In a world where credit is the most readily available option to obtain financial resource whether it is for personal or business capital purposes, credit metric systems are very much useful. Also called credit scorecards, these systems use quantitative measurement indicators called credit metrics or credit risk metrics. The purpose of these systems is to identify the probability of an organization or individual to pay his obligation on time and to present a clear status of a customer's present and past credit standing. But how does credit risk metrics help lenders?
The process of obtaining historical data from a database of client records and corresponding load defaults is commonly called credit scoring. This observation is then processed, analyzed, and presented as a meaningful information. Based on the customer's historical data, the credit score system will then rank the client by order of credit risk but not necessarily pointing out the probability for default.
It may sound easy on the surface, but in reality though the process of measuring the credit riskiness of a borrower is an arduous and often challenging task to do. The reason behind is that credit risk is a tricky to model if it be compared with market risk. Primarily, there is a shortage of liquid market that contributes to the unfeasibility of pricing credit risk for a specific tenor or obligor. Secondly, the probabilities of true default are obtained ambiguously. In some cases, it can be determined through analysis of a default rate resulting from a process of subjective credit approval. And in other cases, true default probabilities can be drawn by assuming default rates according to the recorded historical experience.
The third reason why credit risk is hard to model is that there is a big challenge in measuring or observing the default correlations thus making it difficult to combine credit risk.
Credit risk metrics comes into play by minimizing these challenges. Among the most commonly used indicators in credit scorecards are capital adequacy, gross debt service, customer credit quality, customer perspective, and company perspective. All of these metrics focus on modeling credit risk rather than trying to find out if a client is worthy to be granted a credit.
Because of the emergence of these metrics, credit scorecards today have become smarter and more useful. There are now new approaches to measuring credit riskiness such as reduced form credit models, logistic regression, and hazard rate modeling. These approaches only differ in their database structures and their capacity to calculate the financial vale of the loan, provided that the risk level is known from the credit point of view.
The database of these systems is stored with every client detail, from defaulted loans to unpaid dues, which literally makes it easy for lenders to determine the products of macro-economic elements like stock prices, interest rates, and auto prices.
A credit risk scorecard is very simple to use. After the appropriate metrics are identified, the latest company accounts are then encoded. The system will then compute the financial ratios based on factors like stock turn, trade debtors, and net profit rate. Credit metrics indeed have helped improve the way lenders evaluate borrowers.