lifetime models for probability of default -凯发k8网页登录

estimate loss reserves based on a lifetime analysis conditional on macroeconomic scenarios.

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.

this example shows how to compare a new logistic model for lifetime pd against a "champion" model.

this example shows how to compare three lifetime pd models using cross-validation.

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.

this example shows some differences between discrimination and calibration metrics for the validation of probability of default (pd) models.

this example shows how to work with consumer (retail) credit panel data to visualize observed probabilities of default (pds) at different levels.

train a credit risk for probability of default (pd) prediction using a deep neural network.

this example shows how to use customlifetimepdmodel to create a lifetime model for the probability of default.

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.

this example shows how to generate macroeconomic scenarios and perform expected credit loss (ecl) calculations for a portfolio of loans.