Consumer Credit Risk

Stress Testing of Consumer Credit Default Probabilities Using Panel Data

Use consumer credit panel data to visualize default rates, fit a model to predict probabilities of default, and perform a stress testing analysis.

Model Loss Given Default

Fit different types of models to loss given default (LGD) data. This example demonstrates the following approaches:

Modeling Probabilities of Default with Cox Proportional Hazards

Work with consumer (retail) credit panel data to visualize observed probabilities of default (PDs) at different levels. It also shows how to fit a Cox proportional hazards (PH) model, also known as Cox regression, to predict PDs. In addition, it shows how to perform a stress-testing analysis, how to model lifetime PDs, and how to calculate the lifetime expected credit loss (ECL) value using portfolioECL.

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

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

Create, train, and compare three deep learning networks for predicting credit default probability.

(Deep Learning Toolbox)

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. The example also shows how to use the locally interpretable model-agnostic explanations (LIME) and Shapley values interpretability techniques to understand the predictions of the model. In addition, the example analyzes model predictions for out-of-sample values and performs a stress-testing analysis.

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.

Bias Mitigation in Credit Scoring by Disparate Impact Removal

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

Different techniques for interpreting and explaining the logic behind credit scoring predictions.

Credit Rating by Ordinal Multinomial Regression

Use ordinal multinomial logistic regression to build a credit rating model that you can use in an automated credit rating process.