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
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
, score
, and probdefault
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.
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 |
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
| fitConstrainedModel
| fitmodel
| formatpoints
| modifybins
| modifypredictor
| plotbins
| predictorinfo
| probdefault
| score
| screenpredictors
| setmodel
| table
| validatemodel