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Credit risk is the likelihood that a borrower will default on a loan or credit obligation. It refers to the potential financial loss that a lender faces when a borrower fails to repay the loan according to the agreed terms.
Credit risk modeling is the process of using statistical techniques and machine learning to assess this risk. The models use past data and various other factors to predict the probability of default and inform credit decisions.
This is part of a series of articles about machine learning for business.
The 2008 financial crisis demonstrated the importance of effective credit risk modeling. The crisis was largely caused by the widespread failure of financial institutions to properly manage their credit risk. Poor credit decisions and a lack of effective risk management practices led to the widespread default of subprime mortgages, which ultimately triggered the global financial crisis.
Credit risk modeling is crucial for financial institutions for several reasons:
Credit risk modeling faces several challenges and limitations, including:
Lenders usually consider various factors when evaluating credit risks and determining the terms of a loan:
Probability of Default (POD) is a measure of the likelihood that a borrower will default on a loan or credit obligation. It is expressed as a percentage or a decimal, and represents the estimated risk of default for a particular borrower. The POD is calculated using statistical models that consider various factors such as the borrower’s credit history, income, and payment behavior.
Financial institutions use POD to inform credit decisions, set loan terms and interest rates, and manage their overall risk exposure. For example, the lender might demand higher collateral from a riskier borrower.
Loss Given Default (LGD) is a measure of the expected financial loss that a lender will incur if a borrower defaults on a loan or credit obligation. It is expressed as a percentage of the loan amount and represents the amount of the loan that is expected to be unrecovered in the event of default.
LGD takes into account various factors such as the remaining balance on the loan, the collateral value, and the recovery process. For example, someone who borrows $5,000 will present a much lower LGD than someone who borrows $500,000, even if the second borrower has a higher credit ranking.
Exposure at Default (EAD) is a measure of the outstanding loan amount that a lender is exposed to in the event of a borrower defaulting on a loan or credit obligation. It represents the maximum potential loss that a lender could incur in the event of default and is used to estimate the potential impact of a default on the lender’s financial position.
This type of modeling uses statistical techniques to assign a credit score to a borrower, which reflects their creditworthiness. It is commonly used by lenders to determine the terms and conditions of a loan, such as interest rate and loan amount. Scorecard models use a variety of factors, such as credit history, income, and debt-to-income ratio, to calculate a credit score.
This type of modeling uses statistical techniques to identify the factors that contribute to a borrower’s credit risk. It helps financial institutions understand the drivers of credit risk and make informed lending decisions. Discriminant analysis models use a combination of factors, such as income, debt-to-income ratio, and credit history, to determine the likelihood of default.
This type of modeling uses a tree-based approach to predict the likelihood of a borrower defaulting on their loan. It is useful for visualizing the relationships between different factors and the outcome of default. Decision tree models use a series of branching rules to determine the likelihood of default based on the values of various predictor variables.
This type of modeling uses an ensemble of decision trees to predict the likelihood of a borrower defaulting on their loan. It is known for its high accuracy and ability to handle complex data sets. Random forest models use multiple decision trees, each of which is based on a random subset of the data, to make predictions about the likelihood of default.
This type of modeling uses an iterative process to improve the accuracy of predictions about a borrower’s likelihood of default. It is commonly used for high-stakes applications, such as credit risk modeling, due to its high accuracy and ability to handle large, complex data sets. Gradient boosting models iteratively build decision trees and adjust the weights of the predictor variables to improve the accuracy of predictions.
There are several best practices of credit risk modeling, including:
ML observability ensures models are performing as intended and any potential issues or biases are identified and addressed promptly. This makes it an essential component of credit risk modeling, as financial institutions need to be able to explain the rationale behind their decisions to regulators and customers.
Our ML observability platform is the ideal partner for Data Scientists and ML engineers to visualize, monitor, explain, and improve ML models in production in minutes. Our platform supports any use case and fits naturally into your existing ML stack alongside your favorite MLOps tools. We empower organizations with key features and tools to ensure high model performance:
Root Cause Investigation
To get a hands-on feel for aporia’s ML observability platform, we recommend: