What are Credit Risk Analysis Models?
Credit risk modeling is the application of risk models to creditor practices to help create strategies that maximize return (interest) and minimize risk (defaults). Credit risk models are used to quantify the probability of default or prepayment on a loan.
Financial institutions used credit risk analysis models to determine the probability of default of a potential borrower. The models provide information on the level of a borrower’s credit risk at any particular time.
If the lender fails to detect the credit risk in advance, it exposes them to the risk of default and loss of funds. Lenders rely on the validation provided by credit risk analysis models to make key lending decisions on whether or not to extend credit to the borrower and the credit to be charged.
With the continuous evolution of technology, banks are continually researching and developing effective ways of modeling credit risk.
A growing number of financial institutions are investing in new technologies and human resources to make it possible to create credit risk models using machine learning languages, such as Python and other analytics-friendly languages. It ensures that the models created produce data that are both accurate and scientific.
Factors Affecting Credit Risk Modeling
In order to minimize the level of credit risk, lenders should forecast credit risk with greater accuracy. Listed below are some of the factors that lenders should consider when assessing the level of credit risk:
1. Probability of Default (POD)
The probability of default, sometimes abbreviated as POD, is the likelihood that a borrower will default on their loan obligations. For individual borrowers, POD is based on a combination of two factors, i.e., credit score and debt-to-income ratio.
The POD for corporate borrowers is obtained from credit rating agencies. If the lender determines that a potential borrower demonstrates a lower probability of default, the loan will come with a low interest rate and low or no down payment on the loan. The risk is partly managed by pledging collateral against the loan.
2. Loss Given Default (LGD)
Loss given default (LGD) refers to the amount of loss that a lender will suffer in case a borrower defaults on the loan. For example, assume that two borrowers, A and B, with the same debt-to-income ratio and an identical credit score. Borrower A takes a loan of $10,000 while B takes a loan of $200,000.
The two borrowers present with different credit profiles, and the lender stands to suffer a greater loss when Borrower B defaults since the latter owes a larger amount. Although there is no standard practice of calculating LGD, lenders consider an entire portfolio of loans to determine the total exposure to loss.
3. Exposure at Default (EAD)
Exposure at Default (EAD) evaluates the amount of loss exposure that a lender is exposed to at any particular time, and it is an indicator of the risk appetite of the lender. EAD is an important concept that references both individual and corporate borrowers. It is calculated by multiplying each loan obligation by a specific percentage that is adjusted based on the particulars of the loan.