Credit risk is the chance that a borrower will default in making a loan or debt repayment, which may result in tremendous financial loss to the lender. This risk is very important in the context of financial institutions (FIs) making effective lending decisions. There are quantitative models that are grounded on numerical data, but the qualitative credit risk models are grounded on subjective judgment and non-numerical aspects.
This paper will cover qualitative credit risk models, their types, and the main factors that affect the credit risk evaluation.
What Are Qualitative Credit Risk Models?
Qualitative credit risk models are frameworks used by financial managers to assess the likelihood of a borrower defaulting on a loan without relying heavily on hard financial data. Rather, such models focus on subjective information, including the reputation of the borrower, the business climate, and the economy. The factors assist the lenders in making informed decisions despite the lack of detailed financial information.
Qualitative models for credit analysis can be divided into borrower-specific and market-specific factors, both of which play a significant role in determining creditworthiness.
Types of Qualitative Credit Risk Models
1. Expert Judgment Model
This model is the subjective evaluation of the borrower repaying the loan on the basis of the previous interactions, reputation, and other qualitative issues. The risk manager of the financial institution (FI) assesses the creditworthiness of the borrower and does it based on personal judgment, experience, and intuition.
2. Rating Models
Most financial institutions have internally created rating models that are based on qualitative ratings of the borrowers. Such ratings can be rated as excellent to high-risk, with subjective ratings of the characteristics of the borrower, the market environment, and other qualitative aspects.
3. Expert Systems
As technology increased, most FIs have come up with expert systems that can make decisions automatically by factoring in qualitative aspects. Such systems apply set rules and algorithms to mimic the decisions made by an expert and assess the risk of default by borrowers.
Borrower-Specific Factors for Credit Risk Analysis
These are individual factors specific to the borrower that influence the credit risk assessment.
1. Reputation
One of the most important borrower-specific factors is the reputation of the borrower. A borrower with a long history of making his/her repayments and good business practices is considered more reliable. When a borrower is reputed to be a good person in terms of paying his or her financial obligations, the borrower is deemed unlikely to default on the loan.
2. Leverage
A borrower’s leverage, or the ratio of debt to equity, directly impacts their ability to repay debt. Highly leveraged borrowers (those with high levels of debt relative to equity) may struggle to meet their repayment obligations. This makes them riskier borrowers, as a significant portion of their income goes toward servicing debt.
3. Collateral
This is something that is given by the borrower as a guarantee to the loan. Collateral loans are also less risky to the lender as the collateral can be sold in case of default by the borrower. The less valuable and less accessible the collateral, the less the risk to the lender.
4. Volatility of Earnings
The volatility of the earnings of borrower, or changes in the stream of his or her income, is also an important factor in deciding the capacity of borrower to repay loans. Borrowers who have very fluctuating income might struggle to make constant interest and principal payments. Borrowers who have a steady income, on the other hand, are considered to be less risky.
Market-Specific Factors for Credit Risk Analysis
These are more macroeconomic and market factors that influence the probability of default by the borrower.
1. The Business Cycle
The state of the economy plays a crucial role in credit risk. During a recession, businesses in certain sectors, like consumer durables (cars, refrigerators), tend to experience lower demand, increasing their risk of default. Financial institutions may adjust their lending criteria in response to changes in the business cycle.
2. Interest Rates
Interest rates level affects the cost of borrowing and may affect default risk. The interest rates will be high thus making borrowing costly and some borrowers may find it difficult to repay. On the other hand, low-interest rates may decrease the cost of borrowing and enhance the ability to repay.
3. Economic Environment
Aboriginal macroeconomic forces like inflation, unemployment levels, and currency changes also influence the repayment of a borrower. As an illustration, a borrower in a nation with high inflation risks will have an increased cost of operations, hence may have trouble servicing the loan.
4. Industry Conditions
The creditworthiness of a borrower is also dependent on the conditions of the industry in which he/ she is operating. An example is a borrower in a competitive or volatile industry can be considered to be at higher risk of credit because of uncertainty of their future profitability. Conversely, borrowers in non-cyclical industries (e.g. utilities or food production) may be less risky.
Conclusion
In credit risk evaluation, qualitative models provide valuable insights that complement quantitative assessments. By considering both borrower-specific and market-specific factors, financial institutions can make well-rounded, informed decisions about lending. These models rely on a blend of subjective judgment, experience, and market knowledge, helping to identify potential risks even in the absence of detailed financial data.
As a beginner-friendly guide, we’ve covered the basics of qualitative credit risk models, focusing on the key factors involved in assessing credit risk. Understanding these qualitative aspects is essential for lenders to reduce default risks and maintain a healthy loan portfolio.