Best Practices for Credit Risk Management in the Pandemic

What Is Credit Risk Management?

Credit risk management is a method that lenders use to reduce the odds of borrowers failing to meet the terms of their credit arrangements. It seeks to prevent losses caused by credit default and to reduce amounts lost when default occurs.

To achieve these goals, credit risk management strategy pursues these objectives:

• Avoiding borrowers who are unlikely to repay their debts or make payments on time
• Capping credit limits at amounts borrowers can afford to repay on time
• Adjusting interest rates to reflect risk and mitigate loss
• Identifying borrowers who are at risk of default or late payments so that steps can be taken to promote on-time payments

To meet these objectives, credit risk managers employ risk models and scoring systems that allow them to quantify the risk posed by lenders. They use these tools to screen potential customers and to monitor customers for signs of default risk.

Why Is Credit Risk Assessment Important?

Extending credit to borrowers earns money for financial institutions such as mortgage lenders. It also can benefit businesses in other industries by allowing customers more time to pay for purchases.

However, lending always carries some inherent risk. A certain percentage of borrowers will make payments late, while some will fail to repay what they owe at all.

A credit risk management plan allows you to enjoy the benefits of lending while reducing potential losses from bad borrowers. If you lend customers money, you can’t avoid risk entirely, but by following sound credit risk management policy and procedures, you can keep your losses to a minimum and keep your business profitable.

Credit risk assessment has become especially important during the COVID-19 pandemic. With many businesses facing financial disruption and the U.S. government authorizing relief programs to distressed companies, lenders must tackle the challenge of assessing creditworthiness for companies facing an uncertain future. Businesses that extend credit to consumers must likewise evaluate customers’ ability to repay debt.

The dynamics of the pandemic have forced lenders to adjust conventional credit-risk models, reports management consulting firm McKinsey & Co. For instance, companies with a strong online presence are better positioned to weather COVID-19 than strictly brick-and-mortar businesses, and this must be factored into risk assessments. Having a reliable method for assessing risk is vital if you’re extending credit during the pandemic.

How to Improve Credit Risk Management

Credit risk management strategies revolve around using mathematical modeling to lower the probability of taking on risky borrowers and to mitigate losses. You can use mathematical models to develop scoring systems that measure the likelihood of a borrower defaulting on their payments. This allows you to screen prospective customers and to intervene with customers who display at-risk behavior.

Implementing this procedure involves 5 main steps:

1. Collect customer data systematically
2. Develop risk models
3. Create a credit risk scorecard
4. Screen and monitor risks in real-time
5. Distribute risk reports efficiently

Here’s a breakdown of what each step involves:

1. Collect Customer Data Systematically

A good credit risk management strategy is data-driven. Build a basis for accurate credit risk assessment by creating a checklist of which types of data to collect from customers.

Depending on your business model and whether you lend to consumers or businesses, important data might include:

• Credit rating
• Business credit rating
• Monthly income or revenue
• Financial statements
• Age
• Years in business
• Ratio of balance to credit limit
• Number of open loans and lines of credit
• Credit history of delinquent payments 30 to 59 days past due
• Credit history of delinquent payments 60 to 89 days past due
• Credit history of delinquent payments 90 or more days past due

Select the data most relevant to your industry and market. Make sure to consider both financial and nonfinancial data. For example, when lending to a business borrower, the nature of the business and industry can affect risk. This is especially true in a COVID-19 context where online businesses have an advantage over brick-and-mortar companies.

Standardize Your Data Collection Procedures

To make sure the data you need gets collected, create standardized procedures for acquiring it during interactions with customers. For example, web forms and discovery calls can be used to gather key data.

Set up automated systems for automatically entering data into a central database from collection points where you interact with your customer. This will help ensure that relevant data is collected from all sources and available throughout your organization, providing a more complete view of customers.

2. Develop Risk Models

To interpret how your customer data translates into credit risk, you need a risk model. A risk model allows you to quantify risk in terms of specific, measurable categories.

Components of credit risk grading that typically go into risk models include:

• Probability of default (POD)
• Exposure at default (EAD)
• Loss given default (LGD)

Multiplying these three numbers together yields expected loss, a measure of all possible losses multiplied by the probability of those losses occurring. Here’s a brief explanation of what each of these metrics means and how they’re determined. An adviser can help you customize these variables for your business.

POD

POD represents the likelihood of a borrower defaulting, expressed as a percentage. For individual consumer borrowers, it normally is determined by considering credit score and the ratio of the prospect’s debt to their income (debt-to-income ratio). Other factors such as income level, number of open loans and history of delinquent payments may also be considered. For business borrowers, POD is normally obtained from firms that track business credit ratings.

EAD

EAD, also known as credit exposure in some industries, represents the maximum potential loss to a lender should the borrower default. This varies with loan amount, how much is still left to be repaid and the terms of the loan.

LGD

LGD represents how much you stand to lose should the borrower default on their credit agreement. It can be calculated in a number of different ways, but it is generally based on a percentage of EAD. This full amount is reduced to a percentage on the assumption that a certain amount of the money can be recovered through debt collection or bankruptcy proceedings, an assumption expressed as a recovery rate.

3. Create a Credit Risk Scorecard

Risk models can be used to develop credit scorecards. A credit scorecard uses a scoring system to measure the risk posed by a borrower. This provides a convenient way to represent and evaluate credit risk.

A scorecard is developed by observing which behaviors most often correlate with default. About 10 to 20 variables are normally isolated. Each of these variables gets expressed as a binary score, where 0 represents the customer performing a behavior in accordance with their loan agreement (for instance, making a payment on time) while 1 represents a behavior representing default (failing to make a payment on time). Different variables receive different scaled weight based on how strongly they correlate with default trends.

The resulting scale yields a scoring system that can be used to divide ranges of scores into categories representing how risky a borrower is. For example, scores can be divided into ranges representing bad, fair, good and excellent credit risks.

4. Screen and Monitor Risks in Real-time

Once you have a scoring system, you can use it to assess credit applicants according to your credit risk management policy and procedures. Based on estimated risk, you may choose to reject some applicants, while adjusting terms for others. Higher risk applicants may be offered lower credit limits or higher interest rates.

After extending credit, you can use credit scoring to monitor ongoing risks. By continually updating your customer data with information from your customers and credit bureaus, you can make a real-time assessment of credit risk. This can help you determine when to extend a customer a credit increase or when you need to intervene with a customer at risk of default.

5. Distribute Risk Reports Efficiently

For credit scorecards to be used effectively, they need to be readily available to decision-makers in your company. Use customized dashboards and reports to make it easy for personnel to access scorecard data. Include graphic tools, such as color-coded risk categories, which make numbers easy to visualize.

To put these displays to practical use, create procedures specifying when personnel should review scorecard data. For example, one procedure might prescribe when loan officers who are considering applicants should pull up scorecard data. You might set up another procedure to flag accounts that are at risk of default. Another occasion for flagging accounts might be when customers are due for a review to determine if they should be offered a credit renewal or increase.

Mitigate Your Risk to Maximize Your Profitability

Following the methods of credit risk mitigation we’ve outlined can help you reduce your losses from default and late-payment incidents, allowing you to enjoy the benefits of lending to customers while remaining profitable.

In today’s economic environment, you may be able to accommodate some higher-risk customers by offering creative credit arrangements that pose less risk than conventional options. For instance, revenue-based financing is scaled to a borrower’s revenue level, reducing the risk of default.

Considering alternative financing options such as this can open more opportunities for you to extend credit without placing yourself at undue risk.