Credit Risk Measurement

For banks to generate income from loan products and other lines of credit, managers should be able improve credit risk evaluation with KPI that quantitatively measure exposure to credit risk.

It is a challenge for many corporate executives and risk managers to improve credit risk evaluation with KPI or key performance indicators.

According to the Principles for the Management of Credit Risk released by the Basle Committee on Banking Supervision released in 1999, credit risk is the potential that a counterparty or bank borrower will fail to perform his obligations as previously agreed. In light of this, the goal of credit risk management is to maximize the risk-adjusted rate of return of a bank or financial institution by limiting credit risk exposure. This describes the urgency for banks to effectively manage credit risk in their loan portfolio and transactions. Moreover, this also establishes the need for bank managers to understand the relationship between other types of risks and credit risk. Since the early days of banks, lenient credit standards for counterparties and borrowers, inattention to economic factors that will affect consumer behavior, and poor portfolio and risk management had been identified as the major causes of banking dilemmas. Particularly for banks, loans and other lines of credit are the biggest sources of credit risk. For this reason, banks are expected to make use of efficient credit risk management tools in order to limit risk exposure. Perhaps the foremost manifestation of this is the increasing amount of effort that bank managers put into identifying, measuring, controlling, and monitoring credit risk.

Fortunately, it is now possible for financial institutions to perform credit risk evaluation conveniently due to the onset of modern technology. Some advanced software applications have now become essential tools to support decision-making when it comes to which lending opportunities to pursue and which to ignore. Aside from accurately predicting potential losses to be incurred with high credit risk, these software tools also calculate the amount of assets or capital reserves needed to satisfactorily minimize risk. Moreover, industry experts and risk managers now see the wisdom behind using key performance indicators when measuring and controlling credit risk.

According to credit professionals, credit risk management could be efficiently implemented with thorough understanding and proper use of indicators, like probability of default (PD), Loss Given Default (LGD), Exposure at Default (EAD), Expected Loss (EL), and Unexpected Loss (UE). Probability of Default (PD), or Expected Frequency Default (EFD) is a frequency measure that describes the risk that a borrower may not be able to give full and prompt payment. Loss Given Default (LGD), on the other hand, is a new measurement concept that describes the risk that loss is incurred if there is already a default event. This concept is also labeled as loss in the event of default (LIED). Exposure at Default (EAD), meanwhile, is that measure which quantitatively defines expected drawn risk exposure during the time of default. Expected Loss (EL), as per its name, is a measurement of losses that are anticipated over a given risk period. In contrast, Unexpected Loss (UL) measures what may go wrong in a loan transaction. Aside from these concepts, liquidity ratios can also be used as success indicators. Thorough understanding of these concepts should help bank managers improve credit risk evaluation with KPI.