Trusted CECL & ALM Specialists
Trusted CECL & ALM Specialists
Under CECL, companies are required to consider both quantitative and qualitative factors when adjusting historical data and estimating expected credit losses. The purpose of considering both quantitative and qualitative factors (Q-Factors) under CECL is to ensure that companies take a more holistic view of credit risk when estimating expected credit losses, rather than relying solely on historical data.
Quantitative factors are objective and measurable data points that are used to estimate expected credit losses. These factors may include changes in credit risk, such as Loan to Value (LTV), FICO, Debt Service Coverage Ratio (DSCR), economic conditions that are not already inherent in the historical data, and the contractual term of the loan.
Qualitative factors are subjective and harder to quantify data points that are used to estimate. The lender’s knowledge of the pool’s industries is important. If the lender has a strong understanding of the pool’s businesses and industries, they may be better able to assess the overall ability to pay and the risk of default.
Because identifying these factors is more difficult at the loan pool level, instead of the borrow level, documentation and support for these factors tends to be lacking in substance. Ultimately, both qualitative and quantitative factors, after analysis, are utilized as a quantitative number adjustment to the historical data.
Both CECL and ILM require the consideration of Q-Factors when adjusting historical loss data. However, the functional application is different:
There are several components of the CECL standard that should be considered to adjust historical data when estimating expected credit losses.
| CECL Standard Statement: | Interpretation: |
|---|---|
| Consider available information relevant to assessing the collectability of cash flows. This information may include internal information, external information, or a combination of both relating to past events, current conditions, and reasonable and supportable forecasts. CECL Standard Section: 326-20-30-7 |
Since cash flows include prepayments, net charge-offs, and default statistics, consider all inputs into their CECL model to determine if historical data supporting those specific inputs needs to be adjusted to represent the current loan data risks. |
| Consider relevant qualitative and quantitative factors that relate to the environment in which the entity operates and are specific to the borrower(s). CECL Standard Section: 326-20-30-7 |
The factors an institution considers in adjusting their historical information should directly relate to the credit and prepayment risk of the loans within the pool. |
| Historical credit loss experience of financial assets with similar risk characteristics generally provides a basis for an entity’s assessment of expected credit losses. CECL Standard Section: 326-20-30-8 |
Utilizing the loss experience by relevant credit risk is the best data to use if available. However, an institution can use third party data, but they need to determine how the external data fits to the historical or current data set. |
| Historical loss information can be internal or external historical loss information (or a combination of both). CECL Standard Section: 326-20-30-8 |
An institution can use external data, however they should have a process to determine how the external data relates to their risk pools. If the data is in a set pool structure such as call report data sets, institutions should determine if their historical risk profile matches the external profile. |
| Consider adjustments to historical loss information for differences in current asset specific risk characteristics, such as differences in underwriting standards, portfolio mix, or asset term within a pool at the reporting date or when an entity’s historical loss information is not reflective of the contractual term of the financial asset or pool of financial assets. CECL Standard Section: 326-20-30-8 |
The underlying purpose of Q-Factors is to adjust the historical data selected for the model inputs to match the risk inherent in the current risk pool in which the model will be applied. For example, if the historical data includes loans where the maximum loan to value (LTV) at origination was 80% but the current pool includes underwriting changes where loans are currently originated with LTVs up to 100%. This institution should consider whether the increase in LTVs in the current dataset would require an increase in the historical loss percentage of the pool. |
| An entity shall not rely solely on past events to estimate expected credit losses. When an entity uses historical loss information, it shall consider the need to adjust historical information to reflect the extent to which management expects current conditions and reasonable and supportable forecasts to differ from the conditions that existed for the period over which historical information was evaluated. The adjustments to historical loss information may be qualitative in nature and should reflect changes related to relevant data. CECL Standard Section: 326-20-30-9 |
This reinforces that historical information needs to be adjusted for current conditions that are not inherent in the risk associated with the historical data utilized in the model. |
| Because historical experience may not fully reflect an entity’s expectations about the future, management should adjust historical loss information, as necessary, to reflect the current conditions and reasonable and supportable forecasts not already reflected in the historical loss information. In making this determination, management should consider characteristics of the financial assets that are relevant in the circumstances. CECL Standard Section: 326-20-55-4 |
FASB reinforces that “historical data” should be adjusted for current conditions. This may include differences in historical data and current data to be modeled. In addition, this also refers to characters of financial assets, which includes net charge-offs, prepayments, and default statistics. |
To adjust data for qualitative factors under CECL, financial institutions can follow the steps below. Documentation is key to the entire process!
There are several potential events that may cause an institution to make an adjustment to their historical data under CECL. In general, any event that may affect the credit risk of a financial instrument may cause an institution to make an adjustment. This could include:
Considering Charge-Offs for Adjustments
When adjusting historical datasets for CECL, all amortized cost basis elements, such as charge-offs and write-offs of deferred fees and costs, premiums, and interest should be included as part of the historical loss data. The combined write-offs and charge-offs should be:
Considering Prepayments for Adjustments
One important concept to be incorporated into the model is the fact that prepayments have life cycles, like charge-offs, for each pool. Prepayment rates are not consistent through a contractual term and tend to increase over the first 40% of the term and decrease over the remaining term. Understanding prepayment cycles is extremely important to adjusting prepayments for both the forecast period and the reversion period.
If a financial instrument has a high rate of prepayments, it may indicate that the borrowers are financially stronger and less likely to default. In this case, an entity may need to adjust their historical data to reflect the lower credit risk of the financial instrument. On the other hand, if a financial instrument has a low rate of prepayments, it may indicate that the borrowers are financially weaker and more likely to default. In this case, an entity may need to adjust their historical data to reflect the higher credit risk of the financial instrument.
Considering Probability of Default for Adjustments
If utilizing a Probability of Default (PD) model, an institution should consider historical PD changes throughout the contractual term of the loan pools. ARCSys recommends utilizing a vintage PD analysis which has the number or dollars of loans past due as the numerator and the loans remaining in the vintage pool as the denominator.
Considering Loss Given Default for Adjustments
If utilizing a PD model, institutions must consider Loss Given Default (LGD). To estimate LGD, institutions should calculate the gross charge-off to the balance before charge-off at the charge-off date. This should include the write-offs previously discussed. This percentage is then reduced through time as recoveries are received.
There are several factors that an entity may consider when deciding whether to adjust their historical data for CECL. Some examples of these factors include changes in economic conditions throughout the historical data and forecasted economic conditions, as well as changes in underwriting terms, and other relevant risk categories. Not all of these may be relevant to every situation and other factors not on the list may be relevant. Important factors to consider may include:
Third party data is data that is generated by sources outside of an entity used to build historical loss histories, such as:
An institution needs to document the peer group data utilized and the reasons why that peer group was selected and why the peer group’s loss history is reflective of the institution’s loss history.
The institution can assess changes in their own loss history to determine whether or not the third-party dataset needs to be adjusted based on the institution’s differences between the inherent risk in their current dataset and the third-party historical data utilized.
It is recommended that an institution develops an assessment worksheet to demonstrate the correlation between their historical data and the third-party data set. They should also write a memo on their underwriting changes through time to associate with possible differences in loss rates.
Contact ARCSys to discuss your CECL model and historical data adjustments!
