Create and Analyze Credit Scorecards
Tools for credit scorecard modeling are available in Financial Toolbox.
For information on developing credit scorecards, see Create Credit Scorecards.
creditscorecard object to build credit scorecard
|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
|Create compact credit scorecard (Since R2019a)
- Case Study for Credit Scorecard Analysis
This example shows how to create a
creditscorecardobject, 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
- Credit Scoring Using Logistic Regression and Decision Trees
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
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)
Create, train, and compare three deep learning networks for predicting credit default probability.
- Interpret and Stress-Test Deep Learning Networks for Probability of Default
Train a credit risk for probability of default (PD) prediction using a deep neural network.
- Explore Fairness Metrics for Credit Scoring Model
Calculate and use data and model metrics to investigate the biases that exist in a model.
- Bias Mitigation in Credit Scoring by Reweighting
Use bias mitigation with a credit scorecard model to make it more fair.
- Interpretability and Explainability for Credit Scoring
This example shows different techniques for interpreting and explaining the logic behind credit scoring predictions.