Preparing for new credit loss standards
With the recent Financial Accounting Standards Board vote to proceed and the final Accounting Standards Update now published, the Current Expected Credit Loss (CECL) standards remain top of mind for the C-suite in financial institutions. Even though the official adoption deadline has been pushed back to after December 2019, financial institutions should take actions today to help prepare for the new rules. Data is a great place to start.
The rule change will require financial institutions to alter the way they approach their end-to-end reserving process, replacing the current incurred loss approach with a lifetime expected loss estimate – essentially forecasting cash flows on every loan. The Comptroller of the Currency, Thomas Curry, has gone on record stating he expects a 30% to 50% increase in banks’ allowance for loan and lease losses (ALLL), otherwise known as reserves, as a result of the CECL model. As Curry noted, some increases can fluctuate depending on an institution’s loan portfolio or economic environment at the time. To comply with this change, financial institutions will need a more holistic understanding of their data, as well as a strategy to manage the new process and explain changes in the reserve over time.
Aligning Data and Systems
Looking at this as purely an “accounting” issue may result in the implementation of point solutions that further fragment the data required to effectively manage risk. Better alignment of the data and systems to support strategic business management, including the credit risk management function, is going to be required to comply with the new standards.
Here are some points to consider when implementing a data strategy for CECL:
First, the regulatory design is such that better integrating the data and perspectives of accounting, the Asset Liability Committee, risk-based capital, budgeting, strategy, liquidity and net interest margin is imperative for success.
Second, CECL regulation requires intensive historical data management and storage capabilities. It is likely that to comply with the new CECL mandate effectively, organizations will need to do a better job of marrying data from their financial accounting systems with data from their credit risk management systems and to analyze that integrated data in a more meaningful way.
Third, more data will be required that currently exists in many of today’s incurred loss models. This new data is needed to create a clear loss picture at the portfolio level. At the loan level, balances as well as segmentation (or pooling of data), risk and vintage information will also be critical.
Going forward, financial institutions will have to understand and incorporate historical loss rates into the reserving process. They also must create forward-looking risk and loss assumptions based on a consistent methodology and, in a best-case scenario, comprehensive data. Assumptions must be reasonable and supportable, and organizations must understand and be able to discuss past correlations and their ramifications on financial statements. Both the accounting and credit risk views will be necessary to devise a strategy that effectively calculates the reserve and explains changes over time. Without this capability, it will be nearly impossible to remain current and compliant.
While institutions are struggling to fully understand the ramification of these changes, they can begin what will ultimately be a dramatic alteration of their current capital reserve calculation and risk management strategies. One of the first steps will be to develop the capability to support a common, consistent enterprise performance management approach that connects financial management, financial performance management and financial and credit risk management data.
CECL represents the need to better integrate accounting and risk management, as well as access an expanded dataset with which to calculate credit reserves. Financial institutions will need not only a more complete view of their portfolios but a data strategy to manage the new process effectively. One possible strategy is pooling group loans into similar sub groups, such as 30-year fixed mortgages with a given loan-to-value range and given range of credit scores. Then, financial institutions should see how the default rate for that account changes as one or more economic factors change. By creating data strategies such as pooling, institutions will be able to better monitor and control the adjustments to ALLL and capital.