Adjusting Your Historical Data Under CECL – Qualitative Factors Run Amuck


Quantitative and Qualitative Factors Under CECL

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.

Q-Factor Differences Between CECL and ILM

Both CECL and ILM require the consideration of Q-Factors when adjusting historical loss data. However, the functional application is different:

  • ILM: Q-Factors would adjust for risks that have already occurred or that are occurring, and that are not included in the historical data selected.
  • CECL: Q-Factors are applied when the current loan pool’s data available for calculation contains risks that are not inherent in the longer contractual term historical dataset utilized as the base of the CECL calculation.

CECL Standard Components that Should be Considered to Adjust Historical Data

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.

Steps for Adjusting Historical Data

To adjust data for qualitative factors under CECL, financial institutions can follow the steps below. Documentation is key to the entire process!

  1. Identify the relevant qualitative factors: Understanding the changes over time that affected the credit risk, prepayment, Probability of Default (PD), Loss Given Default (LGD) risk of each pool, based on the following key elements:
    • Asset Specific Risks
    • Portfolio Mix
    • Contractual Term of the Portfolio
    • Economic Environment
    • Concentration Risk Changes
  2. Assess and quantify the impact of these factors on credit risk: Consider how each of these factors may affect the likelihood of default or the timing of expected credit losses, as well as prepayments, PD, and LGD. This step can involve evaluating the potential impact of each factor on the credit risk of the loan or pool of loans, and determining which factors are most likely to have a significant impact.
  3. Collect and analyze data on the identified qualitative factors: Gather historical data from ARCSys’ or your own data warehouse, historical analytics, industry trends, economic indicators, the pool’s financial performance, and industry reports.
  4. Incorporate the data into the CECL model: Quantify the risk of the factors: Consider how the data on qualitative and quantitative factors should be incorporated into their CECL model, determine and document the potential impact on credit risk. This step should involve adjusting the historical loss data based on the quantification of the risk factors.
  5. Monitor and update the data annually: As economic and industry conditions can change over time, financial institutions should periodically review and update the data used in the model, as well as changes to the current dataset that may be different from the historical data selected for the model.

Possible Reasons to Make an Adjustment

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:

  • Current changes in credit and prepayment risk in the active loan dataset that are not completely reflected within the historical data available
  • Current underwriting and mix of active loan dataset that are not completely reflected within the historical data available
  • Current terms of active loans within the dataset that are not completely reflected within the historical data available
  • Current and forecasted economic changes, such as increases or decreases in unemployment rates, property values, commodity values, or other factors that are associated with credit risk on the financial asset or in the group of financial assets that are not completely reflected within the historical data available
  • Other situational changes that are not completely reflected within the historical data available, such as borrower’s creditworthiness, changes in lending strategies and underwriting practices, and the current and forecasted direction of the economic and business environment

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:

  • Adjusted to reflect the current economic conditions
  • Updated annually to ensure that the estimates of expected credit losses remain accurate
  • Evaluated by product and geography, as different products and regions may have different characteristics that can affect credit risk and expected credit losses

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.

Factors to Consider

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:

  • Asset-specific risk characteristics
  • The entity’s lending policies and procedures
  • Portfolio mix
  • The remaining payment terms of the financial asset(s)
  • The remaining time to maturity and the timing and extent of prepayments on the financial asset(s)
  • The quality of the entity’s credit review system and the experience, ability, and depth of the entity’s management, lending staff, and other relevant staff
  • Environment to which the entity has exposure
  • Economic, business, and developmental changes

Utilizing Third-Party Data

Third party data is data that is generated by sources outside of an entity used to build historical loss histories, such as:

  • Financial institution regulatory agencies
  • Credit rating agencies
  • Market data providers
  • External credit databases

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.

Support From ARCSys:

  • Our analytics can help support risk changes through time
  • The annual statistical update helps document external and internal risks
  • Our five modeling options allow institutions to customize individual pool models

Contact ARCSys to discuss your CECL model and historical data adjustments!


About The Author

Michael Umscheid - President and CEO

Michael Umscheid

President & CEO