The Current Expected Credit Loss (CECL) standard was introduced as a way to estimate expected credit losses more accurately. It requires lending institutions to consider future events when assessing potential losses over the lifetime of their loans.
This is done to create reasonable and supportable forecasts, rather than just taking an approach that focuses on historical data.
This is a significant shift for smaller community banks and credit unions, as complying with CECL has proved to be challenging. Many of these institutions lack the resources of larger, more established banks, which would otherwise simplify modeling, data collection, and data management.
As outlined in our CECL overview, the standard further requires banks to be able to analyze and interpret that data in creating defensible forecasts and assumptions.
To eliminate redundant steps, this guide explains what is necessary to meet the CECL standard, such as how to determine a reasonable forecast timeline and overcome common challenges. Empyrean offers integrated tools to further simplify the process, like the CECL calculation platform, to help with testing models under various scenarios and assumptions.
What Does ‘Reasonable and Supportable’ Mean in CECL?
For estimated credit losses to be considered reasonable and supportable, they must be based on assumptions that can be documented with data. This applies not only to loans, but also to leases and held-to-maturity debt securities.
Under the Financial Accounting Standards Board’s (FASB’s) guidance, this is a core requirement to meet CECL compliance. Lending institutions can achieve this by focusing on current market conditions and future events that are relevant to their own loan portfolios.
This means institutions should not make estimates based on general market sentiment or intuition. Regulatory examiners would require that any estimates or adjustments be backed by data.
Unemployment rates, shifts in borrower repayment patterns, and changes in property values are some examples that could be considered reasonable and supportable.
For smaller banks, having sufficient documentation is especially crucial. These institutions are likely to have shorter histories of loan repayment or losses.
With limited historical information, even small changes in assumptions can result in drastically different sets of outcomes. This makes it even more important for them to demonstrate that their assumptions are based on reliable data with appropriate inputs for their portfolios.
How Long Should a CECL Forecast Period Be?
The CECL standard requires that credit loss forecast periods be made as long as assumptions and data remain reliable. Once that point has been passed or the underlying data can no longer be considered reasonable and supportable, models must revert to historical averages.
There is no set time frame for how long a forecast period should be. That is dependent on every bank’s unique composition of loans, availability of historical data, and resources that can be dedicated towards modeling efforts.
For instance, smaller banks and credit unions with limited staffing may only be able to forecast several quarters into the future if they’re only able to consider internal loan trends and local or regional economic factors.
By contrast, larger national banks may be able to forecast further into the future, as they may have access to advanced modeling tools that can consider larger macroeconomic trends.
While it may not be possible to predict the future, CECL’s purpose is for institutions to make defensible projections based on a reasonable set of data or assumptions.
Pro tip: Reviewing data and assumptions regularly, at least quarterly, can help ensure forecasts remain accurate and reliable based on any shifts in market trends, current events, or policy shifts.
CECL Reversion Methods
For CECL standards, reverting to historical averages can be done in one of several ways. Institutions can choose the reversion method best suited to them based on the composition of their loan portfolios.
The goal here is to ensure forecasts of credit losses remain accurate and credible, avoiding assumptions that cannot be supported by data.
- Straight-line reversion: Slowly transitions to historical averages from previous forecast conditions, effectively providing a more gradual shift between periods. This can work well for small banks that have stable portfolios.
- Immediate reversion: Instantly shifts back to historical averages following the period of reasonable and supportable data in the initial forecast. This is typically the simplest and most defensible method for smaller institutions and those with limited data or resources.
- Stepped reversion: Adjusts assumptions on an incremental basis, such as monthly, quarterly, or annually, over a period of time.
- Hybrid approaches: Combines elements of all or some of the above methods, suitable for more complex portfolios that have varying risk dynamics.
Each of the reversion methods above can be achieved with different types of financial models, with the most suitable one being dependent on a bank’s structure and availability of data.
Pro tip: Regardless of which method is selected, document the reasons why it is most suitable for your specific bank’s circumstances. Doing so can build transparency and trust from investors, board members, auditors, and regulators.
Challenges Banks Face in Setting Reasonable and Supportable Forecasts
Large national institutions may have dedicated in-house economists who make it simpler to predict rate and credit shifts. However, for small banks, creating reasonable and supportable forecasts to meet CECL standards can be challenging due to limited staffing, a lack of historical data, and access to sophisticated modeling resources. Some specific hurdles may include:
- Data gaps: Smaller banks may not have large amounts of historical loan or loss data. They may also lack the resources to properly analyze loan performance by differing characteristics, such as location or product type. As a result, it can be difficult to connect assumptions with relevant trends. Tools that track risk management statistics can help overcome this hurdle.
- Forecast uncertainty: Without dedicated teams of forecasters, experienced modelers, or economists, it can be difficult to project how credit losses may be impacted in response to interest rate changes, unemployment shifts, or other economic trends.
- Portfolio complexity: Even banks with simple loan offerings can yield portfolios with a wide range of risk profiles. Commercial real estate, agricultural lending, and consumer loans may require distinct and separate approaches to modeling.
- Governance pressure: To avoid fines and penalties, banks must ensure consistency when it comes to creating, documenting, and maintaining forecast models.
- Operational burden: Relying on manual processes increases the likelihood of human error, taking even more time away from finance teams that may already be stretched thin.
Pro tip: Having a central location and a single source of truth for CECL-related data can reduce errors and ensure a higher level of efficiency and accuracy when it comes to updating workflows and performing updates.
Best Practices for CECL Reasonable and Supportable Forecasts
- Consistency: Consistency is key in generating reasonable and supportable forecasts under CECL. Regulators know it’s not possible to predict the future, but they do expect institutions to make assumptions based on relevant data and to apply them consistently and have them well-documented.
- Brand-Specific Models: A strong foundation for adhering to CECL standards begins with driver-based models relevant to your institution’s specific book of loans. It should be based on internal indicators like delinquencies, payment behavior, and days past due (DPD), rather than industry averages.
- Trusted Macroeconomic Data: Macroeconomic data from trusted sources can also be integrated to further boost credibility, accuracy, and reliability. Published data from reputable sources like the FDIC, the Federal Reserve, CSBS, and FOMC can help in recognizing current economic conditions and events.
- Targeted Stress Tests: Targeted stress tests can also help validate the adequacy of current financial reserves and risk management in the event of rate shocks, deposit volatility, or other local lending trends.
- Synced ALM Cycles: Finally, ensure that CECL forecasts are in sync with asset and liability management cycles, as this can help boost efficiency. It can also establish consistency in assumptions across regulatory reporting and financial planning.
Pro tip: In meeting CECL standards, the focus should be on ensuring assumptions are grounded, consistent, and defensible. Perfect forecasts are not necessary to be considered compliant.
From Compliance to Confidence: How Empyrean Helps
Many banks struggle with developing what can be considered CECL data-driven forecasts. It’s an incredibly time-consuming and challenging process since many institutions store data in siloed systems, and different teams utilize varying sets of assumptions.
This lack of consistency makes it challenging for teams to rely on insights and output from CECL projections.
To eliminate the need for external consultants, Empyrean solves these challenges with tools like the Empyrean CECL solution, which banks can use to determine a starting point. Streamlined integration with existing systems allows banks to run multiple scenarios while documenting each assumption for clarity and transparency.
Empyrean’s budgeting and planning tools also help ensure consistency in the data and assumptions that are used across different functions, such as loan growth, capital planning, provisions for reserves, and expenses.
Empyrean’s ALM can also help run the same scenarios for liquidity and interest rate risk analysis, providing a more comprehensive view of how shifts in the market can impact the balance sheet.
The Profitability module further improves visibility by weighing CECL’s impact on business margins, product lines, and even branch-level performance so finance leaders can manage performance and compliance at the same time.
In satisfying CECL standards, institutions must substantiate reasonable and supportable forecasts, a challenging ask even for the large national lenders. Empyrean’s platform is tailored to help smaller banks meet CECL requirements with accuracy and transparency without overwhelming finance teams.
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Explore our CECL solutions to see how Empyrean simplifies CECL forecasting for institutions with limited resources.
FAQ: CECL Reasonable and Supportable Forecasts
What Are the CECL Requirements?
Under CECL, lending institutions, such as banks and credit unions, must estimate credit losses over the lifetime of their loans. These estimations and forecasts must be done with data that can be considered reasonable and supportable.
CECL’s purpose is to provide a more comprehensive and accurate illustration of risk and potential losses.
What Happens After the Reasonable and Supportable Period Ends?
Once a forecast reaches a point in time where it can no longer be considered reasonable and supportable, CECL requires the model to revert to historical trends in forecasting from that point forward.
What Tools Help Banks Create Defensible CECL Forecasts?
CECL forecasts can be challenging. Out-of-the-box reporting, scenario analysis, and the ability to integrate with ALM, budgeting, and planning are all tools that can simplify the process of creating defensible forecasts.
Empyrean’s platform, including its CECL solution, offers all of this and more, allowing banks to combine compliance with strategic financial and risk management.