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Create and Analyze Credit Scorecards

Credit scorecard modeling, binning, fitting a model, obtaining points and scores, model validation, probability of default using Financial Toolbox™

Tools for credit scorecard modeling are available in Financial Toolbox.

For information on developing credit scorecards, see Create Credit Scorecards.


creditscorecardCreate creditscorecard object to build credit scorecard model


autobinningPerform automatic binning of given predictors
bininfoReturn predictor’s bin information
predictorinfoSummary of credit scorecard predictor properties
fillmissingReplace missing values for credit scorecard predictors
modifybinsModify predictor’s bins
modifypredictorSet properties of credit scorecard predictors
bindataBinned predictor variables
plotbinsPlot histogram counts for predictor variables
fitmodelFit logistic regression model to Weight of Evidence (WOE) data
fitConstrainedModelFit logistic regression model to Weight of Evidence (WOE) data subject to constraints on model coefficients
setmodelSet model predictors and coefficients
displaypointsReturn points per predictor per bin
formatpointsFormat scorecard points and scaling
scoreCompute credit scores for given data
probdefaultLikelihood of default for given data set
validatemodelValidate quality of credit scorecard model
compactCreate compact credit scorecard


Case Study for a Credit Scorecard Analysis

This example shows how to create a creditscorecard object, bin data, display, and plot binned data information.

Credit Scorecard Modeling with Missing Values

This example shows alternative workflows to handle missing values when working with creditscorecard objects.

Comparison of Credit Scoring Using Logistic Regression and Decision Trees

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.

Use Reject Inference Techniques with Credit Scorecards

This example demonstrates the hard-cutoff and fuzzy augmentation approaches to reject inference.

Compare Probability of Default Using Through-the-Cycle and Point-in-Time Models

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).

Compare Deep Learning Networks for Credit Default Prediction (Deep Learning Toolbox)

This example shows how to create, train, and compare three deep learning networks for predicting credit default probability.

Interpret and Stress-Test Deep Learning Networks for Probability of Default

This example shows how to train a credit risk for probability of default (PD) prediction using a deep neural network.