Risk modeling is an essential cornerstone of the lending process. There are a few different ways to generate a risk model that will help guide your loan process and help prevent losses for unpaid loans. Every credit risk model will have its own strengths and weaknesses, therefore it is critical to develop a suitable model to match your lending program style.
Credit risk models must be followed carefully, and when they are implemented correctly, they can prevent a multitude of issues with loan default and the resulting damage to an individual’s financial credit. You have a responsibility to help your customers stay within their financial means, and as a financial institution, you want to be sure that you are funding loans that can be serviced correctly and without issue.
Over the past two years, many financial lending institutions have been operating in the dark, or taking a wait-and-see approach to lending, especially when came to non-prime borrowers. This is due to a variety of factors that have upstaged the economy, sending it on a rollercoaster ride of ups and downs over the past 24 months. Concerns surrounding COVID-19, supply chain disruptions, increasing inflation rates, war excursions, and other elements, it has been difficult to make precise forecasting on long-term patterns, as well as case-by-case credit borrowers.
To the surprise of many experts and credit risk modeling analysts, through 2021, consumer credit performance remained high across several industries including auto, credit card, personal and mortgages. This was particularly exemplified in credit card loans, as the market witnessed a high rate of new account growth in Q3 2021 with a record 20.1 million originations, 9 million of which were to non-prime consumers.
While this strong credit performance is encouraging, financial institutions need to remain sensitive to their risk models as miscalculations and market drops could loom.
Read on for an introduction to credit risk modeling.
What is Credit Risk Modeling?
A credit risk model is a tool used to score the probability of an individual or business being able to repay their loan. In theory, credit risk models are analytics performing at their best. Used by financial institutions and other credit lending companies, a well-optimized model will prevent losses and defaults and help increase returns on loans through the collection of principal and interest payments.
These systems rank applicants based on their creditworthiness and help determine the interest rates and payment structures that each applicant can be offered. Many risk models offer different interest rates and loan payment structures for each product type that a financial institution provides. This risk model system helps make credit available to customers without prejudice or bias.
Credit risk can be defined in many ways, but the most common reason an application might be deemed a credit risk is the customer’s financial history. Customers with many debts or poor credit are often turned away by lenders. Creditworthiness is based on prior loan management and prior ability to pay owed debts. With a credit risk model in place, high-risk loans are identified and are either approved with limitations or denied altogether.
When a credit risk model is used correctly, it will accurately project a probability of default and help protect the financial exposure of banks and other companies. This protects the lending institution from taking an expected loss and helps prevent customers from taking on debts that they cannot afford. These systems reward customers with top-tier credit by offering them low-interest rates and more flexible repayment terms.
Why is Credit Risk Modeling Important to Financial Institutions?
Credit risk modeling is critical for financial institutions to prevent issues with bias, oversights, and losses that could cause the bank or credit union to be forced to close or experience negative stress. While offering reduced terms and higher interest rates to customers with damaged credit might seem unfair, there is a significant risk in offering a loan to someone who has poor credit or an income that will not support this added debt. Consumers with damaged credit may argue that they can pay the loan off, but prior credit history will always be the best indicator of someone’s probability to repay a loan.
When used correctly, a credit risk model will ensure that loans are only given to those with the income and credit history to pay them back on time and without issues related to late payment or non-payment.
As mentioned, the past two years have thrown multiple curveballs at financial lenders. As both the U.S. and global economy remain in an environment of unpredictability, it is with great pressure that financial institutions should invest heavily in credit risk modeling that can adequately handle this unique environment. Many industry experts detail how banks, credit unions, and other specialty lenders have models ill-designed for today’s market due to issues like:
- A limited history and access to high-quality data
- Recent misconceptions of inflation not being a driver of financial losses
- Slow time to value for models
- A lack of agile model deployment
The 2008 financial crisis delivered a hard lesson to banks and credit unions about overextending credit to customers. High-risk lending was given the green light far too often. The change in fortunes of many people led to a downturn in the economy, lost jobs, and increased costs for daily expenses. These forces conspired to generate a domino of losses. Banks and credit unions that failed may not have realized that they were offering loans to people who could not afford them. Risk-based lending is the only way to help protect a financial institution from a miniature version of this sort of crisis.
Therefore, financial institutions need to follow the trending path of fintechs, who are adopting cloud-based credit risk modeling that utilizes artificial intelligence and machine learning to better anticipate credit risks. Through this advanced technology, financial institutions are able to experience improved model process automation. This includes enhanced decision-making processes, data management, quality control of that data, the decision and the associated documentation. This is all to say that, financial lenders are now equipped with the right information sets to make better data-driven decisions on a shorter timeline.
What Are the Factors That are Included in Credit Risk Modeling?
Credit risk modeling analyzes a few key items when assessing loan applications. Using credit risk modeling ensures uniform and equitable decision-making regardless of the applicant.
- Risk of Default: Has the borrower defaulted on a loan before, or do they have a history of non-payment or late payment? Do they have damaged credit related to other repayment struggles? Personal financial history is the strongest indicator of whether or not a borrower will adhere to their loan agreement.
- Concentration of Risk: Does the borrower work in a seasonal industry that’s likely to experience layoffs and staff cuts? Are they self-employed and struggling to stay profitable? Do they have many other loans they are paying on already? Borrowers with inconsistent income our outsized financial responsibilities are at high risk of late payment, non-payment, and default.
- Location or Country Risk: Is the borrower a citizen of another country and only temporarily in the country where they are requesting the loan? Is this person someone who has no ties to the country in which they are asking for a loan? It’s very difficult to recoup funds internationally, and if a borrower is unable to repay a large loan, they may leave the country to avoid consequences.
These more significant considerations are looked at to determine the probability of default and degree of risk exposure. Each applicant’s credit file and other financial factors, or lack thereof, will tell a clear story of the kind of risk inherent in lending to each applicant. While credit history can be very informative, you also need to look at the entire personal portfolio to gain a holistic lending picture in order to make an informed decision and deliver a score rating.
How to Handle Credit Risk Modeling Correctly
Financial institutions need to have a credit risk model in place that will be consistent and fair. This risk model must be applied to each borrower’s application, and exceptions simply cannot be made without the acceptance of an increased probability of default. Faith in the fairness of the lending system is necessary to encourage people to apply for loans and work with your lending program whenever they need to borrow money.
Your risk model will need to adjust to meet the changing times, and you need to be prepared to adjust rates up and down as needed or even create new lending policies altogether. If you’re trying to develop the perfect credit risk model, we can help.
We offer all the necessary support for your credit risk modeling process, and we can help you develop a fair, balanced, and risk-focused risk modeling process. Contact us today to improve your credit risk model.