Managing risk amid economic uncertainty
Even with unclear economic outlooks, financial institutions can make sound risk management decisions. Learn how to get more value from required risk management activities.
Key topics covered in this post:
The new risk management challenge: Preparing without predicting
August 5, 2026
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The value of risk management practices in uncertain environments
Today’s economic outlook gives financial institution leaders few clear signals. Growth remains resilient, but uncertainty around inflation, interest rates, credit performance, and consumer strength continues to complicate reserve decisions, capital planning, and portfolio management.
That uncertainty increases the value of risk management practices that produce useful information across several plausible outcomes. The current expected credit loss (CECL) model, model governance, and emerging analytical tools can help leaders evaluate changing conditions, test assumptions, and make better-supported decisions without relying too heavily on a single forecast.
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Connect with an expertRefine CECL for the risks the portfolio actually carries
Much of the industry’s early CECL work centered on implementation. Institutions selected methodologies, developed forecasts, documented processes, and established governance frameworks. The conversation has since shifted toward performance, refinement (including ongoing CECL model management), and practical use.
Community financial institutions continue to face a particular challenge when historical loss experience does not fully reflect portfolio risk. Smaller datasets and concentrated portfolios can make loss rates volatile. A single charge-off may have a significant effect on the calculation without offering a complete picture of expected losses.
Historical performance remains important, although institutions should also evaluate inherent risk. Peer data, economic forecasts, portfolio characteristics, and qualitative analysis can add context when an institution’s own loss history is limited.
CECL requires a forward-looking estimate grounded in current conditions and reasonable and supportable forecasts. The allowance should represent the best estimate supported by available information. Excessive conservatism in loss forecasting can be just as difficult to defend as an estimate that fails to account for emerging risk, according to speakers at Abrigo’s recent ThinkBIG conference.
Align model governance with materiality
Questions about how often a model should be validated can overshadow a more important issue: how much risk the model creates for the institution.
A useful model governance framework starts with an inventory, materiality assessment, and risk-ranking process. These concepts are not new, but they are becoming increasingly important as banking institutions expand their use of models across credit, liquidity, asset/liability management, anti-money laundering, and financial reporting functions. These steps help leaders understand where models are used, which decisions they influence, and what could happen if their output is unreliable.
A risk-based model risk management approach allows financial institutions to focus more attention on models that affect significant financial reporting, capital, liquidity, credit, or strategic decisions. Lower-risk models may warrant a more proportionate level of oversight. In other words, the framework helps focus attention on the models that have the greatest influence on decision-making while allowing institutions to scale oversight activities for lower-risk applications.
Validation also provides value beyond meeting a recurring requirement. Testing data quality, evaluating assumptions, confirming calculations, assessing performance, and identifying weaknesses can improve confidence in the information leaders use.
Use AI to expand analytical capacity
AI adoption in risk management has moved into practical applications such as documentation review, report preparation, data extraction, financial statement tie-outs, and routine analytics.
Historically, auditors and reviewers often relied on sampling approaches due to practical limitations on time and resources. New tools increasingly allow organizations to evaluate entire populations and identify anomalies and higher-risk items. The broader coverage can be especially valuable in audit and validation.
Professional judgment remains essential. AI can reduce the time employees spend gathering and organizing information, giving them more capacity to evaluate results and investigate exceptions.
When considering AI adoption, many institutions remain focused on identifying the perfect solution before taking action. Change management experts advise a more practical approach: identify high-value AI use cases in banking, begin with manageable implementations, and expand capabilities over time. This philosophy may prove particularly important for community institutions, where resource constraints often make incremental improvements more achievable than large-scale transformations. Early applications can help leaders understand where the technology adds value and where additional controls are needed.
Financial institutions will always have to adapt to changing regulatory expectations and technological capabilities. When it comes to extracting the most value from required activities, the most helpful question is often, “What are we gaining from these activities beyond checking a compliance box?”
For more on turning risk management data into business intelligence, download “2026 Risk management playbook: What bankers need to know"
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The information, content and materials provided through this website are for informational purposes only and are not intended to constitute legal advice. Customers should consult with their legal counsel regarding the application of laws and regulations to their specific circumstances.
About the Author
Kate Randazzo
Senior Content Marketing Manager
Abrigo
Kate Randazzo is a Senior Content Marketing Manager at Abrigo, where she collaborates with industry thought leaders to develop digital content for banks and credit unions. Drawing on her background in strategic communications and content marketing, she translates complex financial topics into practical insights that help financial institutions better serve
