Create creditscorecard
object to build credit scorecard
model
Build a credit scorecard model by creating a
creditscorecard
object and specify input data in a table
format.
After creating a creditscorecard
object, you can use the
associated object functions to bin the data and perform logistic regression analysis
to develop a credit scorecard model to guide credit decisions. This workflow shows
how to develop a credit scorecard model.
Use screenpredictors
(Risk Management Toolbox) from Risk Management Toolbox™ to pare down a potentially large set of predictors to a subset
that is most predictive of the credit score card response variable. Use this
subset of predictors when creating the creditscorecard
object.
Create a creditscorecard
object (see Create creditscorecard and Properties).
Bin the data using autobinning
.
Fit a logistic regression model using fitmodel
or fitConstrainedModel
.
Review and format the credit scorecard points using displaypoints
and formatpoints
. At this point
in the workflow, if you have a license for Risk Management Toolbox, you have the option to create a
compactCreditScorecard
object
(csc
) using the compact
function. You can then use the following functions displaypoints
(Risk Management Toolbox), score
(Risk Management Toolbox), and probdefault
(Risk Management Toolbox)
from the Risk Management Toolbox with the csc
object.
Score the data using score
.
Calculate the probabilities of default for the data using probdefault
.
Validate the quality of the credit scorecard model using validatemodel
.
For more detailed information on this workflow, see Credit Scorecard Modeling Workflow.
creates a sc
= creditscorecard(data
)creditscorecard
object by specifying
data
. The credit scorecard model, returned as a
creditscorecard
object, contains the binning maps or
rules (cut points or category groupings) for one or more predictors.
sets Properties using
name-value pairs and any of the arguments in the previous syntax. For
example, sc
= creditscorecard(___,Name,Value
)sc =
creditscorecard(data,'GoodLabel',0,'IDVar','CustID','ResponseVar','status','PredictorVars',{'CustAge','CustIncome'},'WeightsVar','RowWeights','BinMissingData',true)
.
You can specify multiple name-value pairs.
Note
To use observation (sample) weights in the credit scorecard
workflow, when creating a creditscorecard
object,
you must use the optional name-value pair
WeightsVar
to define which column in the
data
contains the weights.
autobinning | Perform automatic binning of given predictors |
bininfo | Return predictor’s bin information |
predictorinfo | Summary of credit scorecard predictor properties |
modifypredictor | Set properties of credit scorecard predictors |
fillmissing | Replace missing values for credit scorecard predictors |
modifybins | Modify predictor’s bins |
bindata | Binned predictor variables |
plotbins | Plot histogram counts for predictor variables |
fitmodel | Fit logistic regression model to Weight of Evidence (WOE) data |
fitConstrainedModel | Fit logistic regression model to Weight of Evidence (WOE) data subject to constraints on model coefficients |
setmodel | Set model predictors and coefficients |
displaypoints | Return points per predictor per bin |
formatpoints | Format scorecard points and scaling |
score | Compute credit scores for given data |
probdefault | Likelihood of default for given data set |
validatemodel | Validate quality of credit scorecard model |
compact | Create compact credit scorecard |
[1] Anderson, R. The Credit Scoring Toolkit. Oxford University Press, 2007.
[2] Refaat, M. Data Preparation for Data Mining Using SAS. Morgan Kaufmann, 2006.
[3] Refaat, M. Credit Risk Scorecards: Development and Implementation Using SAS. lulu.com, 2011.
autobinning
| bindata
| bininfo
| displaypoints
| fillmissing
| fitConstrainedModel
| fitmodel
| formatpoints
| modifybins
| modifypredictor
| plotbins
| predictorinfo
| probdefault
| score
| setmodel
| table
| validatemodel
| screenpredictors
(Risk Management Toolbox)