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.
|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.