Bureau data features for credit risk modeling…
When building a credit risk model, the choice of features (or variables) that are used can have a significant impact on the model’s accuracy and reliability. Here are some important bureau data features that are commonly used in credit risk modeling:
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Credit score: This is a numerical value that represents a borrower’s creditworthiness, based on their credit history. Credit scores are often used as a primary feature in credit risk models, as they are highly predictive of default risk.
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Credit utilization ratio: This is the ratio of a borrower’s credit card balances to their credit limits. High credit utilization ratios are associated with increased default risk, so this feature can be useful in predicting credit risk.
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Payment history: This feature captures whether a borrower has made their loan or credit card payments on time or not. Late payments or missed payments can be a sign of financial distress, and can be a strong predictor of default risk.
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Credit age: This feature captures how long a borrower has had credit accounts open. Borrowers with longer credit histories are generally considered less risky than those with shorter credit histories.
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Number of credit accounts: This feature captures how many credit accounts a borrower has open. Borrowers with too few credit accounts may be considered risky, as they may not have a diverse enough credit history.
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Debt-to-income ratio: This is the ratio of a borrower’s monthly debt payments to their monthly income. High debt-to-income ratios are associated with increased default risk, as borrowers may struggle to make payments on their loans.
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Loan amount: The amount of the loan itself can be an important feature, as larger loans may be riskier than smaller loans.
These are just a few examples of important bureau data features that can be used in credit risk modeling. It’s important to note that the choice of features may vary depending on the specific context and data available.