What Is CECL in Banking?

CECL, or current expected credit loss, is an accounting standard released by the Financial Accounting Standards Board (FASB). Its purpose is to improve the accuracy with which credit losses are recognized by lending institutions.

While previous models only required recognition of losses after evidence of delinquency, CECL requires banks to estimate lifetime losses from the time the loan is funded.

CECL applies to assets subject to risk. This can include loans, leases, held-to-maturity debt securities, and off-balance sheet exposures.
By forecasting losses throughout the entire life of a loan, CECL aims to provide a more comprehensive and accurate representation of both short-term and long-term risk.

Note that while CECL was implemented for large banks in 2020, smaller institutions, such as community banks and credit unions, had a deadline of 2023.

This created a push to quickly develop models that could be defensible, efficient, and accurate — challenging tasks considering the more limited nature of resources and the need to balance regulatory expectations with operational capacity.

Learn more about the timeline and evolution of the CECL standard and how it has redefined the risk management landscape.

Why CECL Replaced the Incurred Loss Model

Prior to CECL, banks utilized the incurred loss model to report credit losses.

The incurred loss model only required the recognition of losses when they were probable and reasonably likely to occur. This resulted in an incomplete picture of the level of risk and the amount of losses that could occur, leading to insufficient reserves to cover loan defaults.

In other words, the incurred loss model proved to provide too little warning too late in the process to make adjustments in managing credit risk. CECL was introduced to solve this issue by requiring the estimation of losses over the entire lifetime of a loan.

Instead of waiting for loans to exhibit signs of potential losses, CECL allows lending institutions to take a more proactive approach to anticipate losses earlier in the credit cycle.

This new methodology introduced under CECL helps boost investor confidence, as it forces banks to better understand how economic shifts and other business decisions can impact cash flow, reserves, and the bank’s exposure to risk.

By factoring in a wider range of economic data points and assumptions, it also helps institutions better manage risk to create more viable long-term financial strategies.

See our guide on CECL reasonable and supportable forecasts to better understand how banks can incorporate forecasting into their data models.

Pro tip: Take a proactive approach in communicating business decisions impacted by CECL, as it can help build trust with board members, regulators, and investors.

Key Components of the CECL Model

While banks may use varying methods, creating a model for CECL requires five essential components, each of which is crucial to produce accurate, reliable, and usable loss estimates:

• Historical Loss Data: Past performance can be a reliable factor in forecasting future losses, given similar sets of consumer behaviors and market conditions.
• Current Conditions: Current market conditions, such as unemployment rates, loan interest rates, and bank-specific factors, can be weighed against past market conditions to predict future behaviors.
• Reasonable and Supportable Forecasts: Economic forecasts must be made over a specified period of time. These forecasts should reflect varying scenarios and be based on assumptions that can be documented and defensible.
• Reversion Periods: Following the forecast period, models should revert to historical averages and trends, as projections done too far into the future become increasingly unreliable.
• Qualitative Adjustments (Q-factors): Data isn’t always able to capture events that may impact credit losses. Management judgment here allows for external risks, such as policy changes or other economic events, to be considered in estimating credit losses.

CECL Modeling Approaches

Multiple methods can be used to create a model that satisfies CECL standards. Many banks choose from several different financial models depending on the composition of loans in their business portfolio, as well as the availability of resources and data.

Below are five common types of financial models used to meet CECL standards:

• Discounted Cash Flow (DCF): Considers the expected future cash flows from loans and discounts them to present value figures.
• Probability of Default/Loss Given Default (PD/LGD): Considers the likelihood of default independent of the severity of the potential loss to achieve a deeper analysis of each.
• Vintage Analysis: Groups loans by origination year to identify how different years perform as time goes on.
• Roll Rate Models: Tracks how loans progress through various delinquency stages (30-, 60-, 90-day past due) to forecast future losses.
• Regression or Advanced Analytics: Utilizes statistical or machine learning techniques to identify key drivers of loan defaults. Many of these models can incorporate external factors, such as economic conditions and borrower characteristics.

Pro tip: Utilizing multiple methods can result in a higher confidence level for forecasts and can help prove that any assumptions made are reasonably sound in meeting the requirements of a sound risk management model.

CECL Challenges and Risks for Banks

Satisfying CECL standards has been challenging, even for large lending institutions.

For smaller community banks and credit unions, understanding CECL is just part of the challenge. The difficulty lies in being able to implement it effectively with fewer resources, staff, data, and technology:

• Data Gaps: Smaller banks may find it difficult to compile a solid foundation of data that can support statistically sound models. Rather, gaps in data are often filled in with less reliable assumptions.
• Forecast Uncertainty: A major part of being able to build a sound forecast involves a strong understanding of economic modeling, which smaller banks may not have. Even minor changes in economic market conditions can result in drastically different conclusions.
• Governance: CECL requires strict management of documents and version control, which smaller banks may struggle with due to a lack of resources and automation.
• Capital Impact: Since CECL measures the potential of losses over the life of a loan, even small changes in assumptions can significantly impact cash flow and earnings.
• Operational Burden: Institutions relying on manual processes are at greater risk of human error, particularly on finance teams that are already stretched thin.

Pro tip: For insights into how lending institutions can measure and manage these risks, explore our article on risk management statistics. Additionally, note that integrated CECL and asset and liability management (ALM) tools can help streamline inputs and outputs in one system, with automated workflows being key in reducing human oversight.

Best Practices for CECL Compliance

For smaller lending institutions like community banks and credit unions, meeting CECL requirements doesn’t have to involve large amounts of capital being invested into complex systems, nor does it require revamping every policy, procedure, or workflow.

Here are four minimally invasive best practices that even small finance teams can utilize to meet regulatory expectations:

• Invest in Data Governance: Focus on centralizing loan data. This helps teams acquire a single source of truth to provide consistency and accuracy, which can help with model credibility.
• Refresh Forecasts: Forecasts do not need to be updated on a daily, weekly, or even monthly cadence. In most cases, quarterly updates are sufficient to align with business planning cycles.
• Perform Scenario Testing: Scenario testing should be utilized to determine how key factors, such as deposit flows, loan demand shifts, and rate shocks, impact forecasted credit losses.
• Ensure Board Engagement: Since CECL projections have a direct impact on a bank’s cash flow and other critical financial drivers, board members should understand its assumptions and limitations to ensure informed oversight.

How Empyrean Simplifies CECL

Understanding “what is CECL” is just part of the answer. Being able to implement it effectively is where many banks struggle.

Since CECL requires institutions to forecast lifetime losses from loans, it requires a greater deal of data complexity and data governance than the prior incurred loss model. This can be a big challenge, particularly for smaller banks and credit unions that may have limited staff and outdated technology.

Many of these lending institutions still rely on manual processes and reporting and outdated assumptions, and have isolated departments that may not communicate regularly. This makes CECL compliance time-consuming and at high risk of inconsistencies.

Without integrated systems, it can be difficult to complete tasks like forecasting, planning, and asset and liability management — all of which are necessary for effective CECL compliance.

Empyrean Solutions makes CECL compliance simple by combining forecasting, reporting, and credit loss modeling into a single platform:

• Empyrean CECL: Built for ease of use, this gives teams a way to arrive at a quick starting point for estimating credit losses.
• ALM Integration: Takes CECL figures and ties them in with liquidity and interest rate risk management to ensure alignment between reserve requirements and broader business strategic planning.
• Budgeting and Planning: Allows teams to align CECL forecasts with annual budgets, eliminating the need for separate or manual workflows.
• Profitability Analysis: Helps finance leaders quantify the impact of CECL at various levels to make better-informed business decisions.

Empyrean ultimately allows even small finance teams to access enterprise-level tools to meet regulatory requirements, without needing to hire additional employees or expand staffing.