Key Considerations for Debt Funds with Loans After CECL Implementation

Beginning in 2023, the Financial Accounting Standards Board (FASB) adopted Accounting Standards Update 2016-13, guidance for current expected credit losses (CECL). For all debt funds, excluding those using the fair value model for investment companies, FASB Accounting Standards Codification (ASC) 946, Financial Services – Investment Companies, CECL has changed how debt funds estimate credit losses for its portfolio loans. To help ease perceived complexity of the CECL analysis in the second year of implementation, here are some models that are straight-forward in their execution:

1. Historical Loss Rate Model

This model is straightforward and relies on historical data to estimate future credit losses.

Steps:

• Data Collection: Gather historical data on loan defaults and losses.
• Loss Rate Calculation: Calculate the average loss rate (total historical losses divided by total exposure over the period).
• Application: Apply this average loss rate to the current loan portfolio to estimate expected losses.

Benefit of this model: This model is simple to implement and doesn’t require complex calculations or systems, making it ideal for smaller operations.

2. Vintage Analysis Model

This model groups loans by the year or quarter of origination and tracks their performance over time.

Steps:

• Loan Segmentation: Group loans based on origination date.
• Loss Tracking: Track historical losses for each segment.
• Loss Projection: Use historical loss trends to project future losses for each segment.

Benefit of this model: This model provides a structured way to use historical data, which is usually available even in smaller funds.

3. Roll-Rate Model

This model tracks the movement of loans between different delinquency stages to estimate future losses.

Steps:

• Data Segmentation: Segment loans by delinquency status (e.g., current, 30 days past due).
• Transition Rates Calculation: Calculate historical transition rates between stages.
• Loss Estimation: Apply transition rates to the current loan portfolio to estimate future losses.

Benefit of this model: It uses easily understandable metrics and doesn’t require complex modeling techniques.

4. Simplified Probability of Default (PD)/Loss Given Default (LGD) Model

While the full PD/LGD model can be complex, a simplified version can work for smaller funds.

Steps:

• PD Estimation: Estimate the probability of default for different loan segments based on historical data.
• LGD Estimation: Estimate the loss given default (how much is lost if a loan defaults).
• CECL Calculation: Multiply PD by LGD and the exposure at default to estimate the expected credit loss.

Benefit of this model: This approach provides a balance between simplicity and too many details, allowing smaller funds to capture important risk metrics without overwhelming complexity.

5. Discounted Cash Flow (DCF) Model

This model uses expected future cash flows from loans to estimate credit losses.

Steps:

• Cash Flow Projection: Project the expected future cash flows from the loan portfolio.
• Discount Rate: Determine an appropriate discount rate based on loan risk profiles.
• CECL Calculation: Discount the projected cash flows to present value and compare to the carrying amount to estimate expected credit losses.

Benefit of this model: This model is relatively straightforward and uses familiar financial concepts, making it accessible for smaller funds.

Conclusion

Small to Mid-Size debt funds should focus on models that are simple to implement and maintain while still providing accurate estimates. The Historical Loss Rate, Vintage Analysis, Roll-Rate, Simplified PD/LGD, and Discounted Cash Flow models are all workable options. By choosing the right model, debt funds can effectively manage CECL requirements without overwhelming their generally limited resources and time. We hope this helps! If you need more details on any of these models, let us know.