SPONSORED CONTENT FROM NCINO
By Scott Blakeslee, senior product manager, nCino and Nathan King, manager of portfolio analytics delivery, nCino
While financial institutions navigate the current economic storm brought on by COVID-19, an inevitable question arises: How will the current crisis impact my allowance for credit losses? Based on initial reports, many institutions are already building larger credit reserves in response to sharp increases in unemployment and the looming threat of a protracted recession.
We’re already seeing noteworthy examples of what COVID-19-induced economic uncertainty looks like, in the form of earnings reports announced recently by a number of banks. For example, JPMorgan Chase reported a $6.8 billion increase in their provision for credit losses for the quarter ending March 31, of which $4.4 billion was attributed to consumer finance, including a large portion of that marked for the bank’s credit card division. “In the first quarter, the underlying results of the company were extremely good, however given the likelihood of a fairly severe recession, it was necessary to build credit reserves of $6.8B,” CEO Jamie Dimon said in their recent earnings release.
CECL Implications
It’s important to note that many of the largest U.S. banks were required to adopt CECL as of January 1, 2020, which resulted in additional adjustments to their provision for credit losses related to their adoption of the new accounting standard. Due to the effects of COVID-19, the CARES Act passed by Congress included a temporary CECL delay, and financial regulators recently granted a reprieve to institutions who were required to adopt CECL in 2020 by allowing them to mitigate the cumulative regulatory capital effects for up to two years. All other financial institutions will be required to adopt CECL as of January 2023 and will continue using the incurred loss methodology to account for loan and lease losses until then.
The timing and impact of CECL has complicated concerns associated with COVID-19 and its potential impact to estimates for expected credit losses. Under CECL, institutions must account for historical experience, current conditions, and reasonable and supportable economic forecasts, all of which are key provisions of the new accounting standard.
Helpful Strategies
Regardless of whether your institution has already adopted CECL or is currently estimating its allowance under the incurred (ALLL) method, the task of appropriately reflecting the effects of COVID-19 into the estimate of your allowance will be a challenging one. The purpose of this article is to provide helpful strategies that will enable you to effectively assess and manage the impact of the current crisis on your allowance for credit losses.
As financial institutions grapple with the many complexities associated with current economic conditions, there are a number of strategies they can employ to effectively assess and manage COVID-19’s impact on their allowance. Best practices include:
- Understanding key trends in your data
- Conducting scenario analysis
- Updating economic forecast models
- Effective use of qualitative and environmental (Q&E) factor adjustments
The following sections will cover each of these areas in greater detail with the objective of assisting financial institutions in implementing a successful allowance strategy within the context of these unique circumstances.
Understand Key Trends in Your Data
One of the biggest challenges faced by institutions in the current crisis is being able to interpret data while the pandemic and its resulting effects are changing the economic landscape in real time. Most institutions will not have historical data to replicate what is occurring now. For example, unemployment projections of 15 percent haven’t been seen since the 1930s. It will take some time before institutions are able to obtain sufficient data to accurately assess economic factors, delinquency and subsequent default rates, charge-offs, and recoveries. For this reason, it’s essential that institutions understand trends in their current data to help them overcome any gaps in historical loss information. We recommend focusing on the following key data trends:
- Trends in economic data
- Trends in volumes
- Trends in credit utilization
- Trends in credit quality indicators
- Trends in delinquency
- Trends in charge-offs and recoveries
Data quality remains an indispensable requirement for accurately estimating your allowance for credit losses and is now more important than ever in the current COVID-19 environment. Effective analysis and thorough documentation of current data trends while continuing to gather relevant historical information will be a critical part of every institution’s data readiness strategy. It thus behooves every institution to continue working on their data quality during the current crisis to ensure that they can support the assumptions and data requirements associated with the methods and scenarios they select to calculate their allowance.
Conduct Scenario Analysis
While many institutions will experience similar impacts due to general economic uncertainty created by COVID-19, exactly what those impacts look and feel like will be different for each institution. These differences could be based on unique regional qualitative and environmental factors, portfolio mix, underwriting, and how an institution monitors portfolio credit quality, to name a few. Each institution will need to make appropriate assumptions based on the unique elements related to their business and conduct scenario analysis on their institution’s performance based on short-term, medium-term and long-term trends. Institutions should clearly document scenario analysis based on the appropriateness of the assumptions used and the reasonableness of the underlying data.
Whether your institution is using CECL or the incurred method for calculating its allowance, selecting an appropriate loss rate method is an important part of scenario analysis. Each method has different data requirements to calculate a reserve amount. As financial institutions assess various methods, their main objective should be to find the most appropriate method (or methods) for their institution. For example, loss rate methods may not have charge-off data representative of the current crisis. This will require institutions to make significant Q&E adjustments or use a probability of default model (for institutions who have adopted CECL). Applying a thorough scenario analysis using an appropriate loss rate method, combined with effective use of Q&E adjustments, will be crucial to estimating the most appropriate allowance for credit losses in the midst of current economic conditions.
Update Economic Forecast Models
Perhaps the most challenging part of estimating the allowance for credit losses in the current environment is how to effectively forecast macroeconomic conditions. For institutions implementing CECL, the reasonable and supportable forecast provision of the new accounting standard presents unique challenges.
First, there’s the uncertainty around the economic forecasts themselves—we don’t know what the eventual economic recovery will look like. Will unemployment numbers remain low for a protracted period of time, or will they bounce back to pre-crisis levels relatively quickly? Second, is the fact that the conditions we’re experiencing are unprecedented. Unemployment is reaching heights we haven’t seen since the Great Depression and most financial institutions do not have data going back that far. If institutions don’t have relevant historical data incorporated into their models, they will struggle to accurately predict future losses in the current economic environment.
Furthermore, the default deferrals that most lenders will implement will heavily impact the timing of realized losses. For example, we can expect borrowers to miss payments consistent with 15 percent unemployment; however, if there’s an expectation that those borrowers will be reemployed quickly, lenders may correctly hold off on default and avoid it altogether even though the loan would have defaulted in a more typical recession. Thus, default behavior may not be consistent with 15 percent unemployment.
In the near term, institutions will need to update their models and assumptions to incorporate the impact of COVID-19 in their economic forecasts as they would for other catastrophic events. Given that many institutions will lack historical data that correlates to an event like COVID-19, they will need to use the best information possible, including regression analysis and correlations with their data, to form reasonable and supportable forecasts.
Effective Use of Qualitative and Environmental Factor Adjustments
Management can use qualitative and environmental (Q&E) factor adjustments to reflect current economic conditions not already reflected in the historical loss information. The challenge that institutions are presently facing is that they’re still looking at the economic impact of COVID-19 in real time and it will take some time to properly analyze the data and its impact on their portfolio. The purpose of Q&E factors is to provide institutions with the ability to directly adjust the level of recognized credit losses by considering variables that are not accounted for in the base loss rate calculation.
Effective use of Q&E factors will require management to determine how their allowance should be adjusted, based on factors not reflected in the loss rate method selected. Relevant data will be required to support any assumptions made. As noted in a previous section of this article, understanding key trends in your data will prove to be an essential part of management’s analysis when making Q&E adjustments. The following considerations should be made when making Q&E factor adjustments.
- Identify what Q&E factors should be included as direct adjustments to loss rates
- Calculate the appropriate adjustment amount of each factor on a class/segmentation level
- Document assumptions for each factor adjustment using relevant data-driven analysis
Using data-driven analysis to calculate Q&E factor adjustments is difficult. An outside vendor with a Portfolio Analytics platform can assist management in this effort by providing you with empirical data to adequately justify each Q&E factor adjustment. The amount of each Q&E factor adjustment will need to be calculated on a class/segmentation level. It’s important to note that FASB has not prescribed an established way to calculate Q&E adjustments, and while there is a fair amount of flexibility in which Q&E factors are used, it is critical that Q&E adjustments are fully documented and supported by empirical data.
Conclusion
COVID-19 has created a near perfect storm of economic uncertainty and complexity for financial institutions grappling with how best to reflect the pandemic’s impact on their allowance for credit losses. In this article we have described several best practices which institutions can begin implementing immediately to assist them during the current crisis. These best practices include understanding key trends in your data, conducting scenario analysis, updating economic forecast models, and effective use of qualitative and environmental factor adjustments. Employing these best practices will enable financial institutions to move forward with greater confidence.
Interested in learning more? Listen to the authors, Scott Blakeslee and Nathan King, present on nCino’s complimentary webinar: COVID-19 And Its Impact on Your Allowance.
