It turns out that research by Policygenius finds people are 10x more likely to break up with someone if they think their partner is bad with their finances. Of note, about 20% of people say they think their partner is financially irresponsible.
For financially responsible bankers we note that the AICPA released its 63-page CECL practice aid recently, so we share a few key messages today. This is the second BID in a series of three. You can find the first one here called, Management's Role When Outsourcing CECL.
CECL loss estimates will need a lot more data so a new perspective is needed. It is important to have a different perspective when gathering data for CECL. More of the same won't work, so using additional data will require careful thinking around why it is used and its source. It really matters to auditors and regulators, because CECL affects your financial statements, after all. It is important for bankers to be comfortable that the data hasn't been manipulated, know that it has been audited and understand the risks and limitations of using it. Get all stakeholders involved so each unit brings their areas of expertise and understanding to the table early in the process.
Qualitative factors shift. While the Q factors used for CECL are the same as previously used, the adjustments will likely be different. Our advice is to review yours without any reference or linkage to your current loss estimates. Look at them with fresh eyes, since the regulatory expectations are different with CECL and the standards are higher. Simply put, your new Q factor assumptions should not be anchored to your old Q factor assumptions. Documentation here needs to be highly detailed and thoroughly transparent.
CECL loss estimates should not be anchored to the old ways. As bankers work through data, assumptions and loss history, begin with a review of your current loss estimates. As you do, know it is also important not to give those estimates too much weight as things change under CECL.
Challenge the familiar. You may gravitate to a loss reserve that is familiar, because that is what humans do. But, be sure to make appropriate adjustments under CECL. Once these adjustments are made, information to validate these adjustments is needed. Bias can be found when first looking at the most relevant data to use for a reasonable and supportable forecast, so be careful. Information is readily available, so while this may be a comfortable approach to backfill, it is not the best way to approach CECL (assumptions used may not be the same).
Similarly, it is not unusual to avoid seeking out new information or even new methods due to a lower level of familiarity with them. Break this bias to avoid learning new things because both auditors and examiners will challenge assumptions and ask about contradictory ones.
If you find you need some assistance with CECL, we can help. Our CECL FITTM solution provides you with an interactive dashboard along with experts to help, as needed. Contact us today at email@example.com.