Main Content

Create Credit Scorecards

Credit scorecard modeling, binning, fitting a model, obtaining points and scores, model validation, probability of default, create compact scorecard

For information about the workflow for developing credit scorecards, see Credit Scorecard Modeling Workflow.

Objects

creditscorecardCreate creditscorecard object to build credit scorecard model

Functions

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

Examples and How To

Feature Screening with screenpredictors (Risk Management Toolbox)

This example shows how to perform predictor screening using screenpredictors (Risk Management Toolbox).

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 Scorecards with Constrained Logistic Regression Coefficients

To compute scores for a creditscorecard object with constraints for equality, inequality, or bounds on the coefficients of the logistic regression model, use fitConstrainedModel.

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 (Risk Management Toolbox)

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 (Risk Management Toolbox)

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

Credit Rating by Bagging Decision Trees

This example shows how to build an automated credit rating tool.

compactCreditScorecard Object Workflow (Risk Management Toolbox)

This example shows a workflow for creating a compactCreditScorecard object from a creditscorecard object.

Impute Missing Data in the Credit Scorecard Workflow Using the k-Nearest Neighbors Algorithm

This example shows how to perform imputation of missing data in the credit scorecard workflow using the k-nearest neighbors (kNN) algorithm.

Impute Missing Data in the Credit Scorecard Workflow Using the Random Forest Algorithm

This example shows how to perform imputation of missing data in the credit scorecard workflow using the random forest algorithm.

Treat Missing Data in a Credit Scorecard Workflow Using MATLAB® fillmissing

This example shows a workflow to gather missing data, manually treat the training data, develop a new creditscorecard, and treat new data before scoring using the MATLAB® fillmissing.

Concepts

Credit Scorecard Modeling Workflow

Use the credit scorecard workflow to create, model, and analyze credit scorecards.

About Credit Scorecards

The goal of credit scoring is ranking borrowers by their credit worthiness.

Credit Scorecard Modeling Using Observation Weights

Use observation weights with the credit scorecard workflow to create, model, and analyze credit scorecards.

Troubleshooting

Troubleshooting Credit Scorecard Results

Troubleshooting results when using a creditscorecard model.