Financial institutions usually partake in modeling credit risk if they are in the practice of making loans to individuals and businesses for various purposes. This modeling usually involves a statistical inspection of past loan history in the attempt to predict the probability of future loans being defaulted. These models can help a bank or other lender decide which loans to offer and the terms that accompany those loans. Modeling credit risk requires detailed information from a variety of sources to accurately predict risk levels in the future.
One of the main functions of a bank is to provide loans to its customers, which can then be used to buy homes, cars, or any type of significant purchase imaginable. Many of those loans are given by banks unsecured, which means that there is no form of collateral offered by the borrower. For that reason, banks and other lenders are vulnerable when their borrowers do not pay back their credit obligations. As a result, these institutions find ways of modeling credit risk to assure that their loaning practices are sound and wise.
Any method of modeling credit risk is usually only as effective as the reliability of the data it receives. It is crucial that any modeling attempts be used in conjunction with exhaustive efforts to unearth as much information as possible on potential borrowers. This obviously includes their past credit histories, but it should also include thorough examination of their financial standing, future earnings potential, and any other factor that may affect their ability to pay back loans.
Modeling credit risk may also involve a look at the types of loans being offered. For example, if past history shows that mortgage loans in a certain area of a town have been defaulted at an alarmingly high rate, the bank might think twice about offering another such loan in the future. Risk assessments can also be used to alter interest rates offered to individuals seeking loans, thus helping the lender offset the risk.
The outcome of modeling credit risk can be a general assessment, or it can be a much more detailed overview of the potential loan. For example, after assessing all the risks, the statistical model may simply determine that a particular loan is either safe or risky. Other modeling methods may attach a percentage rate to the likelihood of a specific loan being defaulted. Banks and other lenders should judge different modeling methods on their success rate over a lengthy period of time to determine if they're reliable predictors of credit risk.
