Case Study: Analyzing Loan Portfolio Risk and Implementing CECL Compliance

This Redwood Community Bank presentation analyzes loan performance for a ten-year period to improve loan pricing and comply with the Current Expected Credit Loss (CECL) accounting standard. The analysis uses a Probability of Default/Loss Given Default (PD/LGD) approach to estimate expected losses and inform risk-adjusted pricing. Two methodologies for calculating the Allowance for Credit Losses (ACL) are presented, incorporating factors like charge-off rates and recovery percentages. The analysis concludes with recommendations for data improvement to refine future calculations and loan pricing strategies improve profitability.

This case study, prepared by Premier Insights, examines the methodologies and outcomes of analysis conducted by Premier for a financial institution. To maintain confidentiality, all bank and market names have been fictionalized. The specific dollar amounts and percentages in this case study shown may not reflect real-world scenarios.

Introduction

Premier Insights, Inc. was engaged by Redwood Community Bank to analyze its loan portfolio performance, prepare for CECL (Current Expected Credit Loss) implementation, and enhance loan pricing strategies to better account for credit risk. The project utilized loan data for a ten-year period to identify key risk factors and improve the bank's understanding of its loan portfolio.

Project Objectives

The primary objectives of this engagement were to:

• Analyze loan performance using historical data.
• Determine borrower and loan characteristics that predict default.
• Ensure compliance with CECL standards by quantifying lifetime expected losses.
• Quantify qualitative factors and market conditions.
• Improve profitability through risk-adjusted loan pricing.
• Enhance understanding of the loan portfolio and associated risks.

Methodology

Premier Insights, Inc. employed a Probability of Default (PD) | Loss Given Default (LGD) approach. This approach involved two stages:

1. Probability of Default Estimation: Using regression models and historical data, we identified predictive factors for loan defaults and assigned a PD to each open loan.
2. Loss Given Default Estimation: We utilized two different approaches to estimate the percentage of loan amount lost in the event of a charge-off.
   • Approach 1: Employed regression models and historical data to calculate LGD.
   • Approach 2: Used assumptions based on loan type, such as losing 110% on unsecured loans and recovering 70% of collateral value on secured loans.

Illustrative Example

To illustrate the concept of expected loss and its impact on loan pricing, we examined a set of hypothetical loans. Assuming a 2% average charge-off rate, for example, the expected interest earned changes significantly. For example, with a 2% probability of default and a $5,000 loss given default on a $10,000 loan with $1,000 in interest, the expected return is calculated as 98% ($1,000) + 2% (-$5,000) = $880. The calculation clearly shows the impact of incorporating the possibility of default.

Key Findings

The analysis revealed the following insights into Redwood Community Bank’s loan portfolio:

• Loan Portfolio Composition: The Bank's portfolio included mortgage loans, consumer loans (secured and unsecured), and commercial loans (secured and unsecured). The total dollars outstanding for each loan type were identified, as well as expected loss rates.
• Probability of Default: The average probability of charge-off varied by loan type. For example, mortgage loans had an average probability of 1.80%, while consumer loans (unsecured) had an average of 3.60%.
• Loss Given Default: The average loss given default also varied across loan types. For instance, mortgage loans had an average LGD of 34.00%, while commercial loans (unsecured) had an average of 46.00%. In another approach, average LGDs were as high as 110% for unsecured loans.
• Expected Loss: The expected loss was calculated for each loan type, and the analysis provides the basis for calculating the Allowance for Credit Losses (ACL).
• ACL Calculation: Two approaches were used to estimate the ACL. Approach 1 resulted in a total ALLL of $8,867,383, while Approach 2 resulted in a total of $5,782,399.

Applicability to Loan Pricing

The analysis is applicable to loan pricing by incorporating:

• Probability of Default
• Cost of Capital
• Cost of Funds
• Overhead

For example, to achieve a desired return of $1,000 in interest, the Bank would need to charge 11.22% on a loan with the previously mentioned risk and loss profiles. The analysis helps determine the appropriate interest rate for various loan types, incorporating expected loss, cost of capital, and other overhead.

Data and Next Steps

To further enhance the accuracy of the analysis and better understand the loan portfolio, it was recommended that Redwood Community Bank:

• Continue to consistently capture data, including charge-off and recovery data.
• Capture "bank owned principal assets" on the day before a loan is charged off, in addition to when the loan charges off, since the current value is zero.
• Record loan terms in months to increase data consistency.
• Regularly recalculate and update the analysis to reflect changes in data and market conditions.

Conclusion

Through this analysis, Premier Insights, Inc. provided Redwood Community Bank with a thorough understanding of its loan portfolio's risk profile, established a foundation for CECL compliance, and offered recommendations for risk-adjusted loan pricing. The project demonstrated the value of a data-driven approach to financial risk management and the importance of considering the probability of default and loss given default when assessing loan portfolios. The results of this analysis have allowed Redwood Community Bank to better understand and manage risk while improving profitability and planning for the future.