adjusted for prepayments to estimate future balances by month. This is extraordinarily helpful when adhering to ASC 326-20-30-6, which instructs institutions to model “expected credit losses over the contractual term of the financial asset(s).”

ASC 326-20-30-6 also says “ An entity shall consider prepayments as a separate input in the method or prepayments may be embedded in the credit loss information.” Speaking from experience, it’s neither an easy nor fun task to defend changing expected lives due to changes in prepayment speeds in varying rate environments when prepayments are “embedded in the credit loss.”

This approach, whether discounted or undiscounted, offers the opportunity to eliminate a life assumption, which is the most difficult and material assumption to support in a remaining life model (described below).

In fact, in order to support the remaining life input, one must run cash flows adjusted for prepayments, which begs the question – why not just stop there? As an added opportunity, forward-looking amortization schedules, interest income, and periodic expected loss all provide a strong foundation from which to manage. After all, it is the language of banking.

Armed with this kind of output, estimating future balances is accurate, which can allow for production budgeting. Loan-level detail on interest income that considers the default probability is also helpful in its own right. Lastly, timing-specific loss estimates make backtesting, monitoring, and scenario analysis feasible.

When to use DCF for CECL

Financial institutions have a number of considerations when selecting a CECL methodology. If using the discounted cash flow model is a possibility, remember that the methodology is best suited for specific situations.

These include the following:

  • While the discounted cash flow method works for nearly all loan types, it’s best for loan portfolios with contractual obligations extending beyond a year.
  • When an institution wants to quantify the impact of an economic forecast
  • When an institution wants to quantify the impact of a prepayment speed input
  • When an institution has loan-level fair market value adjustments resulting from an acquisition or whole loan purchase
  • When an institution prefers loan-level modeling and/or loan-level auditability
  • When industry or peer data is necessary or helpful

Pros

Some advantages of calculating the allowance using the discounted cash flow methodology include that it:

  • Is highly flexible, granular, and precise
  • Accurately reflects timing of losses, recoveries, and prepayments
  • Natively integrates reasonable and supportable forecasts
  • Supports layering of external or peer-derived inputs where internal data is sparse

Cons

Banks and credit unions have found that some of the challenges tied to using DCF are that it:

  • Requires detailed loan-level data (e.g., cash flow schedules, EIR, risk ratings)
  • Involves a heavier computing power burden

What is the remaining life method?

The remaining life method estimates losses using historical annualized loss rates and then applies those losses to balances using some form of life-of-loan assumption. Adjustments for prepayment speed changes, current conditions, and reasonable and supportable forecasts are usually estimated and applied through qualitative overlays or adjusting a life-of-loan input.

When to use it

Some of the reasons a financial institution might select WARM for the allowance for credit losses include:

  • When an institution is seeking an expedient
  • Comfortable with qualitative factors for forecasting and prepayment changes
  • Limited access to loan-level data or modeling capacity
  • As a transitional methodology

Pros

The remaining life method has the following qualities that might cause a bank or credit union to choose this methodology:

  • Easy to implement and maintain
  • No need for extensive historical or loan-level data
  • Easily understandable and auditable
  • Widely used and regulator-accepted for community institutions

Cons

Some of the feedback we’ve gotten about why financial institutions might not want to select WARM includes:

  • Difficult to support in changing rate environments (prepayment speed changes)
  • Loss timing is not explicitly modeled
  • Limited flexibility for dynamic economic forecasts
  • Assumes flat distribution of risk across remaining life
  • May misstate risk for longer-duration or prepayment-sensitive assets

Choosing the right method: A practical decision framework

Making a choice of CECL methodology involves many factors. Below is a simplified framework to help guide your selection.

chart of DCF vs. WARM methods for CECL

Both methods are fully compliant with CECL, but DCF offers better alignment with forward-looking credit risk modeling. The remaining life method offers an alternative for institutions prioritizing ease of implementation over support and defensibility.

Documentation and validation best practices

Regardless of the method used, institutions should:

  • Document method selection rationale, tying it to portfolio characteristics
  • Clearly define all inputs, assumptions, and external data sources
  • Perform annual CECL model validations or revalidations whenever portfolios materially change
  • Monitor and backtest model performance
  • Retain version control for model updates and assumption changes
  • Align CECL methodology with internal ALM, stress testing, and strategic planning frameworks where possible

Aligning methodology with institutional maturity

There is no universally “right” method for CECL compliance—only the method best aligned with your institution’s size, systems, staffing, and risk complexity.

  • Start with WARM if you're prioritizing ease of implementation and are comfortable re-implementing later as you grow or experience changes in rates or economy.
  • Evolve toward DCF as your institution builds stronger data pipelines, economic forecasting capabilities, and strategic modeling needs.
  • DCF provides not only more refined allowance estimates but also enhanced insight into credit risk behavior. These can enable better pricing, strategy, and capital planning.

Abrigo has guided hundreds of financial institutions through CECL implementation, tailoring the process to their unique goals and operational realities. Whether adopting a simplified model like remaining life, or ready to unlock the full potential of loan-level DCF modeling, Abrigo's allowance solutions and CECL advisors can help you navigate every step—from methodology selection and model validation to reporting and examiner readiness.

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This article covers these key topics: 

Understanding the WARM method

Learn how the WARM method supports CECL compliance with step-by-step guidance, example calculations, and comparisons to other methodologies like SCALE and discounted cash flow models.

The WARM method, short for Weighted Average Remaining Maturity, is one approach to estimating credit losses under the current expected credit loss accounting standard (CECL).

The allowance for credit losses (ACL) is one of the most significant estimates in an institution’s financial statements and regulatory reports. Understanding the basics of WARM, how financial institutions implement it, and how the methodology compares to alternative allowance methodologies under CECL can guide more informed discussions.

Unlike models that calculate the allowance under CECL using loan-level data encompassing the entire life of the loan, the WARM methodology estimates lifetime credit losses without requiring such granular information. Instead, it leverages an institution’s top-down historical annual charge-off rates along with information about the remaining life of a loan pool. This is why WARM is also known as the remaining life method.

Since limited data has been one of the biggest challenges for institutions transitioning to CECL, WARM has been an attractive option to some financial institutions, especially after regulators recognized it as an acceptable CECL methodology.

Other reasons financial institutions consider using the WARM method include that it is forward-looking (similar to the discounted cash-flow method) and offers a transparent and repeatable process.

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How the WARM method estimates credit losses

Under CECL, an allowance must cover losses expected over the entire life of the loan. Previously, losses weren’t recognized until they were both probable and estimable. In other words, credit losses under CECL are calculated by assuming that all loans are originated with some measurable risk of default.

Estimating lifetime losses is difficult without the historical, life-of-loan or life-of-cycle data detailed at the loan level.  Data gaps may stem from operational issues (such as a small portfolio acquisition or a system conversion) or numerical limitations (such as a new line of business or a de novo institution without sufficient history).

The WARM calculation helps address the data gap by combining three key inputs:

  1. Average annual charge-off rate, typically derived from historical loan-level or portfolio-level losses, institution call report data or peer group data.
  2. Amortization-adjusted remaining life, reflecting expected paydowns or prepayments and curtailments. Remaining life is derived by calculating the average life of the segment using an attrition model or other methods.
  3. Qualitative adjustments, which account for factors not captured in the historical data that are quantitative in nature.

As FASB has explained, “The average annual charge-off rate is applied to the contractual term, further adjusted for estimated prepayments to determine the unadjusted historical charge-off rate for the remaining balance of the financial assets.” To comply with CECL’s requirement for adjustments to historical loss information for current conditions and a “reasonable and supportable forecast period,” the qualitative components are layered in.  

For example, in a widely cited interagency illustration, a $13.98 million loan pool with a 0.36% charge-off rate and a 2.52-year remaining life yields an unadjusted lifetime loss rate of 0.90%. Adding a 0.25% qualitative adjustment raises the final allowance for credit losses (ACL) to 1.15%, or $161,000.

The math for WARM is relatively simple, but examiners have cautioned that supporting these inputs and layering on qualitative factors to account for forward-looking information is important to establish CECL model credibility.

How to implement the WARM method

Step-by-step implementation process

Institutions implementing WARM typically follow this process:

  • Segment the loan portfolio by similar risk characteristics, such as type, term, and credit quality.
  • Determine remaining life using contractual terms adjusted for expected prepayments. One approach is to use a quarterly attrition analysis that tracks exit events and develops an annual percentage of how often loans exit the portfolio via payoffs, renewals, maturity, or charge-offs. Call Report data (yours or peer data) may also be used for this.
  • Calculate the historical loss rates at the pool level using available charge-off data, Call Report data, or peer data.
  • Apply forecast model to capture the forward-looking impact on the reserve. This can be done through an expedient method or through application or regression models.
  • Apply the WARM formula to generate the lifetime historical charge-off rate for the remaining balance of the financial assets.
  • Incorporate qualitative overlays to adjust for reasonable and supportable forecasts.

Banks are expected to reflect forward-looking risks, not just replicate historical patterns. That’s where adjusting credit loss estimates with so-called qualitative factors comes in.

Adjusting for qualitative factors (Q factors)

What are Q factors, and why do they matter?

Qualitative factors, or Q factors, are adjustments to credit loss estimates that help account for risks that quantitative models and historical loss data can’t fully capture. Q factor adjustments to the allowance developed with the remaining life method will ensure reserve levels reflect what’s expected in the future, based on reasonable and supportable forecasts, rather than capturing past performance alone.

Under CECL, these adjustments are especially important because institutions must incorporate a reasonable and supportable forecast period. In this context, the forecast period is a near-term horizon over which management can credibly link economic forecasts (such as unemployment, interest rates, or local market conditions) to expected credit performance.

However, CECL also requires institutions to address what happens after that forecast horizon. Since forward-looking forecasts cannot be projected indefinitely with reliability, the guidance requires a reversion period. The reversion period is the point at which loss rates transition back to long-term historical averages.

In practice, this means:

  • Reasonable and supportable forecast period: The near-term horizon (often 1–3 years) where economic forecasts are applied directly to adjust expected losses.
  • Reversion period: The time after which forecasts become unreliable, during which modeled loss rates revert to long-term historical loss experience, either immediately or gradually (e.g., linear reversion).

For WARM specifically, the average annual charge-off rate is extended across the pool’s remaining life, but it must be adjusted first, for near-term forecasts (via Q factors) and second, for how losses revert beyond that period.

For example, an institution may adjust loss rates upward for the first two years due to a projected economic slowdown but then revert to historical charge-off levels for the rest of the pool’s life.

Documenting how management determined both the forecast horizon and the reversion approach is essential. Examiners have emphasized that unsupported overlays or vague explanations of forecast and reversion assumptions are among the most common CECL exam findings.

Documenting qualitative overlays for examiners

According to recent Fed findings, another common CECL-related exam issues was unsupported Q factors. Some institutions either neglected to apply qualitative overlays or failed to document them adequately, despite experiencing known risk shifts, such as entering new markets or launching new loan types.

Even though the adjustments are labeled “qualitative,” they can and often do have a quantitative basis.

Examples of qualitative adjustments in WARM

Examples of well-supported Q factors that could be incorporated into the WARM method include:

  • Local unemployment rate changes
  • Loosened underwriting standards compared to historical periods
  • New or inherently riskier product types, such as commercial real estate lending expansions

One way to determine what economic factors may apply is to talk with internal credit and Treasury teams to identify which economic factors are already being used internally, for example, for stress testing. Regardless of which factors are selected, they need to be tied to measurable indicators, and financial institutions should document their rationale as part of overall model governance. They should also define when the institution will revisit the factors as part of sound model governance.

Model governance and documentation

CECL internal controls

Speaking of model governance, CECL auditors and experts have noted that recent exams and audits have focused on this important aspect of calculating the allowance. Good CECL model governance, regardless of whether the institution uses a more simplistic model like WARM or something else, should include documentation, validation, and back testing. Regulators have cited inadequate documentation as another common issue in CECL exams.

Governance best practices and examiner expectations

To meet examiner expectations regarding other aspects of model governance, institutions using WARM should also:

  • Maintain a clear allowance policy that aligns with CECL
  • Explain why WARM was chosen to calculate the allowance and where it applies
  • Detail the assumptions specific to using WARM, lookback periods, segmentation logic, and forecast policies
  • Review and update assumptions periodically

Sound governance, including documenting assumptions, is essential when using the remaining life methodology or other options for CECL calculations.

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Comparing the WARM method to other CECL approaches

WARM method vs. SCALE

Both WARM and the Federal Reserve’s SCALE tool (Scaled CECL Allowance for Losses Estimator) are intended to help estimate the ACL for less complex entities or those with less complex financial asset pools. As a result, both methods appeal to community banks and credit unions. However, they serve different needs and have some important differences.

  • SCALE leverages peer data, which may ease data requirements for banks with limited internal loss history. The regulators and auditors still expect institutions to adjust for their own experience, which can be challenging and hard to support.
  • WARM can also use peer data, but it allows for greater use of internal data and portfolio-specific risk segmentation. It also offers more customization, which requires documentation.

Other considerations when it comes to comparing SCALE and the remaining life method:

  1. SCALE relies on proxy expected lifetime loss rates of peers, which are already adjusted for reasonable and supportable forecasts. Management should consider whether a qualitative adjustment should be applied to account for local economic conditions expected to perform better or worse than industry economic conditions.
  2. The peer data used for SCALE would typically be based on information from the previous reporting period. Management should consider the timing issue and determine the impact of any changes in business or economic conditions as between when proxy loss rates were calculated and the reporting date.
  3. Regulators have said the SCALE tool is not appropriate for institutions with assets greater than $1 billion that are required to submit data into Schedule RI-C since that would result in an institution both submitting data and using data derived from Schedule RI-C.

WARM vs. discounted cash flow and static pool models

WARM, discounted cash flow (DCF), and static pool methods all comply with CECL, but they vary greatly in data requirements, forecasting precision, and operational complexity.

  • Discounted cash flow models simulate individual loan cash flows using assumptions for prepayments, default timing, recovery rates, and discount rates. They provide high granularity and allow institutions to align economic forecasts with precise time periods, which can be a key advantage when forecasting near-term economic volatility or credit deterioration.
  • Static pool or cohort-based models track over time a defined group of loans originated during a given period, measuring cumulative loss behavior. Static pool methods conceptually align with CECL’s lifetime loss perspective, but they require longitudinal, loan-level data and robust cohort tracking practices.

By comparison, WARM estimates credit losses using historical loss averages applied across the remaining life of the pool. It does not account for the timing of losses and treats the portfolio as a single amortizing asset. As a result, WARM is easier to govern and document but sacrifices modeling precision.

Considerations for use cases of each model type

WARM may be best for institutions with relatively simple, amortizing portfolios and limited access to life-of-loan data, or for less complex portfolios/segments, new lines of business, or immaterial acquired portfolios. It may be appropriate for specific types of loan portfolios, such as segments without losses or segments lacking loan-level data because they are serviced by a third party. A credit card portfolio is an example.  

DCF and static pool models may be better suited for institutions with complex portfolios, short-term credit volatility, or regulatory pressure to align forecasts more tightly with exposure timing.

Trade-offs and limitations of the WARM method

As noted above, the WARM method is designed for clarity and feasibility. But that simplicity comes with trade-offs. Institutions using this approach should recognize its potential limitations and actively mitigate them through documentation and governance.

Challenges with data granularity and outliers

Because WARM is a top-down method relying on aggregated portfolio data, it lacks visibility into loan-level performance patterns. This can mask significant outliers or emerging risks within a pool. For smaller institutions or smaller segments, one or two charge-offs can skew the average charge-off rate, especially when loss histories are shallow or inconsistent.

Institutions should consider the statistical volatility of smaller pools and whether supplemental analysis or exclusion of outlier periods is needed to avoid distortions.

Situations where WARM may introduce oversimplification

WARM assumes straight-line balance reduction across the remaining life of the pool. While this simplifies exposure modeling, it often does not reflect actual amortization behavior. Products with balloon structures, revolving credit, or seasonal paydown patterns may not fit the straight-line assumption well.

Institutions using WARM should document how they determined remaining life and consider qualitative adjustments if actual repayment patterns materially diverge from the model assumption.

Balancing ease of use with accuracy

CECL requires incorporating reasonable and supportable forecasts, and WARM supports this through qualitative overlays and near-term forecast layering. However, because the method applies charge-off rates evenly over the remaining life, WARM lacks the ability to time forecasted risk precisely.

For example, if an institution anticipates elevated losses within the next 12–18 months due to deteriorating market conditions, the WARM method can’t easily front-load those losses. More granular methods like DCF or PD/LGD modeling are better suited to simulate time-based loss expectations.

When WARM may not be the best fit

The WARM method may not be appropriate for all loan types or portfolios. Institutions should reassess their methodology if they have:

  • Newly introduced or unseasoned loan products without reliable loss history
  • Complex structures (e.g., interest-only periods, variable-rate resets, or off-balance sheet exposures)
  • Significant concentration risk in high-volatility asset classes
  • Rapid growth into new markets with limited historical performance data

In these cases, WARM may oversimplify the credit risk profile, potentially leading to under- or over-reserving. If the institution cannot clearly tie the model assumptions to observed portfolio behavior, examiners may challenge the methodology’s adequacy.

Is WARM the right fit for your institution?

The WARM method remains a viable, widely accepted CECL methodology, especially for community institutions seeking a manageable and auditable approach. Still, institutions should ask:

  • Have we documented why WARM is appropriate for our portfolio?
  • Are we using recent, relevant historical loss data?
  • Have we applied and supported Q factors tied to observable risks?
  • Have we backtested the remaining life model to ensure results truly reflect the institution risk profile? Even though WARM is considered one of the more straightforward CECL methodologies, regulators expect every institution regardless of model complexity to validate and backtest their allowance methodology. This is not optional.
  • Do our policies reflect CECL’s expectations, not just our legacy allowance process?

If the answer to any of these is “no,” it may be time to revisit model inputs, documentation, or governance structure.

Practical tips for financial institutions

An automated CECL solution that supports WARM and other methodologies can help institutions remain compliant without unnecessary complexity. Moreover, it allows institutions to fine-tune the CECL process as the institution grows and changes and as best practices evolve.

Abrigo supports institutions using WARM and other methodologies through CECL software, CECL advisory services, and CECL allowance training programs. Abrigo’s allowance software contains an attrition calculator and examiner-ready documentation tools.

Revisiting CECL methodologies

CECL was one of the most significant changes to financial institution accounting in decades. 

For many financial institutions, the remaining life or WARM method offered a practical on-ramp to CECL. Now that community financial institutions have navigated their first year of compliance with CECL, some are revisiting their allowance methodologies. For those using WARM, reviewing model selection and assumptions regularly can demonstrate “ownership” and make for a smoother review by auditors and examiners.

 

This blog was developed with the assistance of ChatGPT, an AI large language model. It was reviewed and extensively revised by Abrigo's subject-matter expert for accuracy and additional insight.

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What is reputational risk: How it fits into business strategy

As banks and credit unions navigate evolving regulatory expectations, reputational risk remains important, especially for community financial institutions that prioritize strong, trusted relationships. While regulatory agencies have clarified they will not examine reputational risk in isolation, many institutions continue to consider it part of a broader, community-centric strategy. Understanding reputational risk and its impact can help financial institutions stay aligned with their mission, protect stakeholder trust, and make informed business decisions.

Regulatory guidance on reputational risk

In a recent shift in examination expectations, the three federal banking regulators—the Federal Reserve Board, the Office of the Comptroller of the Currency, and the Federal Deposit Insurance Corporation—have confirmed that they will no longer consider reputational risk as a stand-alone factor during bank examinations. The National Credit Union Administration is being called upon by America’s Credit Unions to follow suit. The change is part of a broader effort to clarify expectations and reduce the likelihood of institutions feeling pressure to exit entire industries based solely on perceived reputational concerns.

This shift provides more certainty around how regulators approach client risk for banks and credit unions. However, it raises important questions about how financial institutions define and manage reputation within their risk frameworks, primarily when serving diverse or high-profile client groups. While regulators have stepped back, reputational considerations remain, especially regarding community trust and long-term strategy.

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What is reputational risk and why it matters

Reputational risk is the potential for negative public perception to affect a financial institution's credibility, client trust, or financial performance. It often stems from how an institution is viewed by its community, clients, regulators, or media, and it may arise even when no compliance violations or operational failures have occurred.

Today, reputational risk can be influenced by various factors, including the types of clients an institution chooses to serve or decline, partnerships with third-party vendors, lending or investment decisions, or how it handles emerging social or political issues. Even lawful and risk-based decisions may draw public scrutiny if they appear inconsistent with the institution's stated values or community expectations.

These factors make reputational risk more dynamic than ever. For example, exiting a relationship with a high-profile business might be entirely appropriate from a risk management perspective. Still, without clear communication or documentation, the public can misinterpret the decision as unfair or discriminatory. Likewise, continuing to serve a controversial client segment may prompt questions from community members, advocacy groups, or employees.

New regulatory changes

The decision to remove reputational risk as a factor in formal bank examinations was mainly in response to concerns about derisking—when financial institutions end relationships with entire categories of clients or industries based on perceived reputational harm rather than individual risk assessments. In previous years, some banks chose to exit entire sectors, such as money services businesses, cannabis-related businesses, or international remittance providers, not because of direct regulatory violations, but because of uncertainty around supervisory expectations. This derisking trend created unintended consequences. Legitimate businesses, especially those serving underserved or international communities, were sometimes left without access to essential financial services. In response, regulatory agencies clarified that while banks can make risk-based decisions, supervisors would no longer use reputational risk alone as a basis for exam criticism. The goal is to promote financial inclusion while ensuring client decisions are grounded in individual risk, not generalized assumptions.

 

Reputational risk is connected to other areas of risk

Reputational risk rarely stands alone. In fact, it often overlaps with other key risk categories, which means it can surface in situations where it may not be immediately obvious. Understanding these connections helps financial institutions see the full picture before making decisions that could have lasting effects.

  • Strategic risk can rise when an institution chooses to enter or exit industries that are considered sensitive or controversial. Expanding into a new market segment may create growth opportunities, but it can also bring heightened public attention. Likewise, stepping away from a line of business for sound risk reasons may be interpreted by some as a value statement.
  • Compliance risk can occur when actions do not align with written policies or when those policies are applied inconsistently. Even if a decision is legal and defensible, failing to follow established processes can create reputational questions about fairness, transparency, or governance.
  • Credit risk can be complicated when the financial strength of a borrower becomes secondary to how the relationship is perceived. A strong-performing borrower in a controversial industry may pose minimal credit risk, but the reputational implications could still influence the institution’s decision-making.

For example, an institution might decide to end a relationship with a client whose lawful business activity does not align with the bank’s mission or community priorities. While the decision could be supported by credit analysis, operational considerations, or policy requirements, it may also reflect reputational concerns.

The key is to evaluate the full risk picture and anticipate how a decision will be perceived by customers, staff, and community stakeholders. By doing so, banks and credit unions can minimize surprises, ensure decisions are consistent with their stated values, and preserve the trust that often takes years to build.

 

Reputation as part of governance conversations

Even though examiners will no longer evaluate reputation as a stand-alone risk, institutions benefit from including it in board and leadership discussions. In fact, the regulatory agencies expect banks and credit unions to engage in sound risk management practices, operate in a safe and sound manner. For many community financial institutions, reputation remains an important aspect of risk management. Common governance examples of include:

  • Reviewing client onboarding and exit policies for reputational considerations: Customer acceptance and closure policies should account for reputational implications and be applied consistently. Clear criteria and documentation help staff explain decisions to customers, community members, or regulators.
  • Evaluating new products or services through a community impact lens: Before launching new offerings, consider how they will be received by the community and whether they align with the institution’s mission. This helps prevent missteps that could harm trust or brand perception.
  • Discussing high-profile vendor relationships that may attract stakeholder attention: Vendors with public visibility can influence how the institution is perceived. Leadership discussions should weigh the benefits against potential reputational costs and ensure there is a plan to address questions if they arise.

When reputation is discussed at the board or committee level, it helps ensure that the institution's decisions align with its stated values and risk appetite.

 

A simple framework for assessing reputational exposure

Adding structure to reputational risk management does not require new systems. Institutions can adapt existing tools to include questions such as:

  • Does the client or activity align with our institution's mission and values?
  • Could this relationship or decision attract public or media attention?
  • Have we documented how and why we reached a decision?
  • Would this decision be viewed as fair and consistent by clients and regulators?

These questions help bring consistency to decisions that often involve subjective judgment. They also help front-line and risk staff feel confident in their approach when handling complex or sensitive situations.

 

Prioritizing reputation

For many banks and credit unions, reputational risk is not just about regulatory expectations but about living out the values they promote within their communities. These institutions are not managing their reputation to meet a rule. They are doing it because they believe it is the right thing to do.

Local financial institutions often have deep relationships with their clients. Their reputations are built over years, sometimes decades. Once trust is lost, it is not easily regained. Institutions that include reputational considerations in their risk and business decisions are often better positioned to preserve that trust during times of change.

Reputational risk may no longer be central to regulatory examinations, but remains a valuable part of business strategy. Financial institutions that proactively consider reputation in their governance, client policies, and community engagement are taking steps to ensure long-term strength and stakeholder trust.

 

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Elder sextortion: The emotional and financial toll

Financial exploitation of older adults continues to evolve as fraudsters shift their tactics to digital platforms. According to the FBI's Internet Crime Complaint Center (IC3) 2024 data, more than 147,000 Americans aged 60 and older reported falling victim to online fraud, with total losses approaching $4.9 billion. This represents a 43 percent increase in reported losses compared to 2023.

Among these crimes, elder sextortion stands out as one of the most emotionally and financially damaging. Elder sextortion is a form of blackmail that combines romance scams, impersonation, and manipulation to target older adults online. A 2024 study published in Computers in Human Behavior estimated that one in seven adults globally has experienced someone threatening to share intimate images.

The FBI reports that extortion, including sextortion, among older adults increased by 134 percent year over year. These are not isolated incidents. They are part of a growing trend in digital crime affecting some of the most vulnerable members of our communities.

For community banks and credit unions, responding to this threat is not simply a matter of fraud detection. It is about protecting customers, preserving trust, and reinforcing the financial institution's role as a safe, trusted partner.

 

What is elder sextortion

Elder sextortion occurs when scammers deceive older adults into sharing explicit photos, videos, or engaging in private online interactions. The fraudster then uses that content, or the threat of having it, to extort money. In many cases, the scammer never actually possesses any images. Instead, they rely on fear, shame, and the victim's concern for their family, social circle, or religious community to force compliance.

These cons often begin as online romance scams. Fraudsters are patient and calculated, gradually building emotional trust before introducing explicit content or requests. The scammer starts making demands once the relationship feels real to the victim.

While financial loss is significant, the emotional toll can be devastating. Many victims do not report the crime due to embarrassment. The resulting shame may lead to social withdrawal, depression, or, in some cases, even premature death.

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How scammers target older adults

Criminals executing sextortion scams use sophisticated tactics that take advantage of the emotional, digital, and social vulnerabilities of older adults. These include:

  • Preying on loneliness or grief: Scammers identify recent widows or widowers by scanning online obituaries or public posts. They initiate contact under the guise of companionship.
  • Romance scams on digital platforms: Many scams begin on dating apps, social media, or text messaging. Initial messages may seem innocent or friendly, but quickly turn flirtatious.
  • The "wrong number" trick: A scammer pretends to text the wrong number. If the recipient responds, the conversation escalates into a manufactured romantic interest.
  • Fake profiles and video calls: Scammers use images of attractive individuals, often stolen or computer-generated, and may encourage victims to engage in private chats. Some use deepfake technology or screen recordings to fabricate compromising content.
  • Phishing links and malware: Clicking on suspicious links can give scammers access to the victim's device, camera, or personal files. Older adults with limited cybersecurity protections are particularly vulnerable.

These methods exploit both emotional trust and gaps in digital literacy. Increasingly, these scams are not random acts. Many are carried out by transnational organized crime groups that share victim information and target retirement communities or individuals identified through data breaches.

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When sextortion escalates

A recent case reported to the AARP Fraud Watch Network Helpline involved a 70-year-old man from Missouri. He was targeted through what he believed was a romantic relationship. After sending nude photos, he was quickly threatened with exposure unless he paid $2,500.

Soon after, another person contacted him, pretending to be a law enforcement officer. This impersonator accused him of criminal behavior related to the images and warned that his employer would be contacted if he did not send more money. The scammer knew where the man worked, intensifying the pressure.

This case demonstrates how sextortion can quickly evolve from emotional manipulation to impersonation and coercion. It also highlights the importance of financial institutions being prepared to spot signs of distress, especially when older customers make unusual transactions or appear anxious.

Protecting clients from fraud: Steps to stay safe

Fraud can happen to anyone, but the FBI advises there are practical steps financial institutions can coach their clients on to help protect them and their personal information:

  • Verify before trust: If a client is contacted by someone they do not know, they should take time to research the contact’s name, phone number, email address, or the offer they are presenting. A quick online search can often reveal warnings from others who may have been targeted by similar scams.
  • Avoid being rushed: Scammers often try to create urgency to pressure victims into making quick decisions. They may appeal to their emotions or promise financial gain or companionship. Advise clients to take a step back and give themselves time to evaluate the situation.
  • Never share sensitive information: Clients should never give out personal or financial information, including social security number, bank details, or wire instructions, unless they have verified the legitimacy of the request.
  • Take immediate action if an account is compromised: If your client suspects someone has gained access to their personal devices or financial information, they should notify their bank or credit union right away. Advise them about placing additional safeguards on their accounts and remind them to monitor their activity closely for unauthorized transactions.

How financial institutions can protect their customers from elder sextortion

Banks and credit unions serve as critical touchpoints for many older adults. When staff are trained to recognize potential fraud, they can help stop scams before more harm occurs. Proactive steps include:

  • Educate frontline employees: Tellers and customer service teams should be trained to spot red flags such as abrupt wire transfers, large cash withdrawals, or changes in financial behavior. Empower staff to ask respectful, nonjudgmental questions when something seems unusual.
  • Use fraud detection tools: Implement systems that monitor behavior outside a customer's typical transaction pattern. Real-time wire fraud monitoring combined with behavioral analytics helps stop fraud losses before they occur. Banks and credit unions should combine technology with transparent internal escalation processes.
  • Provide targeted client education: Offer ongoing education through brochures, in-branch events, webinars, or one-on-one consultations. Topics include safe online practices, how to identify scams, and how to respond to suspicious messages.
  • Partner with external agencies: Establish relationships with local law enforcement, elder protection agencies, and fraud reporting centers. Coordination can lead to faster, more effective responses when fraud is suspected.
  • Foster a safe, supportive environment: Many older adults fear being judged or losing independence if they disclose being scammed. Creating a culture of support and privacy encourages disclosure and early intervention.

Why action matters

As more older adults embrace digital communication, the threat of elder sextortion will continue to grow. Financial institutions are in a strong position to detect early warning signs and respond with compassion and professionalism.

Preventing financial loss is essential, but helping clients preserve their dignity, feel supported, and recover from emotional harm is just as important. Addressing elder sextortion goes beyond risk mitigation. It reflects the core mission of community financial institutions to serve and protect. With strong fraud detection tools, well-trained teams, and a commitment to client care, banks and credit unions can be a first line of defense against this increasingly personal exploitation.

 

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FinCEN Investment Advisor Rule delay: A shift in regulatory priorities

On Aug. 5, 2025, the Financial Crimes Enforcement Network (FinCEN) issued an Exemptive Relief Order delaying the effective date of the Investment Adviser Rule. The delayed effective date gives some breathing room to affected advisers and their banking partners for adjusting systems, training staff, and coordinating with technology vendors. However, financial institutions would be wise to nevertheless begin planning for implementation, given the importance of the rule’s ultimate goal of protecting the U.S. banking system.  

Once implemented, the rule will require certain investment advisers to establish formal anti-money laundering/countering the financing of terrorism (AML/CFT) programs, maintain detailed records, and file suspicious activity reports (SARs). Finalized on Sept. 4, 2024, the rule extends long-standing Bank Secrecy Act (BSA) obligations, already applied to banks, credit unions, and broker-dealers, to segments of the investment industry that have not been covered.

It was originally set to take effect Jan. 1, 2026, but now the Treasury Department has exempted covered investment advisors from all rule requirements until Jan. 1, 2028. FinCEN intends to issue a notice of proposed rulemaking (NPRM) to propose a new effective date for the rule no earlier than Jan. 1, 2028.

 

Why FinCEN issued the delay

FinCEN said it issued the relief order to ensure the regulation is “efficient” and “appropriately balances costs and benefits” following industry feedback. According to the relief order, the extension is intended to:

  • Allow investment advisers to design and implement effective AML/CFT programs without creating undue operational risk;
  • Provide time for FinCEN to develop additional guidance and respond to industry feedback on rule interpretation;
  • Support coordination between investment advisers and financial institutions to ensure consistent application of due diligence standards;
  • Avoid compliance bottlenecks that could arise if thousands of newly covered entities rushed to meet the same deadline.

These statements are a significant shift from FinCEN’s stance when the rule was finalized in 2024.  FinCEN issued the rule after carefully considering public comment and extensive consultations with staff and various government and industry advisors. At that time, FinCEN determined that the rule would:

  • Help prevent criminals from laundering money through the U.S. financial system
  • Improve the U.S. financial system’s transparency and integrity
  • Provide beneficial information to law enforcement authorities
  • Bring the U.S. into greater compliance with international AML/CFT standards and address a significant gap identified by the Financial Action Task Force (FATF)

The delay reflects a broader regulatory approach under the federal government’s current administration, which has emphasized deregulation to reduce perceived compliance burdens across multiple sectors. Even so, the underlying need to protect the U.S. financial system from illicit actors remains, and any delay in implementation should be balanced with measures that preserve the integrity and security of our banking system.

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Why the delay matters to financial institutions

Even though the delay offers breathing room, it is still recommended that financial institutions begin preparing. Once implemented, the rule will influence how they work with investment adviser clients and bring greater alignment in compliance expectations across the industry. While banks and credit unions are not the primary targets of the rule, it will likely impact their compliance priorities, especially in customer due diligence (CDD) and coordinated financial crime prevention, assuming an effective date is eventually set.

This pause is a chance to get ahead of the ripple effects. Institutions should be ready for:

  •  Requests for closer collaboration from adviser clients
  •  More consistent application of CDD standards
  •  Heightened regulatory scrutiny once the rule takes effect

Acting now can help avoid last-minute disruptions, prevent operational bottlenecks, reduce compliance risk, and position banks and credit unions for smoother coordination with investment adviser clients when deadlines return.

We recommend that banks and credit unions prioritize the following actions for eventual compliance:

  • Update policies and procedures: Review and revise AML/CFT policies to address the specific risks and compliance expectations tied to investment adviser relationships. Document procedures for onboarding these clients, including when to apply enhanced due diligence, how to verify beneficial ownership information, and escalation steps when unusual or suspicious activity is detected. Ensure these policies are consistent across business lines so retail and commercial teams follow the same standards.
  • Evaluate technology readiness: Work with your AML software provider and core system vendor to confirm that existing platforms can handle expanded monitoring rules, higher volumes of alerts, and any additional data points associated with investment adviser accounts. Consider testing automated scenarios now to identify gaps in detection logic or reporting capabilities. Build in time for any needed upgrades, integrations, or workflow adjustments before the rule’s compliance date.
  • Train compliance and frontline staff: Develop targeted training for employees on how investment adviser business models operate and the unique risks they may present. Include case studies on common red flags such as layered transactions, unexplained transfers between client accounts, or the use of complex corporate structures. Reinforce escalation and SAR filing procedures, especially as the FinCEN Investment Advisor Rule delay concludes and expectations increase.
  • Refresh AML/CFT risk assessments: Update your institution’s AML/CFT risk assessment to reflect the potential impact of serving investment adviser clients. Consider how these relationships might influence customer risk ratings, monitoring thresholds, and the allocation of compliance resources. Document any changes in risk mitigation strategies so they can be communicated to regulators during examinations.

Next steps

The delay in the FinCEN Investment Advisor Rule is a limited opportunity for banks and credit unions to get ahead of future compliance demands. While the deadline has shifted, the expectation to maintain strong AML/CFT programs has not. Institutions that use this time to enhance monitoring processes, test technology, update risk assessments, and strengthen staff expertise will be well-positioned to adapt quickly when the rule takes effect. Early preparation reduces operational and regulatory risk and reinforces an institution’s role in safeguarding the U.S. financial system from illicit activity.

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Day 2 accounting work can get tricky 

Following a deal, financial institutions need to recognize the accretion or amortization of purchase accounting marks in an auditable and transparent way. Don't overlook income recognition.

This article covers these key topics: 

Deal activity is heating up; be accounting-ready

With deal activity heating up again, bank and credit union leaders can’t afford to be caught flat-footed when it comes to the accounting side of a merger.

Piper Sandler Chairman and CEO Chad Abraham said recently that conditions “have continued to improve for depository M&A,” according to a transcript of the firm’s recent earnings call.

“Credit’s been pretty good. There’s capital available if that needs to be part of transactions,” he said. “We’re definitely seeing proof of regulatory approvals being quicker. And I would say our pace of announcements has increased kind of across the spectrum—the small deals, a few of the larger deals.”

Improved conditions for mergers and acquisitions are good news for institutions looking to grow, but they’re also a reminder that critical financial reporting follows a deal.

Income recognition, in particular, is one of those areas that can get overlooked until it’s too late. Now’s the time to line up a merger accounting playbook that’s audit-ready and built to scale.

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Income recognition accounting can’t be an afterthought

Closing a bank acquisition is a big milestone, but it’s really just the start of the hard work. Day 2 accounting—particularly income recognition—is where things can get tricky fast. These purchase marks can be material to earnings, and if financial institutions aren’t paying attention to how they flow through their systems, they might set themselves up for issues with auditors and regulators. Abrigo Advisory has seen this firsthand. A lot of institutions get focused on valuation and CECL planning tied to a deal, and they treat income recognition almost like an afterthought. That can be a costly mistake.

What drives the complexity is the materiality of purchase marks (fair value adjustments). Deferred fees and costs, which are accounted for under the same accounting standard, aren’t significant enough for expedients to generate material differences.

Fair value adjustments, especially in dynamic rate environments, are significant, and it is the responsibility of management to understand how these amounts find their way into the income statement.

The core and income recognition: A recipe for auditor questions

A lot of institutions figure they’ll run the accretion through the core system or track it in a spreadsheet. The problem is, that only gets you so far. ASC 310-20 is the accounting standard that describes the treatment for recognizing fees, costs, fair value adjustments, etc., over the life of the loan as an adjustment to yield. As such, management should be able to recalculate yields, reconcile beginning and ending marks between reporting periods, accelerate and decelerate accretion in order to maintain yield, and provide loan-level positions. If not, something is missing.

To make matters more challenging, we've seen numerous cases where the valuation was calculated at a pool level with purchase marks subsequently being allocated to each underlying loan. This approach results in irrational yields and subsequent problems with loan-level accretion.

Time and time again, we've seen the inability of management to get any information or clarity from core-calculated accretion. When a parallel calculation is performed accurately, the results are significantly different. That’s why we always push for loan-level granularity from the beginning. It gives you control and defensibility.

CECL and income recognition

Some folks ask, “Well, can’t we handle all of this with our CECL model?”

If you’re talking about how marks flow into your allowance or how expected credit losses for acquired loans need to be calculated on Day 1 and for each subsequent reporting period, then yes, there’s overlap. But the allowance isn’t income recognition. They touch, but they’re not the same. As its name implies, the current expected credit loss (CECL) model is about expected credit losses. Income recognition is about amortizing or accreting a known mark into earnings over time. Different accounting standards and different processes.

Documentation matters here more than people think. If you can’t explain why income is rising or falling as those marks burn off, someone’s going to start asking questions. What you don’t want to happen is to get caught flat-footed by assuming the core system was handling it, or by the person who built the spreadsheet leaving without someone else knowing how it worked. That’s not a position you want to be in.

Abrigo built income recognition software with all of this in mind. It takes 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.

But regardless of what system you’re using, the point is this: don’t wing it. You need a process that scales and holds up when someone comes asking to see the details.

Who really owns the income recognition process?

We’d also encourage banks not to treat income recognition as a bolt-on task. If you’ve got one firm doing the valuation and another firm helping with CECL, and no one really owning income recognition, you’re going to have gaps. Those gaps usually show up in audits. What you want is someone who can help you stitch the full process together, someone who’s been on calls with examiners and knows what they’re going to ask.

Income recognition isn’t the flashiest part of a deal, but it’s one of the most important parts when it comes to the integrity of your financials. It’s where the marks you put on at close start hitting your earnings every month. A problem isn’t simply a technicality; it’s visible. So get ahead of it. Build the right foundation. And make sure your process isn’t merely working—make sure it’s defendable.

 

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Fraud victim support: How financial institutions can respond and restore trust

As fraud schemes evolve in complexity and scope, financial institutions are called upon to do more than just detect and prevent illicit activity. Banks and credit unions often also serve as first responders when individuals or businesses fall victim to financial fraud.

Institutions that respond with urgency and empathy to support victims of fraud can rebuild trust, restore confidence, and reinforce long-term relationships with clients. But fraud victim support is about more than recouping financial loss. Understanding the common fraud schemes clients may encounter and taking an intentional approach to assist in the aftermath demonstrates an institution’s values, dedication to client care, and role as a trusted advisor within the community.

 

The growing cost of fraud

Reported fraud losses exceeded $12.5 billion in 2024, according to the Federal Trade Commission (FTC). The FBI documented an even higher total loss of over $16.6 billion across 859,000 complaints. These figures speak not only to the scale of financial harm but to the emotional toll these crimes leave behind.

The volume and impact of fraud are increasing across all channels. In 2024, investment scams topped the list in financial damage, with $5.7 billion in reported losses. Imposter scams followed closely at nearly $3 billion. Criminals prey on trusting and vulnerable people, and they continue to leverage digital platforms to initiate contact via email, phone, or text, and move funds through cryptocurrency, bank transfers, or wire services.

According to the FBI, phishing scams were the most frequently reported. However, business email compromise and investment fraud caused the most significant monetary damage. These trends highlight the urgent need for comprehensive fraud victim support programs that go beyond the basics of account recovery.

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Understanding the scope of fraud

Financial institutions must first understand the various forms of fraud affecting their clients to deliver meaningful assistance. Some of the most prevalent methods include:

  • Cybercrime attacks: Cybercrime attacks occur approximately every 11 seconds, costing organizations an average of $13 million per incident. Small businesses are especially vulnerable due to limited cybersecurity infrastructure.
  • Consumer fraud: Consumer fraud takes many forms, including synthetic identity theft, spoofing, romance scams, and grandparent scams. These often target the elderly and financially inexperienced.
  • Business and investment fraud schemes: Scams such as Ponzi operations, business email compromise, and wire fraud continue to result in significant losses for commercial clients.
  • “Pig butchering”: A particularly alarming emerging scam is known among criminals as “pig butchering” because victims are deceived over time through emotional manipulation before being persuaded to make large financial transfers.

Each scheme can leave a trail of emotional distress and financial disruption. A thoughtful, informed approach to fraud victim support is essential to help affected individuals navigate the aftermath.

A layered approach to fraud victim support

    1. Prevention through education and technology

Preventing fraud begins with awareness. Banks and credit unions can help clients identify red flags by offering regular educational materials across digital and in-person channels. Topics include the creation of secure passwords, the identification of phishing attempts, and safe usage of peer-to-peer payment apps.

Technology also plays a pivotal role in prevention. Sophisticated fraud detection tools incorporating artificial intelligence and behavioral analytics can monitor suspicious activity in real time. Institutions can also empower their clients with biometric login, multi-factor authentication, and real-time fraud alerts.

  1. Helping clients create a response plan

Helping clients prepare a response plan before fraud occurs can reduce confusion and stress if the worst happens. Encourage clients to keep a written checklist that includes how to report fraud to their financial institutions, contact information for the FTC and FBI, and steps for freezing credit with the major bureaus. The plan should also cover resetting login credentials and enabling fraud alerts. Reviewing this plan regularly gives clients confidence that they know what to do and who to call. It is a simple way to support a long-term client relationship.

  1. Responding with clarity and compassion

A fast and empathetic response is critical following a fraud incident. Banks and credit unions should have clear procedures in place to support victim response plans, including measures around:

  • Freezing or closing affected accounts
  • Reissuing account credentials and payment cards
  • Assisting with dispute processes and documentation
  • Communicating directly with law enforcement when appropriate

Empowering front-line employees to handle these cases with care can help ease client anxiety and reestablish trust during a particularly vulnerable time.

  1. Supporting financial recovery

While banks and credit unions often must reimburse clients for unauthorized transactions, many fraud cases involve victims being tricked into authorizing payments. In these situations, reimbursement is not always guaranteed. Still, financial institutions can support victims with the following meaningful actions:

  • Assist with regulatory reporting: Help victims file official complaints with the FTC, the FBI, or Consumer Financial Protection Bureau (CFPB). These reports establish a record of the incident and contribute to broader fraud tracking efforts.
  • Work with law enforcement and other financial institutions: Cooperate with authorities and peer institutions to trace stolen funds and flag suspicious accounts. Swift action can help contain damage and may lead to partial recovery.
  • Provide recovery resources: Refer victims to identity theft protection services, legal aid, or nonprofit support organizations. These resources can help clients manage credit impacts and protect against future fraud.

Even when full financial recovery is impossible, these steps demonstrate a commitment to care and accountability. Institutions prioritizing fraud victim support during recovery reinforce trust and deepen client relationships.

Sustained support beyond the incident

Helping a client through the immediate fallout of fraud is the first step. Ongoing protection is key to rebuilding confidence. Financial institutions can offer continued support through:

  • Identity theft monitoring
  • Credit and account activity alerts
  • Help with placing credit freezes
  • Referrals to advocacy groups for seniors or other vulnerable individuals

Staying engaged after the crisis helps banks and credit unions show they are not just financial service providers but also long-term partners in their clients’ security and peace of mind.

Making victim support a shared responsibility

An effective response to fraud must involve collaboration across internal teams. Anti-money laundering (AML), information technology, fraud prevention, and client service departments should operate under a unified plan to ensure quick and coordinated action. Regular training and updates on emerging fraud trends are essential.

Equally important is leadership support. Institutions that invest in fraud prevention tools, adequate staffing, and client education signal that fraud victim support is not a side function but a core priority.

Turning crisis into opportunity

Fraud response efforts should be viewed as risk mitigation and opportunities to lead with purpose. Financial institutions can demonstrate their commitment to ethical banking and social responsibility by standing with victims and guiding them through recovery. Banks and credit unions that take fraud victim support seriously will be better positioned to retain loyal clients, enhance their brand reputation, and serve as trusted pillars in their communities.

 

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AI-driven elder fraud: Deepfakes; the newest threat

As artificial intelligence (AI) continues to advance, fraudsters are leveraging these tools to exploit one of the most vulnerable groups in our communities, older adults. According to the FBI's Internet Crime Complaint Center (IC3) data, there were $4.88 billion in losses from seniors in 2024. These numbers continue to trend upwards, and the rise of AI-driven elder fraud presents new risks to victims and financial institutions. AI-driven elder fraud involves scams that use artificial intelligence to make attacks against older adults more convincing, harder to detect, and easier to carry out on a large scale. The increased threat requires both awareness and proactive mitigation by banks and credit unions to protect clients and maintain trust in their communities.

 

The evolving tactics behind elder financial exploitation

Historically, seniors have fallen victim to fraud schemes such as phishing, romance scams, and complex investment schemes. Today's fraudsters are taking scams to a new level by using generative AI tools—such as deepfakes and voice cloning—to impersonate loved ones and create compelling, urgent scenarios. A deepfake is a video, photo, or audio recording that seems real but has been manipulated with AI. Perpetrators often extract voices from social media videos or manipulate photos to craft believable messages. These AI-powered deceptions can lead to hurried decisions by victims, resulting in panicked wire transfers, large cash withdrawals, or the sharing of sensitive account credentials.

In an example of an AI-enhanced grandparent scam, a fraudster might scan public social media profiles to learn a grandchild's name, see that they are vacationing abroad, and note that they call their grandparent "Nana." Using a voice-cloning tool and this easily accessible personal information, the scammer can generate a frightened phone call from the "grandchild" claiming to be in legal trouble and urgently needing bail money. The voice's realism and details make it alarmingly easy to convince the victim to send funds immediately, without stopping to verify the story. The nature of AI-driven elder fraud has made it more difficult to detect using traditional red flags. What once might have seemed suspicious can now appear legitimate, making staff training and innovative detection systems even more essential.

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What financial institutions can do now

Banks and credit unions are uniquely positioned to safeguard older adults through technology and personalized service. Here are some key actions institutions can take to prevent their clients from becoming victims:

  • Employ robust fraud detection software:  Enhance fraud monitoring systems to flag unusual activity on accounts held by older adults, typically aged 60 and above. Use tailored parameters to detect anomalies like sudden large wire transfers, frequent ATM withdrawals, or new payees that do not align with the client's typical behavior. These targeted settings improve your institution's ability to catch early signs of AI-driven elder fraud and take timely action.
  • Train employees to recognize new scams: Equip front-line staff and fraud teams with practical training to identify signs of AI-driven elder fraud. These signs can include clients who appear anxious, confused, or unusually secretive during large transactions, or those referencing family emergencies with limited or inconsistent details. Staff should know how to respond empathetically, ask clarifying questions, and escalate concerns when needed. Regular training helps teams stay alert to evolving scam tactics and reinforces a culture of prevention.
  • Clarify communication protocols: Remind clients, especially seniors, that your institution will never request sensitive information like passwords or social security numbers by phone, email, or text. Understanding communication methods is critical as AI-driven scams increasingly use cloned voices and urgent messages to pressure victims. Make it clear that legitimate staff will not use threats or demand immediate action. Encourage clients to hang up, verify requests by calling a published number, and ask questions. Reinforcing this message during visits, alerts, and outreach helps build confidence and reduce the risk of fraud.
  • Build trust through relationships: Strong relationships with long-time clients are key to spotting and preventing fraud. Encourage staff to visit clients when something feels off, using a conversational tone to avoid alarming or upsetting the client. For example, saying, "That's a larger transaction than usual. Is everything okay?" can open the door for a helpful discussion. Building trust before issues arise makes it easier to address concerns if signs of elder fraud appear later.

Find out how Abrigo Fraud Detection stops check fraud in its tracks.

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Understanding regulatory expectations

Financial institutions are expected to play a central role in identifying and reporting elder financial abuse. With AI-driven elder fraud rising, examiners and enforcement agencies may scrutinize how effectively institutions adapt to emerging typologies. The Financial Crimes Enforcement Network (FinCEN) has named fraud one of its national AML/CFT priorities, emphasizing the importance of proactive detection and reporting. Filing a suspicious activity report (SAR) is just one component; maintaining a culture of vigilance and continuous training is equally critical.

Institutions integrating fraud detection with anti-money laundering (AML) processes are better positioned to respond quickly to evolving threats. AI and machine learning can enhance monitoring by identifying unusual behavioral patterns that are common in modern fraud cases. While operational functions may remain separate, collaboration between fraud and AML teams is essential. Working in silos is no longer effective in detecting complex, AI-driven fraudulent activity.

Community education can prevent losses.

Technology is essential, but it is not the only solution. Many cases of AI-driven elder fraud can be avoided through targeted education and outreach. Consider hosting in-person fraud awareness sessions at senior centers, places of worship, or branch locations, where trusted staff can explain how fraudsters use AI to manipulate voices, images, and personal information. Partnering with local organizations or law enforcement can add credibility and help reach broader audiences. Institutions can also distribute printed guides or quick-reference tip sheets that walk through common scam scenarios, what to look out for, and how to respond. Posting short educational videos on your website or sharing alerts through account notifications and email campaigns reinforces these lessons and helps keep seniors informed between visits. A consistent focus on community education builds trust and positions your institution as a proactive ally in fraud prevention.

Practical tips to share with clients

Educating seniors with simple, actionable steps can go a long way in preventing AI-driven elder fraud. Consider sharing the following guidance during outreach efforts or in printed materials at branches:

  • Confirm unexpected requests: If someone claims to be a relative in trouble or a representative from the bank, urge clients to hang up and call back using a known, trusted number, never the one provided in the message or call.
  • Be cautious with links and urgent messages: Remind clients not to click on links, download attachments, or send money based on a single phone call, text, or video, even if the message appears to come from a loved one. AI tools can make fake messages seem personal and convincing.
  • Enable account alerts: Encourage seniors to set up text or email alerts for large transactions or unusual activity. These real-time notifications can provide an early warning and allow for quick intervention.
  • Review account activity regularly: Suggest checking account statements frequently or enrolling a trusted family member to help monitor for suspicious transactions.

Sharing these tips in clear, non-technical language can empower clients to act confidently and avoid becoming victims of increasingly sophisticated fraud attempts.

 

Protecting seniors in the age of AI

As fraud tactics evolve with AI, so must the strategies used to stop them. Financial institutions have a unique opportunity, and responsibility, to protect older clients through education, collaboration, and well-equipped fraud detection programs. By combining personal relationships with innovative technology and ongoing awareness efforts, banks and credit unions can serve as a first line of defense against AI-driven elder fraud. Staying informed and proactive today means safeguarding trust and financial well-being for the seniors who rely on you tomorrow.

 

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AI value opportunities for banks and credit unions: A sample 

AI is reshaping how financial institutions operate, compete, and deliver value. Learn some of the areas in banking where predictive and generative AI are creating the most value and where to start.

A framework for AI opportunity for financial institutions

Artificial intelligence (AI) is no longer a future-facing technology — it’s a present-day differentiator. Across banking, AI is reshaping how financial institutions operate, compete, and deliver value. From marketing to compliance, the most promising AI use cases in banking help organizations improve decision-making, reduce operational risk, and grow more efficiently. This article explores a sample of opportunities where AI creates the most value today by showing major areas of banking where artificial intelligence (either predictive or generative) can be useful. It also explains how financial institutions can safely begin or accelerate their AI journey and identify applications for the technology in their own banks or credit unions.

Learn the basics of AI and an approach to adopting it.

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Examples of current AI applications across banking

There are two primary forms of AI driving transformation: predictive AI, which forecasts outcomes and detects patterns, and generative AI, which creates new content from existing data sources​.

The graphic below illustrates dozens of current AI applications across the banking lifecycle.

Graph of automation and AI-driven value opportunities

Current applications of artificial intelligence across banking fall into six core areas of opportunity, described below along with examples of how financial institutions are already benefitting from AI on these fronts:

  1. Marketing and sales

AI enables personalized engagement and smarter targeting for bank and credit unions’ marketing and sales.

  • Predictive models forecast customer lifetime value
  • Generative tools create hyper-personalized messaging and offer recommendations
  • AI segments audiences for cross-sell campaigns and streamlines onboarding

Personalizing content on JPMorgan’s mobile phone apps helped increase engagement rates by 25%, the company said during its May Investor Day.

  1. Prospecting and onboarding

AI can reduce friction in early-stage customer interactions for banking services.

  • Automate document verification and identity validation
  • Prepopulate onboarding forms and streamline KYC workflows
  • Use chatbots for initial data collection and customer guidance

Costs to verify investment bank clients are down 40% where AI is being deployed across the workflow, JPMorgan also said.

  1. Credit risk underwriting and review

AI enhances accuracy and speed in credit decisions, helping lenders make good decisions quickly.

  • Predictive models analyze cash flow, credit scores, and risk thresholds
  • Generative AI assists in drafting credit memos and narrative summaries for loan reviews
  • Real-time data integration supports more holistic, dynamic underwriting​

Bankers Trust, a $7 billion community bank, reduced its commercial loan process for certain loans from two weeks to three to five days using Abrigo’s loan origination for smaller commercial loans, which automates decisioning and features AI-powered loan scoring. And Abrigo’s Loan Review Assistant allows credit risk review staff to evaluate credit quality and document insights in minutes rather than days.

  1. Operations

Financial institutions can improve back-office efficiency with AI automation.

  • AI routes payments, classifies documents, and extracts insights
  • Automated financial spreading saves hours of manual entry
  • Collections strategies are optimized through borrower-level pattern recognition

NVIDIA’s latest survey of financial institutions’ use of AI found that more than 60% of respondents credited AI with helping reduce annual costs by 5% or more.

  1. Customer support

With artificial intelligence, financial institutions boost service quality while scaling support teams.

  • Chatbots answer common questions and reduce call center volume
  • AI listens to and analyzes call transcripts to coach agents and spot risk indicators
  • Personalized engagement improves retention and satisfaction

Bank of America’s AI-driven virtual assistant for employees, Erica for Employees, reduced calls into the IT service desk by more than 50%, the bank said. Similar support improvements can benefit clients.

  1. Risk and compliance

Both predictive AI and generative AI enable institutions to meet regulatory demands with precision and agility.

  • Alert narratives and ongoing due diligence tasks can be automated
  • AI helps detect fraud patterns across transactions
  • Compliance checks are embedded into loan review and audit workflows​

Texas National Bank uses Abrigo’s AI-driven check fraud detection to identify fraudulent checks before they are cashed. Within just two months, the bank identified and prevented over $377,000 in fraudulent check transactions.

Altogether, these banking AI use cases drive measurable business benefits: faster loan decisions, higher operational efficiency, improved accuracy, and reduced churn.

Check out helpful AI resources for bankers, including an AI-readiness checklist.

AI resources

How to prioritize projects when implementing AI

The range of available AI use cases in banking can feel overwhelming. But successful institutions typically begin with focused, high-impact projects that align with their data readiness and staffing capacity.

To start:

  • Partner with trusted providers who understand regulatory frameworks and can integrate AI into existing systems. Abrigo prioritizes data security and privacy by developing AI technology with stringent data protection measures, using encrypted data environments and robust access controls to secure client data. Make sure that any vendors you choose have similar controls in place. You may choose to consider vendors that specifically work with financial institutions so that you can be sure their solutions fully comply with banking regulations. Vendors should be continuously monitoring regulatory landscapes to ensure their solutions meet legal and regulatory requirements.
  • Experiment with pilot programs such as automating credit memos or onboarding flows to introduce AI one process at a time. Identify and prioritize low-risk, high-value pilot projects, and make sure that leaders from across the organization are united when assessing the feasibility, risks, and intended outputs before starting a project. Once an AI tool is adopted, conduct ROI analysis regularly to make sure the new process is working as intended.
  • Educate your team on what AI is and what it isn’t to build buy-in across departments​. While AI automates certain tasks, it primarily augments the capabilities of banking staff by allowing them to focus on more complex and strategic activities. Done well, this enhances job satisfaction and productivity. Make sure staff are well-trained and emphasize that a human-in-the-loop is always necessary to keep AI processes running smoothly.

From AI’s value potential to AI’s value creation

Long-term AI success requires thoughtful governance, clean data inputs, and strategic planning. With the right use cases and the right partners, banks and credit unions can unlock the true value of AI: accelerating growth, reducing risk, and improving every interaction.

See how an AI assistant shaves days off the loan-review process for more efficient credit risk review.

Loan review assistant
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