In a recent blog post, we offered a comprehensive checklist of time-tested steps for designing and implementing your bank’s dual risk rating system. One of the most important steps outlined in that post was documentation – the process of documenting the dual risk rating system so that there’s total clarity across the organization on how to implement it effectively.
A great place to start with this documentation is compiling a glossary of commonly used risk rating jargon and arriving at an exact definition of what each term means for your bank. While the glossary will rely partly on dictionary definitions, the full explanations should reflect the uniqueness of your bank’s risk rating methodology – or at least that of the vendor.
To help get you started on the glossary portion of your documentation, here are some examples of key terms and widely used definitions:
Dual risk rating
This is a risk rating methodology for commercial loans in which the “dual” refers to the separate evaluation of borrower creditworthiness and loan riskiness.
These credit scoring tools use input financial variables to produce outputs (scores) that define a borrower’s creditworthiness.
A borrower is in default when it cannot meet its obligations to pay principal and interest on a loan. Commonly, the borrower is 90 days or more past due on principal or interest payments and has likely filed for bankruptcy protection. As a result, the loan will no longer accrue interest and will be fully or partially written off.
A scale, or master scale, refers to the number of ratings on each dimension. The scale can be numeric, letters, or variations thereof. For example, a bank could use a numeric scale of 1 – 10, where 1 would represent the least risky borrower. A larger scale can provide more insights than a smaller scale, but only up to some limits. One could argue that a 100-point scale would be too much, and that it would be hard for users to truly distinguish the difference between a 44 and a 48.
A risk factor is a data element used in the scorecard, for example Debt Service Coverage Ratio. The value of that factor will be given a score based on the scale, and the overall impact of the risk factor in the scorecard depends on the weight assigned.
Some scorecards include an adjustments feature, which gives the analyst a standard way to introduce any material deal or borrower information not reflected in the scorecard. For example, if the analyst knows that the anchor tenant in a commercial property is leaving after next year, the analyst can notch the risk rating downward to reflect this risk to rental income.
Creditworthiness is an informed opinion about the future likelihood of a business to generate cashflow sufficient to pay expenses, meet obligations to debt and equity holders, and reinvest a sufficient amount in assets to support future sales.
Probability of default (PD)
A key output of the credit risk rating model, the probability of default (PD) is a measurement of the likelihood that a borrower, or obligor, will default on its obligations to pay principal and interest.
Loss given default (LGD)
Loss given default (LGD) is the percentage of the total value of a debt instrument that is lost when a default occurs.
Expected loss (EL)
The percentage of expected loss (EL) for a borrower equals PD times LGD in the event of a default. To get the dollar amount, multiply by Exposure at Default.
Average expected loss of a portfolio
This is the weighted average of expected loss of the debt instruments in the portfolio with weights being equal to the proportion of each individual exposure in comparison to the size of the total portfolio.
Unexpected loss (UL)
Unexpected loss (UL) aims to safeguard against volatility and quantify portfolio diversification. This can be difficult since portfolio diversification depends on the correlation between possible defaults of the individual assets in the portfolio. The portfolio’s unexpected loss is a function of the individual debt instruments, weights, and the correlation between individual assets. Therefore, default correlation matrices must be applied to estimate portfolio diversification.
Defining These Terms for Your Bank
Together, the above terms comprise the holistic framework of variables that make up a risk rating system. Since every bank and risk rating system application is different, however, it’s vital that you don’t stop at these dictionary definitions in your documentation.
Start by unpacking your bank’s specific methodology regarding each term, especially the more fluid ones like average expected loss and unexpected loss. Ask questions like, “How does this work at our bank?” then capture those answers in your documentation to ensure everyone is on the same page. And if the answers are fluid based on real-time factors, host the documentation on a collaboration platform like SharePoint so everyone can see the latest updates as they occur.
In addition to the glossary, you should also create documentation of what each rating – 1A through 14J, for example – means for your bank. While the scale will be and should remain objective, there may be a strategic component regarding how much risk you can take on if you’re pushing to improve portfolio quality. Answer questions like:
- What is the override process?
- When are overrides permissible?
- Who gets the final say on overrides?
- What is the lowest rating we’re willing to accept?
- How do we approach adjustments?
Finally, if you’re implementing dual risk rating with the help of a trusted partner who offers either a Software as a Service platform or integrated solution, you should work closely with them to understand their system and the corresponding documentation so it aligns with your bank’s risk rating goals. Rely on their expertise to get the most out of the system and utilize any existing documentation that makes sense for your bank.
Will Kutteh is the Senior Manager of Risk Products at RMA, overseeing the RMA Model Validation Consortium and Dual Risk Rating solution. Will has over five years of experience in the financial services industry, with a focus on Enterprise Risk and Model Risk Management. He has performed validations of models used in liquidity measurement, credit analysis, interest rate risk management, CCAR stress testing, and CECL. Will holds a BA in economics from Wake Forest University.