Finance is a business built on trust: finding clients worth taking on credit risk and building relationships with them. Unfortunately, the credit risk of each applicant and customer isn't always easy to determine — especially if they have missing or fraudulent documentation.
And with loan originators up to their eyeballs in applications, missing or fraudulent information isn't always easy to catch. Yet, overlooking these issues can put your company thousands, or even millions, into debt. Thankfully, there are tools and processes you can implement to improve credit risk management.
Let's review what credit risk management is and how you can manage it using the right software.
What is credit risk?
Credit risk is the risk of lending money to a client and not receiving the owed principal and interest (i.e., a borrower failing to repay a loan or meet another financial obligation like paying rent). This messes up your cash flow and leads to expensive debt collection procedures.
Most times, applicants with a higher credit risk receive higher interest rates from lenders to increase cash flow. But this alone doesn't prevent defaults, nor is it easy to predict who will stop making loan payments. However, there are ways to reduce the odds, which we'll touch on below.
Why is credit risk important?
Lenders are in the business of on-time payments. When customers are late, it hurts the company. It delays cash flow, which is necessary to pay the salaries of the owner and workers. So having too many high credit risk loans in your portfolio might run your business into the ground.
This is why it's critical to assess the credit risk of applicants and ensure you're not taking on too many high-risk clients.
A great example is the financial crisis of 2008. Lenders gave out loans to poor-credit applicants with a questionable means to repay them. So you had homeowners getting large loans they couldn't afford, and when the economy took a hit, the real estate mark collapsed. Bankruptcies skyrocketed across the financial sector, leading to bailouts from the government to prop up the system.
Thankfully, laws are in place now to prevent this from happening again. But it's still up to lenders to ensure their borrowers are a good financial fit for the loan programs they apply for.
3 factors that affect credit risk modeling
Banks, lenders, and other financial institutions need to reduce risk. So they use models to determine the creditworthiness of a borrower. Here are the factors used in the modeling:
- Probability of Default (POD): The odds of a borrower defaulting on a loan obligation. For a home mortgage, lenders use credit scores and debt-to-income (DTI) ratios to determine the creditworthiness of borrowers.
- Loss Given Default (LGD): Represents the amount of loss lenders may suffer in the event of loan default. The larger the loans given, the higher the risk of (or exposure to) loss becomes.
- Exposure at Default (EAD): Evaluation of a lender's loss exposure based on how many loans it gives out and at what amounts. The more a lender gives out, the larger the credit risk exposure to default.
What are the 5 C's of credit?
When a client applies for a credit card, auto loan, mortgage, or other financing, you must consider the 5 C's of credit. These include:
- Character: This represents the financial history of the borrower, which sheds light on their debt management skills. Credit reports from all three bureaus allow lenders to determine this. The lender approves or disapproves the applicant using scores provided by credit rating agencies, FICO, or VantageScore. Or the lender increases the interest rate if low scores aren't an issue for approval.
- Capacity: How much room does the borrower have to repay the loan? Calculating the amount of debt and income shows what's left over to use towards paying it back. Having a DTI ratio to determine what's good helps to identify the best candidates. For example, a 36% DTI is typical (e.g., $2000 in income and $720 in debt, with $1,280 remaining) for a mortgage.
- Capital: Income is great, but financial reserves, investments, and assets also show creditworthiness. Borrowers can use these towards the down payment for a mortgage, for example. This is ideal for borrowers who want to reduce their interest rates and monthly payments. This capital is also useful in the event the borrower defaults on the loan.
- Collateral: Secured loans and credit cards use cash and assets as security in case the borrower defaults on their loan. If this happens, the lender or creditor can take the asset and cash to use towards what's owed.
- Conditions: Lenders often create their own requirements for loan approval. For instance, being on the job for a certain number of years or the funds being used for a specific investment (e.g., primary home or investment property).
How does credit risk affect interest rates?
Traditionally, lenders raise the interest rate for high-risk borrowers. So the higher the credit score, the lower the interest. The same goes for other factors, such as debt-to-income ratios.
If a borrower has a high DTI, then this is a higher risk, so the lender will ask for more down and per month (via a higher interest rate). The purpose is to increase the interest payments, so the lender gets more cash flow earlier in the event of a default.
What is a credit risk management process?
Every lender should have a strategic credit risk management process for qualifying applicants for a loan, credit card, or other contractual obligations. Within this process, you can use a mix of credit scores and pre-set conditions from the firm to determine loan eligibility. Using this, loan originators can create a risk profile for underwriters to review.
Here's a look at the typical process financial institutions use.
- Prequalification: This is where the company determines whether the applicant meets the minimum qualifications needed to get approved for a loan.
- Underwriting: This is the step that uses the prequalification results to determine the applicant's approval for the loan.
- Approval: The final step involves approving the loan. It looks at the information gathered during underwriting and may require additional documentation (e.g., updated credit check or bank statements).
How can banks reduce credit risk?
So we covered the various ways to calculate the risk of a credit applicant, but what can a lender do to minimize defaults? There are a set of credit risk management practices banks can use to be consistent with credit risk management.
Don't just look at credit data
Credit information shows of an individual's ability to pay bills and repay loans, and a credit score offers a qualitative analysis of the applicant. But it's not the complete picture. It's also ideal to conduct your own analysis.
This includes having an internal set of conditions and scores, alongside considering your entire portfolio. Have a standard for the level of risk you want to take in advance, then ensure you don't surpass this. For example, holding no more than $500 million in your loan portfolio or having only 20% of your portfolio made up of high-risk borrowers.
Use technology to streamline your process
Technology exists today to make document processing and analysis easier. For example, Inscribe makes it easy to review online credit applications and qualification documents, such as financial statements, licenses, and utility bills remotely. Our AI-powered software analyzes the documents to detect fraud, parse information (names, addresses, etc.) and assess the creditworthiness of the applicant.
This can make your job easier, enabling you to process more applications faster and submit approvals with confidence. Great news, especially if you're dealing with an influx of applications you can't get through quickly enough.
To date, Inscribe's helped financial organizations 10x the speed of application reviews, saving finance teams over 200 hours per week. Fraud detection is also critical—our platform catches $80 million in fraud per month.
Build a structure everyone follows
Adopting technology into your credit risk management workflow reduces human error and fraud. But it alone won't ensure a low level of credit risk. You need a structure your teams can follow to ensure everyone's on the same page about what's acceptable and what isn't.
Create a system everyone follows to maintain consistency. And educate your teams on how to use the software you adopt so costly mistakes don't occur.
Improve your credit risk management with AI
Safeguarding your company from credit risk keeps businesses afloat for years, so you shouldn’t just rely on manual processes. By adopting AI technology, you can get more than just a second pair of eyes.
Machine learning tools can analyze applications and help determine the risk of an applicant. This includes flagging fraud and credit risk threats within minutes (not hours). The number of applications you can process using credit risk management software is astounding.
Inscribe’s fraud detection and automation platform is designed for financial institutions, complete with databases of financial templates used to determine if a bank statement or utility bill is fake or altered. So you save time and minimize your credit risk.
Curious to see how it all works? Speak with one of our experts today.