Lifetime Models for Probability of Default

Estimate loss reserves based on lifetime analysis

Develop and validate Lifetime models for probability of default (PD) based on a lifetime analysis conditional on macroeconomic scenarios. Calculate the estimated loss reserves using Expected Credit Loss (ECL) calculator.

Functions

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PD Functions

`fitLifetimePDModel`	Create specified lifetime PD model object type (Since R2020b)
`predict`	Compute conditional PD (Since R2020b)
`predictLifetime`	Compute cumulative lifetime PD, marginal PD, and survival probability (Since R2020b)
`modelDiscrimination`	Compute AUROC and ROC data (Since R2020b)
`modelDiscriminationPlot`	Plot ROC curve (Since R2021a)
`modelCalibration`	Compute RMSE of predicted and observed PDs on grouped data (Since R2023a)
`modelCalibrationPlot`	Plot observed default rates compared to predicted PDs on grouped data (Since R2023a)
`discardResiduals`	Discard residual information of underlying Cox model (Since R2023a)

Lifetime Expected Credit Loss (ECL) Calculator

portfolioECL Compute the lifetime ECL at individual or portfolio level (Since R2022a)

Objects

`Logistic`	Create `Logistic` model object for lifetime probability of default (Since R2020b)
`Probit`	Create `Probit` model object for lifetime probability of default (Since R2020b)
`Cox`	Create `Cox` model object for lifetime probability of default (Since R2021b)
`customLifetimePDModel`	Create `customLifetimePDModel` object for lifetime probability of default (Since R2022b)

Topics

Overview of Lifetime Probability of Default Models
Estimate loss reserves based on a lifetime analysis conditional on macroeconomic scenarios.
Basic Lifetime PD Model Validation
This example shows how to perform basic model validation on a lifetime probability of default (PD) model by viewing the fitted model, estimated coefficients, and p-values.
Compare Logistic Model for Lifetime PD to Champion Model
This example shows how to compare a new Logistic model for lifetime PD against a "champion" model.
Compare Lifetime PD Models Using Cross-Validation
This example shows how to compare three lifetime PD models using cross-validation.
Expected Credit Loss Computation
This example shows how to perform expected credit loss (ECL) computations with portfolioECL using simulated loan data, macro scenario data, and an existing lifetime probability of default (PD) model.
Compare Model Discrimination and Model Calibration to Validate of Probability of Default
This example shows some differences between discrimination and calibration metrics for the validation of probability of default (PD) models.
Modeling Probabilities of Default with Cox Proportional Hazards
This example shows how to work with consumer (retail) credit panel data to visualize observed probabilities of default (PDs) at different levels.
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.
Create Custom Lifetime PD Model for Credit Scorecard Model with Function Handle
This example shows how to use customLifetimePDModel to create a lifetime model for the probability of default.
Create Custom Lifetime PD Model for Decision Tree Model with Function Handle
This example shows how to fit a decision tree model for credit scoring and then use the customLifetimePDModel object to create a lifetime model for probability of default.
Incorporate Macroeconomic Scenario Projections in Loan Portfolio ECL Calculations
This example shows how to generate macroeconomic scenarios and perform expected credit loss (ECL) calculations for a portfolio of loans.
Create Weighted Lifetime PD Model
This example shows how to use fitLifetimePDModel to create a PD model using weighted credit and macroeconomic data.

Featured Examples

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.

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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.

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Create Custom Lifetime PD Model for Credit Scorecard Model with Function Handle

Use customLifetimePDModel to create a lifetime model for the probability of default. Using a retail credit data in panel format, you can create a credit scorecard model and then use a function handle with customLifetimePDModel to create a lifetime PD model.

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Create Custom Lifetime PD Model for Decision Tree Model with Function Handle

Fit a decision tree model for credit scoring and then use the customLifetimePDModel object to create a lifetime model for probability of default.

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Incorporate Macroeconomic Scenario Projections in Loan Portfolio ECL Calculations

Generate macroeconomic scenarios and perform expected credit loss (ECL) calculations for a portfolio of loans. The workflow in this example shows important computational steps required to estimate provisions for credit losses following regulations such as IFRS 9 or CECL. In practice, the determination of loan provisions is a much more involved operational program requiring the collaboration of multiple departments in an institution. The goal of this example is to show useful computational tools that can support this process.

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Create Weighted Lifetime PD Model

Use fitLifetimePDModel to create a PD model using weighted credit and macroeconomic data.

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