Using an Attribution Template to Support Calculating Expected Credit Loss

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In this blog we will be discussing how financial attributes impact your expected credit loss. When it comes to calculating expected credit loss (ECL), an attribution template plays a crucial role in ensuring accuracy and efficiency. An attribution template defines the objects used in an attribution analysis, including input data definitions, output variables, and attribution factors. This structured approach allows for a comprehensive analysis of the factors influencing ECL. Attribution analysis enables you to gain an understanding of what factors contributed to your period-over-period changes in expected credit losses. For example, if you estimated the expected credit loss last quarter and this quarter, you can use attribution analysis to determine how changes to the portfolio, models, and scenarios have individually contributed to the overall change in the expected credit loss. You could then answer the following:

• How much has the expected credit loss changed due to newly acquired positions?
• What was the impact of the changes to our projected GDP growth on ECL?
• How have the  new credit models impacted our ECL estimates?

The attribution analysis makes incremental changes to the analysis performed in a previous period and records the values of user-specified output variables with each change. Specifically, the previous period's analysis is altered by replacing components with components from the analysis performed in the current period. Attribution analysis enables customers to:

• create waterfall reports detailing how changes in an analysis run's inputs have impacted the outputs.
• create multiple Attribution Templates to streamline the creation of these reports.
• quickly and easily run an attribution analysis using an Attribution Template, as part of a Cycle or as a separate Analysis Run.
• compare any two runs of the same analysis model, whether the underlying models are MIP models, Python models, R models, or SAS scripts.

Understanding Attribution Templates An attribution template is essentially a blueprint that outlines the components required for an attribution analysis. It includes:

• Input Data Definition: This specifies the data sets and variables that will be used in the analysis. It ensures that all relevant data is included and properly formatted for the analysis.
• Output Variables: These are the variables that will be tracked and analyzed during the attribution process. They represent the key metrics that will be used to assess changes in ECL.
• Attribution Factors: These are the specific factors that will be analyzed to determine their impact on ECL. The attribution template typically includes multiple factors, such as expired positions, portfolio aging, new originations, and reclassifications.

  The Role of Attribution Factors Attribution factors are the core elements of the attribution analysis. They represent the various components that can influence changes in ECL. For example, the template might include factors such as:

• Expired Positions: Analyzing the impact of expired positions from one period to the next.
• Portfolio Aging: Assessing how the aging of the portfolio affects ECL.
• New Originations: Evaluating the impact of new loan originations on ECL.
• Reclassifications: Understanding how reclassifications within the portfolio influence ECL.

Conducting an Attribution Analysis The attribution analysis is conducted by running the analysis repeatedly, with each run focusing on a different attribution factor. The order in which the factors are analyzed can affect the results, as each factor's impact is assessed in the context of the others. For example, the analysis might first evaluate the impact of expired positions, then move on to portfolio aging, and so on.

Creating and Modifying an Attribution Template Creating an attribution template involves several steps:

1. Define Input Data: Specify the data sets and variables that will be used in the analysis.
2. Select Output Variables: Identify the key metrics that will be tracked and analyzed.
3. Choose Attribution Factors: Determine the specific factors that will be included in the analysis.

Modifying an attribution template is an ongoing process. As new data becomes available or as the business environment changes, the template can be updated to reflect these changes. This ensures that the ECL calculations remain accurate and relevant. Conclusion An attribution template is a powerful tool for supporting the calculation of expected credit loss. By providing a structured framework for analyzing the factors that influence ECL, it helps organizations make more informed decisions and better anticipate potential losses. Incorporating both quantitative and qualitative factors into the attribution analysis ensures a comprehensive and realistic assessment of credit risk.