The Role of Qualitative Factors When Calculating Expected Credit Loss In SAS ACL
The purpose of this blog is to discuss the importance of using qualitative factors if adjustments to the calculated expected loss is necessary. When calculating expected credit loss (ECL), it's crucial to incorporate both quantitative and qualitative factors to ensure a comprehensive and accurate assessment. While quantitative factors rely on historical data and statistical models, qualitative factors provide additional context that can significantly influence the final ECL estimate.
Understanding Qualitative Factors Qualitative factors, often referred to as Q-Factors, are adjustments made to the quantitative ECL model to reflect current and future conditions that may not be captured by historical data alone. These adjustments allow management to incorporate economic, business, and other relevant factors into their credit loss calculations.
Key Qualitative Factors
- Economic and Business Conditions: Changes in the economic environment, such as recessions or booms, can impact borrowers' ability to repay loans. Adjusting for these conditions helps in aligning the ECL with the current economic reality.
- Management's Experience and Judgment: The experience and judgment of lending management play a crucial role in assessing credit risk. Factors such as the depth of management's experience and their ability to navigate challenging conditions are considered.
- Portfolio Characteristics: The nature and volume of the loan portfolio, including the terms of loans and the types of borrowers, can influence credit risk. For instance, a portfolio heavily concentrated in a particular industry may require adjustments based on industry-specific risks.
- Collateral Value: The value of collateral securing loans can fluctuate, affecting the potential recovery in case of default. Adjustments for changes in collateral value ensure that the ECL reflects the true risk of loss.
- Credit Concentrations: The existence and effect of credit concentrations, such as loans to a single borrower or industry, are important qualitative factors. High concentrations can increase risk and necessitate adjustments.
Applying Qualitative Adjustments
To apply qualitative adjustments, analysts typically create a Q-Factor rule set that defines how each qualitative factor will impact the ECL. This rule set is then applied to the quantitative model to adjust the ECL estimates. For example, adjustments might be made based on geographic location, instrument type, or other attribute-based filters. The SAS Allowance for Credit Loss solution provides a sample Q-Factor adjustments rule set. Each factor can be assigned a specific sentiment by the quantitative analyst. There are five customizable options.
- Positive and limited positive impact, which would result in a decrease of ECL.
- No Impact means no change in ECL.
- And a Negative and limited negative impact would increase the ECL.
An analyst could review a rule factor and determine that the economic and business conditions that affect the collectability of the portfolio could have a limited negative impact on the expected credit loss.
Modifying a Qualitative Factor Adjustment Rule Set The process of modifying a qualitative factor adjustment rule set involves several steps:
- Creating a Q-Factor Rule Set: Analysts start by creating a Q-Factor rule set that includes various qualitative factors and their corresponding adjustments. This rule set can be applied to specific groups of instruments using attribute-based filters such as geography or instrument type.
- Applying Weighted Adjustments: Each instrument within the defined filter is assigned a weighted adjustment based on the Q-Factor rule set. This ensures that the ECL reflects both current and future conditions.
- Updating the Rule Set: The rule set can be updated as needed to reflect changes in economic conditions, portfolio characteristics, or other relevant factors. This allows for continuous improvement and accuracy in the ECL calculation.
