Much goes on before conducting any analysis of the ALLL. Many financial institutions must first gather and ready their data, which consists of pulling it from their core or other departments, validating balances, and manipulating it into a usable format, among other things. Some institutions choose to deploy an automated solution which integrates with the core and bypasses the often time-consuming process of gathering and readying the data.
Another component of preparation which often goes overlooked is the establishment of a succession plan. Regulators require a repeatable methodology when determining the ALLL, so as a part of preparation, financial institutions should consider implementing a contingency plan in the case that their usual process gets disrupted.
Preparing for the ALLL
Here’s a Rundown of the CECL Methodologies Available to Financial Institutions
An important step in CECL implementation is selecting what methodology or methodologies the institution will use for estimating credit losses. For a quick glance at the seven methodologies available, and to get a better sense of how they compare, check out a complimentary infographic, “CECL Methodologies: Pros and Cons for Your Loan Pools.”
Segmenting the Loan Portfolio
he extent of segmentation recommended for a bank or credit union depends on the size of the institution and the nature, scope and risk of its lending activities (new products, significant changes to underwriting, origination in new markets, etc.). Guidance suggests the loan portfolio should be segmented into homogenous pools based on similar attributes, “stratifying the portfolio into segments that have common risk characteristics or sensitivities.” But, be sure that the segmentation reflects the segmentation risk within your institution’s portfolio. Segmentation strategy should be tailored to each institution to address its specific circumstances and needs.
CECL – Data and Methodology
The Financial Accounting Standards Board's (FASB) Accounting Standards Update (ASU) 326 provides the guidance for estimating allowances for credit losses, as the current expected credit losses methodology (CECL) will be applied. The allowance will be reported as a valuation account, or the difference between the financial assets’ amortized cost basis and the net amount expected to be collected. Two common questions that bankers ask about FASB's CECL model are: 1) What methodologies make the most sense for CECL? 2) What are the data requirements for my institution?
The two D’s of the ALLL: Data and documentation
One critical component of the ALLL is data-gathering and documentation. The reserve is more than simply a calculation of funds, and a lack of or insufficient data and/or documentation is often a sore spot for many banks and credit unions. Whether an institution assesses the ALLL monthly or quarterly, collecting portfolio-level and loan-level data is often the most time-consuming aspect of the process, but a crucial one.
Institutions should begin to gather the necessary documentation, both internal and external, before commencement of the ALLL calculation. Many sources of information, such as appraisal values or cash flow schedules, may require updating prior to the time of the calculation. As such, institutions should be mindful of documentation requirements as they begin to plan for their ALLL calculation.
Institutions must consider succession planning when they prepare for the ALLL. If an institution has one person responsible for the process, it may not be repeatable should that person step down. The process should be well-documented from start to finish, and there are certainly other ways to ensure the continued soundness of a bank or credit union’s ALLL process by “institutionalizing” the calculation.
It is vital to ensure data integrity before performing the actual calculation. Institutions must validate that the numbers they have on their books are up-to-date in order to derive an accurate output on the ALLL calculation.
Before an institution can begin on the ALLL calculation, it must first gather and prepare the necessary data. This may require collecting data from disparate sources and converting the data from an illegible core output to a ready-to-use format. If an institution uses an automated solution that integrates with its core, this step in the process may not be necessary.