Create Credit Scorecards
|Perform automatic binning of given predictors|
|Return predictor’s bin information|
|Summary of credit scorecard predictor properties|
|Replace missing values for credit scorecard predictors (Since R2020a)|
|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 (Since R2019a)|
|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|
|Screen credit scorecard predictors for predictive value (Since R2019a)|
|Create compact credit scorecard (Since R2019a)|
Live Editor Tasks
|Thresholds for Screen Predictors||Select thresholds for predictor risk metrics in the Live Editor (Since R2021b)|
Examples and How To
- Feature Screening with screenpredictors (Risk Management Toolbox)
This example shows how to perform predictor screening using screenpredictors and then set predictor thresholds using the Threshold Predictors live task.
- Case Study for Credit Scorecard Analysis
This example shows how to create a
creditscorecardobject, bin data, display, and plot binned data information.
- Bin Data to Create Credit Scorecards Using Binning Explorer (Risk Management Toolbox)
Create a credit scorecard using the Binning Explorer app.
- Credit Scorecards with Constrained Logistic Regression Coefficients
To compute scores for a
creditscorecardobject with constraints for equality, inequality, or bounds on the coefficients of the logistic regression model, use
- Credit Scorecard Modeling with Missing Values
This example shows alternative workflows to handle missing values when working with
- Credit Scoring Using Logistic Regression and Decision Trees (Risk Management Toolbox)
Create and compare two credit scoring models, one based on logistic regression and the other 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
compactCreditScorecardobject from a
- 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®
- Explore Fairness Metrics for Credit Scoring Model (Risk Management Toolbox)
Calculate and use data and model metrics to investigate the biases that exist in a model.
- Bias Mitigation in Credit Scoring by Reweighting (Risk Management Toolbox)
Use bias mitigation with a credit scorecard model to make it more fair.
- Bias Mitigation in Credit Scoring by Disparate Impact Removal (Risk Management Toolbox)
Use disparate impact removal as a pre-processing technique in bias mitigation to a credit scorecard model to reduce bias in the model.
- Interpretability and Explainability for Credit Scoring (Risk Management Toolbox)
This example shows different techniques for interpreting and explaining the logic behind credit scoring predictions.
- 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.
Troubleshoot results when using a