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Portfolio Risk & CECL CECL Accounting CECL Methodologies CECL Models

The Fed’s ELE tool for CECL: What it is and isn’t

Mary Ellen Biery
June 19, 2022
Read Time: 0 min

New Fed tool: ELE for 2023 CECL implementation

The Federal Reserve's new Expected Loss Estimator, or ELE, tool for CECL is a spreadsheet-based option for smaller financial institutions to implement the current expected credit loss standard.

You might also like these webinars especially for 2023 CECL adopters: "CECL Streamlined."

WATCH

Takeaway 1

The ELE tool for CECL from the Fed is aimed at reducing the operational burden of implementation ahead of the 2023 deadline. 

Takeaway 2

ELE relies on the institution to provide loan-level data, assumptions for prepayment rates, and annualized loss rates for individual loan types.

Takeaway 3

The ELE tool for CECL requires an institution to determine the appropriate data and assumptions for layering qualitative adjustments.

Spreadsheet expected loss estimator

ELE tool for CECL released by Fed

The Federal Reserve unveiled its Expected Loss Estimator, or ELE, tool – a second spreadsheet-based tool aimed at helping smaller financial institutions implement the current expected credit loss (CECL) standard. And while regulators said some institutions would find the ELE tool useful for CECL, they acknowledged it did not represent a preferred method of regulators or a “safe harbor” method for GAAP compliance. They said it would not change examiner reviews of CECL allowance components.

Banks and credit unions facing a 2023 deadline for implementing CECL are within six months of the adoption requirement, Fed Governor Michelle Bowman said on a webinar with bankers unveiling the new expected loss estimator. Pronounced “Ellie,” the ELE tool for CECL is aimed at reducing the operational burden on smaller financial institutions and will support implementation, she added.

“This new tool is an automation of an existing CECL methodology, the weighted average remaining maturity, or WARM methodology,” Bowman said.

Regulators during the webinar described several characteristics of the ELE tool that financial institutions will find helpful in understanding it as they decide whether to implement CECL on their own, automate the allowance using CECL software, or outsource CECL implementation entirely. 

They also reminded financial institutions that CECL would be a topic during exams. “Prior to the required CECL adoption in 2023, community financial institutions can expect that examiners will be interested in and asking how they are progressing in their preparation for CECL,” said Chris Riba, Assistant Vice President of the Minneapolis Federal Reserve.

“Examiners will be interested in and asking how they are progressing in their preparation for CECL.”

Get advice from two recent CECL adopters in this whitepaper: "Implementing CECL for Large and Small Banks."

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Loss estimator uses WARM

What the ELE tool for CECL is

Based on the webinar and supporting resources for the ELE tool for CECL, it’s apparent the ELE tool is:

  • A spreadsheet-based tool that supports CECL implementation.
  • An option for automating parts of CECL implementation using the weighted average remaining maturity, or WARM methodology.
  • Intended to be considered only by institutions with less complex financial assets.
  • Reliant on institution-provided loan-level data, as well as assumptions for annualized prepayment rates and annualized loss rates for individual loan types.
  • Allows institutions to use loan segments beyond those on the Call Report. Loans in the same portfolio are expected to have the same payment frequency and amortization type, as well as similar risk characteristics, and the ELE tool is limited to 25 portfolios.
  • Aimed at providing transparency in the calculation. The tool uses Visual Basic for Applications, or VBA, programming language to provide information in each cell about the source of the calculation within it.

Related subhead

What the ELE tool for CECL isn’t

The webinar also described what the ELE tool for CECL isn’t or what it cannot do for financial institutions. For example, the ELE tool:

  • Requires the financial institution to develop its own loss estimates for loan types as ELE does not provide loss rate assumptions.
  • Does not supply prepayment assumptions. The ELE tool applies prepayment information provided by the financial institution.
  • Requires the institution to determine the appropriate data and assumptions to use in layering qualitative adjustments.
  • Lacks integration with economic data/forecasts.
  • Requires the financial institution to calculate the allowance outside the ELE tool for the following:
  • Nonperforming or delinquent loans
  • Securities portfolios
  • Unfunded commitments
  • Loans with guarantees
  • Requires the institution to capture and provide documentation or narrative for all policies, procedures, and decisions related to the allowance since the tool doesn’t have that capability.
  • Does not change supervisory expectations related to CECL implementation. “Supervisory expectations will not be changing for the WARM method and the ELE tool,” Riba said. “They apply regardless of the tool used to implement CECL.”

Regulators encouraged bankers to use the methodology that would develop a reasonable and supportable CECL forecast that makes sense, taking into account their institutions’ risk appetite, underwriting standards, quality of loans and performance, and other portfolio characteristics. They also noted that the financial institution’s board or a board committee is responsible for overseeing management’s judgments and estimates used for the allowance for credit losses.

Exam prep

Relevant supervisory questions about CECL

As the adoption deadline for CECL draws closer for community financial institutions, supervisory questions have been circulated in an examination preparation questionnaire to smaller depository institutions ahead of or during this year’s exam cycle. Several of these are relevant, especially if an institution is considering utilizing either the ELE tool or the other tool provided by the Fed, the Scaled CECL Allowance for Losses Estimator, or SCALE.

The topic areas have centered on forecast sources and forecast lengths, economic inputs, and qualitative adjustments. Here are a few examples of relevant known supervisory questions:

Economic inputs

  • Which economic data series are you using (unemployment rate, GDP, etc.)?
  • Are you using multiple data series, and if so, how are you weighting them?
  • Are you using national data or local?

Forecast sources and lengths

  • What is the source of the forecast data?
  • Are you relying on baseline forecasts or considering multiple scenarios and weighting them based on management’s judgment?
  • What is the length of your forecast?
  • Is your forecast period fixed or dependent on the average life of the loan portfolio and/or the perceived forecast uncertainty?

Qualitative adjustments

  • Are you using a formulaic approach or qualitative adjustments in adjusting credit loss estimates due to changes in the forecast?

In addition to the above questions, financial institutions should anticipate questions around several topic areas based on the supervisory and audit inquiries received by Abrigo’s public business entity clients already reporting results under CECL. These questions are easy to answer in a straightforward manner and with the supporting work products of an engagement, based on Abrigo’s approach. Still, they are topics that financial institutions facing the 2023 deadline should keep in mind as they select tools and resources for implementation.

Clients have been asked to support elections they made around:

  • Segmentation
  • Peer/industry loss data and relevancy
  • Qualitative framework
  • Unfunded commitment liability
  • Securities
  • Sensitivity testing
  • Backtesting and monitoring
  • Individual (impaired) analysis
  • Peer data controls/industry data

Financial institutions adopting CECL for 2023 should consider the above questions, regardless of whether they are outsourcing the CECL calculation, purchasing software to calculate the allowance, or using a tool like the Fed’s Expected Loss Estimator.

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About the Author

Mary Ellen Biery

Senior Strategist & Content Manager
Mary Ellen Biery is Senior Strategist & Content Manager at Abrigo, where she works with advisors and other experts to develop whitepapers, original research, and other resources that help financial institutions drive growth and manage risk. A former equities reporter for Dow Jones Newswires whose work has been published in

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