create and analyze credit scorecards -凯发k8网页登录

credit scorecard modeling, binning, fitting a model, obtaining points and scores, model validation, probability of default using financial toolbox™

tools for credit scorecard modeling are available in financial toolbox.

for information on developing credit scorecards, see .

objects

create creditscorecard object to build credit scorecard model

	perform automatic binning of given predictors
	return predictor’s bin information
	summary of credit scorecard predictor properties
	replace missing values for credit scorecard predictors
	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
	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

this example shows how to create a creditscorecard object, bin data, display, and plot binned data information.
this example shows alternative workflows to handle missing values when working with creditscorecard objects.
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.
this example demonstrates the hard-cutoff and fuzzy augmentation approaches to reject inference.
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).
(deep learning toolbox)
create, train, and compare three deep learning networks for predicting credit default probability.
train a credit risk for probability of default (pd) prediction using a deep neural network.
calculate and use data and model metrics to investigate the biases that exist in a model.
use bias mitigation with a credit scorecard model to make it more fair.
this example shows different techniques for interpreting and explaining the logic behind credit scoring predictions.