When a lender starts a financial institution with the aim of lending money to entities, he is most strongly fortified against credit risk. He undertakes several measures to lower credit risk and this is called credit risk modelling.
“A good credit risk assessment can prevent avoidable losses for an organization. When a borrower is found to be a debtor, it could dent their creditworthiness. The lender will be skeptical about offering loans for fear of not getting it back,” says a report.
Credit risk assessment is done to gauge whether a borrower can pay back a loan. The credit risk of a consumer is determined by the five Cs – capacity to repay, associated collateral, credit history, capital, and the loan’s conditions.
“If a borrower’s credit risk is high, their loan’s interest rate will be increased. Credit risk shows the likelihood of a lender losing their loaned money to a borrower.”Credit risk highlights a borrower’s ability to honour his contractual agreements and repay loans.
“Conventionally, it deals with the risk every lender must be familiar with, which is losing the principal and interest owed. The aftermath of this is a disturbance to the lender’s cash flow and the possibility of losing more money in a bid to recover the loan.”
While there is no pronounced way to determine the credit risk of an individual, credit risk modeling is an instrument that has largely come to be used by financial institutions to accurate measure credit risk.
“Credit risk modeling involves the use of data models to decide on two important issues. The first calculates the possibility of a default on the part of a loan borrower. The second determines how injurious such default will be on the lender’s financial statement.”
Popular examples of these models include Moody’s RiskCalc and Altman Z-score. “The financial statements obtained from borrowing institutions are analyzed and then used as the basis of these models.”
The Merton model is a suitable example of this kind of credit risk modeling. The Merton model is also a structural model. Models like this take into account a company’s capital structure “because it is believed here that if the value of a company falls below a certain threshold, then the company is bound to fail and default on its loans”.
“The influence of machine learning and big data on credit risk modeling has given rise to more scientific and accurate credit risk models. One example of this is the Maximum Expected Utility model.”
These are quantitative and qualitative methods adopted for the evaluation of a borrower.
“This generally looks into the track record of a borrower to know their reputation in the aspect of loan repayment.”
“This takes the income of the borrower into consideration and measures it against their recurring debt. This also delves into the borrower’s debt-to-income (DTI) ratio.”
The amount of money a borrower is willing to contribute to a potential project can determine if the lender will lend him money.
“It gives the lender a win-win situation, in the sense that upon a default, the lender can sell the collateral to recover the loan.”
“This takes information such as the amount of principal and interest rate into consideration for a loan application. Another factor that can be considered as conditions is the reason for the loan.”
There is no formula anywhere that exposes the borrower who is going to default on loan repayment. However, the proper assessment of credit risk can go a long way in reducing the impact of a loss on a lender. For more on this, do visit the DexLab Analytics website today. DexLab Analtyics is a premiere institute that provides credit risk analysis courses online.
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