Credit Risk Measurement

There are various credit risk measurement methodologies today. It is a challenge for managers to see how these can be used to resolve the problem of credit risk measurement.

Executives of banks, financial institutions, and business organizations should be able to address the problem of credit risk measurement in order to protect their firms from credit loss.

Credit risk commonly refers to that risk that lenders face in case an obligor or creditor fails to settle or repay his debt. Credit risk measurement is, therefore, crucial as it helps determine and assign a quantitative value with regards to the capacity of a creditor to settle his account, as well as the default probability involved. Financial analysts consider credit risk more complex to measure compared to market risk for several reasons. It is also as difficult to model as market risk. One of the contributing factors for this is the absence of a liquid market that is capable of assigning a value of credit risk for a specific creditor and tenor. Another factor is that default probabilities can be distorted by inferring default rates that can be determined from historical public credit scores and subjective credit approval process. Lastly, default correlations are generally difficult to measure and observe, making it more complex to determine aggregate credit risk.

Company executives of banks and lending institutions now see the need to develop an accurate credit risk measurement process in order to balance rewards and risks. They should ensure that loan products do not have high interest rates, or else they stand the chance to lose a customer. At the same time, these loan products should not be too low to the point that they will translate to minimal profit margin or losses. In credit risk measurement, concepts like recovery rate, default exposure, unexpected losses, and default probability are just some of the concepts that are normally used. These measures are usually taken into account since small variations in credit risk measurement could have huge implications on credit risk estimates. Generally, consumer lenders use borrower credit scorecards as basis of improving portfolio management and decreasing underwriting costs. Nevertheless, the development of commercial credit risk measurement methodology is hampered by infrequency of defaults, as well as limited historical data that is available.

When measuring credit risk, two components are generally assessed. These components are unexpected and expected loss. According to the formula commonly used by banks, expected loss is a product of exposure, loss given default (LGD), probability of default (PD), and Exposure at Default (EAD). In essence, expected loss is a measure of average losses incurred over a given risk period. Unexpected loss (UL), on the other hand, is a measure of what might go wrong over a given period of time. To measure this, financial institutions usually employ a credit value-at-risk approach. The UL metric gives lenders an idea of potential volatility of a credit portfolio.

Most lending companies have departments that are specially organized to deal with the problem of credit risk measurement. The credit scorecard approach is widely used in the industry. Others also use specialized metrics and key performance indicators for measuring credit risk as part of their evaluation of clients seeking to benefit from their loan products.