Tools for credit scorecard modeling are available in Financial Toolbox.
For information on developing credit scorecards, see Create Credit Scorecards (Financial Toolbox).
|Create creditscorecard object to build credit scorecard model|
|Perform automatic binning of given predictors|
|Return predictor’s bin information|
|Summary of credit scorecard predictor properties|
|Modify predictor’s bins|
|Set properties of credit scorecard predictors|
|Binned predictor variables|
|Plot histogram counts for predictor variables|
|Fit logistic regression model to Weight of Evidence (WOE) data|
|Fit logistic regression model to Weight of Evidence (WOE) data subject to constraints on model coefficients|
|Set model predictors and coefficients|
|Return points per predictor per bin|
|Format scorecard points and scaling|
|Compute credit scores for given data|
|Likelihood of default for given data set|
|Validate quality of credit scorecard model|
|Create compact credit scorecard|
Case Study for a Credit Scorecard Analysis (Financial Toolbox)
This example shows how to create a
bin data, display, and plot binned data information.
This example shows the workflow for creating and comparing two credit scoring models: a credit scoring model based on logistic regression and a credit scoring model based on decision trees.
This example demonstrates the hard-cutoff and fuzzy augmentation approaches to reject inference.
This example shows how to work with consumer credit panel data to create through-the-cycle (TTC) and point-in-time (PIT) models and compare their respective probabilities of default (PD).