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The proper assessment of credit risk can go a long way in reducing the impact of a lender’s possible loss. Check out this guide to learn the basics of credit risk assessment.
Assessing the credit risk of a borrower is a lender’s priority. There are different measures available to do this. 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.
Credit risk shows the likelihood of a lender losing their loaned money to a borrower. It sheds light on the ability of a borrower to pay back a loan or meet their contractual agreement. 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.
There is no one way to determine the credit risk of an individual. Due to this, coming to a conclusion concerning a person’s credit risk is very complex. Since there is a lot on the line, the need for accurate measurement of credit risk has become more pronounced. So far, the best way has been found to be credit risk modeling.
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.
No financial institution can do without credit risk models because without them the credit risk of potential borrowers cannot be ascertained. This helps them arrive at a decision, whether to give a loan or decline its application. By it, the financial institution can calculate appropriate interest rates to reflect the borrower’s verified credit risk.
The ability to put the data, such as financial history, income, etc, about a borrower into good use will determine how accurate a credit score will be. To determine credit risk, here are some examples of how to go about it.
Financial Statement Analysis Models
Popular examples that fall under these models include Moody’s Risk Calc And Altman Z score. The financial statements obtained from borrowing institutions are analyzed and then used as the basis of these models. Financial ratios that have been proven to be useful in determining credit risk are prioritized by these models. For instance, financial ratios like sales/total assets and EBIDTA/total taxes are used by Altman Z score in different proportions to find out the chances of a company going bankrupt.
Default Probability Models
The Merton model is a good 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. This is so 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
Machine Learning Models
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 methods adopted for the evaluation of a borrower and it involves the use of both quantitative and qualitative measures.
Though referred to as character, credit history is more suited for the first C. This generally looks into the track record of a borrower to know their reputation in the aspect of loan repayment. This information is not hidden as it is shown on the credit report of the borrower. Three major credit bureaus are responsible for this report and they are Equifax, Experian, and TransUnion. The report reveals the past loan transactions of an applicant and whether they were repaid as at when due.
The majority of lenders have attached to their loan services a minimum credit score that must be satisfied by applicants before a loan may be granted. The minimum credit score is not the same with all lenders and loan products, they vary. When a borrower’s credit score is high, then there is a good chance of them getting their loan application approved.
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 DTI is found by summing up the total monthly debt payment of a borrower and dividing it by their gross monthly income. A borrower with a low DTI is more likely to get a loan before an applicant with a high DTI. A DTI score of around 35% or less is the standard among many lenders.
The amount of money a borrower is willing to contribute to a potential project can either move or stay the hand of a lender towards granting a loan application. When the borrower is ready to commit a large sum of money into the project, the lender sees it as a sign of possible repayment in the future. An example is in the area of paying for a home. Those who are able to make a down payment will have an easier time accessing a mortgage.
Collateral is another factor that can speed up a borrower’s loan request. 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. In most cases, collateral represents what the loan will be used for. For instance, the collateral for auto loans is cars and that of mortgage loans is homes. This is why collateral-backed loans are known in some quarters as secured debt or secured loans.
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. An example is a loan application for a car or a home improvement. The lack of vagueness in the loan application may convince a lender to part with the requested sum of money. Other aspects of conditions a lender may look at include industry trends, pending legislative changes or the state of the economy, all of which are beyond the control of the borrower.
A lender has a lot of risks to contend with from cash flow disruption and loss of interest and principal to increased collection cost. With all these in the picture, the accurate forecast of credit risk can never go out of fashion.
In the evaluation of credit risk, there are several influential factors that one cannot afford to disregard. Some of them include The borrower’s financial health and the aftereffects of default on both the lender and borrower. A borrower’s credit risk can be affected by the following factors:
The Probability of Default (PD)
This reveals the borrower as someone who is likely to default in repayment of a loan. For individuals, this conclusion is arrived at via the score of their debt-income ratio, as well as other credit scores. Institutions have it different. For those who fall into the category of bond issuers, their score is calculated by rating agencies like Moody’s and Standard & Poor’s. In most cases, the PD is used to evaluate the amount of down payment and interest rate needed.
Loss Given Default
This focuses on the worst-case scenario of failure to repay a loan. Here the spotlight is on the lender and the amount they stand to lose. Even with a similar debt-income ratio and the same credit score, two borrowers will not have the same credit risk profiles if the amount of loan one of them is seeking is higher than the other person’s own.
This is so because the lender will suffer more loss if the borrower of the larger amount defaults. This factor also comes in handy when down payments and interests are being determined. However, if the collateral is part of the loan negotiation, then it will change a lot, including the interest rate.
Exposure at Default
A lender’s total exposure at any given time is measured by this factor. Credit risk is influenced by this because it reveals the level of risk a lender is comfortable with. To calculate this, the loan is multiplied by a certain percentage based on the nature of the loan.
Credit risk is usually given an additional cover through excess cash flows. 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. When a borrower makes interest payments, it is seen as a reward to a lender who assumed credit risk.