Mortgage lenders and banks use credit risk modeling to minimize losses from loan defaulters. They rely on custom models developed using historical data, borrower characteristics, and economic conditions. Proper modeling produces more accurate predictions of the likelihood of a borrower to default on payments. Here are some of the key components of the modeling process:
Knowing Your Borrower
Data collection and preparation are the first steps in building a credit risk model. One key data set financial analysts assess is borrower information, such as age, income, credit history, and employment. Effective credit risk modeling enables lenders to filter out ineligible borrowers early on. Mortgage lenders can implement borrower background assessments to avoid money laundering and wrongful funding.
Gathering comprehensive information about your borrowers enables you to predict their likelihood of defaulting on a line of credit. Know Your Customer is the process that banking and non-banking financial companies use to understand their customers. This process involves requesting documents that offer proof of personal details like age, income, employment status, address, background, and identity. Such details must then be verified for accuracy and consistency with other records.
Assessing Credit Worthiness
After verifying your customers’ information, you can assess their creditworthiness to determine whether they are capable of repaying the loan. This step involves calculating the probability of default, which is the likelihood that a borrower will fail to meet their loan obligations. If the borrower is an individual, the probability of default is assessed based on the individual’s credit score and debt-to-income ratio. For corporate borrowers, the information is gathered from credit rating agencies.
If a borrower has a lower probability of default, their loan may attract a lower interest rate and down payment. Borrowers with higher default probabilities may be charged higher interest rates or disqualified for the loan. Your financial institution can manage the risk of default by allowing borrowers to pledge collateral against the loan.
Quantifying Potential Losses
Assessing the probability of default allows you to optimize credit allocation, pricing, and terms based on the borrower’s risk. You should also estimate the potential losses your business may incur in case the borrower defaults. Credit risk models factor in variables like loss given default and exposure at default. Loss given default is the amount of money you could lose if the borrower defaults on the loan.
As a mortgage lender, you can calculate the total loss exposure across your entire portfolio of loans. Exposure at default is the amount your institution is at risk of losing at any particular time. Since borrowers make partial payments and installments, this exposure varies. Credit risk modelers calculate exposure by multiplying each loan obligation by an adjusted percentage based on loan specifics.
Understanding Credit Risks
Building a credit risk model involves understanding the different types of risks your financial institution faces when working with borrowers and making investments. Credit default risks occur when the borrower is unable to pay back the loan in full several weeks after the due date. This risk may be caused by changes in the borrower’s financial situation or broader economic circumstances, such as a national recession.
Concentration risks arise when you have less diversification in your lending portfolio. The high level of concentration on one asset may result in losses if borrowers stop using your main product. Country risks occur due to macroeconomic factors, such as political instability or a country freezing foreign currency payment obligations. Changes in the business environment also affect regional interaction with credit products. Understanding the different types of risks allows you to:
- ·     Anticipate and hedge economic risks
- ·     Structure asset-backed securities
- ·     Forecast credit losses
- ·     Set proactive loan servicing and refinancing
Get Professional Credit Risk Modeling
Credit risk models enhance your decision-making process by determining the likelihood of default based on customer data. These tools automate the lending process while minimizing the risks and losses associated with missed payments. Contact a credit risk modeling provider today to learn more about their risk modeling services.
