A CECL Timeline for Credit Unions
CECL implementation timelines have been altered since the release of this post. Find updated information here.
We are closing in on six months until the SEC filers’ CECL effective date. While credit unions have some additional runway after the November 2018 CECL delay, there is likely less time than expected. CECL is still happening, and in order to be ready to transition in time and with confidence, then it’s time to prepare now. “If you’ve kind of been dragging your feet on this, now is the time,” said Brandon Quinones, Manager of Credit Consulting. “The bottom line is, there are no benefits to starting early because it is no longer early. The time is now to ensure you are ready for January 1, 2022.”
The time to start is now, but if you’re struggling with where to start, consider this timeline.
Getting Started with CECL in 2019
There are two key things that credit unions should consider in 2019: Partnerships and data. Your credit union may be considering a third-party vendor to assist with CECL. While a partnership could be beneficial help in the CECL transition, finding the right vendor is paramount. “You want to avoid black box solutions,” Quinones warns. “CECL is all about the data that you have available and the way that you use that data. So, if you’re just putting it all into a box that’s just spitting out an answer, and you can’t actually go in and identify where those numbers are coming from, then you put yourself in a difficult position to defend your calculations with examiners.”
When completing vendor due diligence, ensure that the solution is transparent and can be easily communicated with examiners. This includes the need for user-driven changes and complexity made easy from a data integration standpoint. Your credit union will need to test and compare different methodologies to determine the right one for CECL, so it’s critical that the third-party solution has the ability to run multiple scenarios concurrently, which is key for your modeling decision-making. Once your credit union selects a vendor, ensure that clear timelines and action plans have been communicated and are in place.
Another key consideration in 2019 is data adequacy. This year, assess how far back your data currently goes. Lack of loan-level data can hamstring an institution’s methodology options. Additionally, consider how accurate your data is – do you trust it? Just because you have a lot of data doesn’t mean that it’s quality data. Perhaps your credit union recently underwent a core migration or lacks quality control on data input processes. Ideally, your credit union will have loan-specific loss data encompassing a full economic cycle (more than 10 years). However, if your credit union finds that it lacks data, has inaccurate data, or struggles with gaps in data, you will likely need to make assumptions based on peer or industry data. Quality and quantity of data are both important for CECL. For a successful CECL transition and to satisfy examiners, assessing your data situation is a critical first step.
CECL Prep for 2020
Next year, your credit union should begin making strategic decisions regarding segmentation and determining potential methodologies. “It’s important to dispel the myth of, ‘I need to just try every option on every loan type,’ and begin understanding your options to implement CECL more quickly,” advises Quinones. While considering various scenarios is generally a good idea, there is no expectation to model every possible permutation. For example, there will be some methodologies, like Vintage Analysis, that can be ruled out quickly where data is simply insufficient, and Discounted Cash Flow or Remaining Life would be a better approach.
Regardless of the methodologies your credit union decides on, documenting your decision is critical. Not every methodology will work or be logical, and there will likely be significant back and forth during CECL committee discussions before your credit union determines the direction it wants to go in. Examiners, however, will lack the context of the discussions and strategies, unless these processes are well-documented. Make sure examiners understand why your credit union ultimately decided on the methodology it chose.
Testing, discussing, and deciding does not happen overnight. Make sure that your credit union has enough time to devote to each of these areas.
CECL in 2021: The Testing Phase
This year is where consistency is key. Each quarter represents a final opportunity to refine your CECL model prior to the 2022 adoption, so it’s critical to look for consistency in application and in results. During this testing phase, ask yourself the following questions:
- Do previous methodologies that weren’t previously available make more sense to use now that the credit union has built more data?
- Do forecast components work as expected? Has the outlook changed and produced increased or decreased expected losses?
- Do you understand why you’re getting the results? Can you explain those results to an examiner?
- Can you reasonably project your Day 1 impact on retained earnings?
This is a critical period for credit unions to make sure that they can justify their model(s). Your credit union will still likely be making changes to the model and may even have to go in a different direction due to granular details that weren’t available earlier on in the process. Leveraging a partnership for guidance and second opinions can be especially useful for this step in the process. Regardless, this is a critical year to get comfortable with your model and your results.
Yes, credit unions extra have extra time to prepare for CECL due to the delay. However, this is not a ticket to procrastinate. Your credit union likely has less data than expected, and the additional time will probably feel like less than you think. The key is to get started now. By starting now, your institution will have enough time to consider its options, get its processes in place, test various models, monitor for consistency, and ultimately transition successfully to CECL. Don’t bank on a CECL delay, and don’t underestimate the importance of an abundance of quality data.