Regardless of whether financial institutions choose to stay in-house or outsource their CECL solution, they have four important considerations to make.

Takeaway 1
Institutions complying with CECL in 2023 must make their build or buy decision quickly.

Takeaway 2
Institutions will need to ensure their CECL solution has adequate data and can easily assess different methodologies.

Takeaway 3
CECL solutions should support “reasonable and supportable” forecasts with transparency and auditability.

Preparing for CECL

Time is Ticking for 2023 Adopters

Time is ticking for institutions that must comply with the current expected credit loss (CECL) by January 2023. SEC registrants that have already adopted CECL and other experts have repeatedly advised banks and credit unions to immediately begin their CECL implementation. Despite the warnings and the impending deadline, many financial institutions are at a standing start. During Abrigo’s recent CECL Kickstart, an event aimed at those institutions who are only now getting to work on this major accounting change, attendees were polled on their current implementation status. Overwhelmingly, most respondents (42.1%) reported that they were at a standing start. Meanwhile, less than 3% of attendees reported being finished with implementation.

Some financial institutions may be at a standstill as they build their own CECL model using spreadsheets or buy a solution through a third-party vendor. CECL requires more inputs, assumptions, analysis, and documentation, and institutions must decide how to efficiently – and accurately – take each piece into account. This can require an extensive amount of resources that many financial institutions may not have, making the option to automate and modernize the process significantly more attractive for busy banks and credit unions. Regardless of whether financial institutions choose to stay in-house or outsource their CECL solution, they have four important considerations:

Making Key Decisions

Ensuring Adequate Data and Selecting the Right Methodology

1. Data
An institution’s ability to calculate lifetime loss rates is predicated on the accuracy and availability of loan-level data. But having large amounts of data isn’t always enough. Many institutions lack the material loss data needed under CECL. Because of the important role data plays in calculating CECL, some financial institutions will find that partnering with a vendor can significantly help streamline the data-gathering process and identify and fill gaps in their data. If an institution considers leveraging a third party to assist with data, understand what the vendor offers for data archive, data architecture, and data adequacy services and how that compares to in-house capabilities. Compare the degree of automation and flexibility available for each option.

2. Methodologies
The CECL model is not prescriptive, meaning banks and credit unions can determine the best methodology or methodologies to use based on their loan portfolio. With seven different methodologies to choose from, some financial institutions have “analysis paralysis” trying to determine the right approach for their institution.

Once an institution determines the methodology best suited for its portfolio, it should consider the costs for developing and maintaining the methodology in-house compared to a vendor’s costs. How automated and flexible is the solution? Can the institution easily calculate, evaluate, and change methodologies? Finally, when weighing the pros and cons of building or buying a CECL solution, banks and credit unions should consider the advisory assistance and support they may need and what resources will be available to them as they work through their methodologies.

Transparency and Auditability

Forecasting and Documentation are Key to a Successful CECL Solution

3. Forecasting and Adjustments
Unlike the incurred loss model, the CECL model is forward-looking, estimating loans’ lifetime losses using reasonable and supportable forecasts. For periods beyond an established forecast, reversion to average historical experience is required. The subjective factors of CECL can make the new standard more daunting – another reason institutions may be at a “standing start.” Qualitative adjustments have played an important role in calculating the allowance under the incurred loss method, and these adjustments will continue to be significant under the CECL model. Models should allow for quick inclusion and exclusion of all observable analysis periods and provide forecasting intelligence, support, and application. Ideally, management should be able to evaluate all observed loss rates, make any documented exclusion/inclusion decisions, including forecasted conditions, and see those decisions reflected in the estimated reserve level.