Welcome to the CECL time warp. When the FASB announced its CECL requirement in 2016, it seemed like there was an endless amount of time available to meet CECL 2021 implementation deadlines. Now, in 2019, the road to implementation seems like a speedy downhill drive in a car with squeaky brakes – except there are, in fact, ways to check the brakes along the way.
The Financial Accounting Standards Board (FASB) did not prescribe a specific approach when it required the Current Expect Credit Loss (CECL) standard, leaving it up to financial institutions to determine the best path forward. Since Allowance for Loan and Lease Losses (ALLL) is no longer an apples-to-apples comparison, how will this impact a financial institution’s ability to compare itself to its peers?
If you have followed the news about MoviePass and its recent financial downfall, you may have read about declining stock prices and angry customers, and made a mental note about what not to do in business. However, there is one critical but less obvious lesson to learn from the MoviePass pandemonium – and it is one that just might keep your financial institution from going down the wrong CECL road.
If credit unions want to stay on track for implementing the Current Expected Credit Loss (CECL) standard, they need to have already completed several milestone tasks and to complete several more by the end of the year. Credit unions also need to be sure they have allocated sufficient time to complete the tasks that need to be finished between 2019 and 2022.
After many quiet years, merger and acquisition (M&A) activity at banks has been on the rise in 2018, and several favorable trends will likely sustain the momentum through the remainder of the year. By the middle of 2019, as banks evaluate acquisition opportunities, they likely will add a new component to their customary due diligence: an exploration and understanding of the target company’s Current Expected Credit Loss (CECL) methodology.
A few years ago, “big data” emerged as a buzzword across many industries, including financial services. The concept and push was to capture all-encompassing information. While that talk has wound down, we’re now hearing an uptick in discussions about artificial intelligence and machine learning, interpreted as using big data to improve decision making.
At banking organizations, financial model validations can be simply viewed as a necessary task on a checklist for following regulatory guidance. Some institutions also believe that the quality of a model validation is less important when the institution or business line is successful and when local, regional and national economies are all thriving.