Goal of the analysis:
Evaluate how effectively the bank identifies, measures, and provisions for potential credit losses across its loan portfolio.
Data required:
- Loan portfolio data segmented by risk rating, industry, or asset class
- Historical default and loss data
- Credit models, internal ratings, and probability of default (PD) or loss given default (LGD) parameters
- Provisioning methodology and calculation details (e.g., IFRS 9, CECL)
- Regulatory requirements or guidelines on provisioning
- Peer or industry benchmarks for provisioning practices
Detailed step-by-step instruction on how to conduct the analysis:
Step 1: Gather comprehensive loan portfolio information, including borrower risk ratings and classifications (e.g., performing, watchlist, non-performing), and segment the data by relevant factors such as industry or collateral type.
Step 2: Review the bank’s credit risk assessment models and methodologies, such as PD and LGD estimates, ensuring they incorporate current macroeconomic variables and historical default trends.
Step 3: Examine the provisioning approach (for example, IFRS 9’s stages or CECL in the US) and verify alignment between model outputs (expected credit losses) and recorded provisions on the balance sheet.
Step 4: Compare the bank’s provision coverage ratios and credit cost rates to regulatory guidelines and peer benchmarks, identifying potential under-provisioning or over-provisioning relative to industry norms.
Step 5: Validate the accuracy of risk ratings and model assumptions by analyzing back-testing results, which compare estimated losses to actual losses over time, and investigate significant variances.
Step 6: Summarize findings in a detailed report, highlighting any gaps in the credit risk assessment process or deficiencies in provisioning levels, along with recommendations for improvement.
Format of the output of analysis:
- Tables showing provisioning levels, coverage ratios (e.g., allowances vs. NPL), and breakdown by risk rating
- Charts illustrating historical trends in provisioning, default rates, and recovery rates
- Narrative summary outlining model assumptions, validation findings, and recommended actions
How to interpret results:
- Adequate provisioning levels suggest the bank has sufficiently accounted for anticipated credit losses, reducing the likelihood of unexpected hits to capital.
- Under-provisioning can inflate short-term profits but exposes the bank to higher risk if defaults exceed prepared allowances.
- Over-provisioning, while conservative, may indicate overly pessimistic models or misaligned assumptions, impacting profitability.
Steps a company can take to improve on this measure:
- Regularly update credit risk models with recent macroeconomic and borrower-level data.
- Strengthen governance and oversight of the provisioning process, ensuring senior management and board engagement.
- Incorporate robust back-testing and stress testing to validate model assumptions and adjust provisioning strategies.
- Refine borrower risk ratings to better capture emerging risks and improve accuracy of PD and LGD estimates.
- Maintain transparent disclosures and ongoing dialogue with regulators to align provisioning practices with evolving guidelines.
