Banking and Finance Risk Terminology

Many of the listeners to The International Risk Podcast ask about risk terminology specific to the banking and finance sector.  Here are some examples of key risk terminology and the most common definitions:


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.


These credit scoring tools use input financial variables to produce outputs (scores) that define a borrower’s creditworthiness.


This is a risk rating methodology for commercial loans in which the “dual” refers to the separate evaluation of borrower creditworthiness and loan riskiness.


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 one to ten, where one 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 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.


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.


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) is the percentage of the total value of a debt instrument that is lost when a default occurs.


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.


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) 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.