What Is Credit Scoring for SAS Enterprise Miner? :: Developing Credit Scorecards Using Credit Scoring for SAS(R) Enterprise Miner(TM) 13.1

Credit scoring is the set of decision models and their underlying techniques that aid lenders in the granting of consumer credit. These techniques describe who should get credit, how much credit they should receive, and which operational strategies will enhance the profitability of the borrowers to the lenders (Thomas, Edelman, and Crook 2002).

Credit Scoring, as defined by SAS, includes the following:

applying a statistical model to assign a risk score to a credit application or an existing credit account
building the statistical model
monitoring the accuracy of one or more statistical models
monitoring the effect that score-based decisions have on key business performance indicators

Although credit scoring is not as glamorous as pricing exotic financial derivatives, it is one of the most successful applications of statistical and operations research techniques in finance and banking. Without an accurate and automated risk assessment tool, the phenomenal growth of consumer credit would not have been possible over the past 40 years (Thomas, Edelman, and Crook 2002).

In its simplest form, a scorecard is built from a number of characteristics (that is, input or predictor variables). Each characteristic includes a number of attributes. For example, age is a characteristic, and “25-33” is an attribute. Each attribute is associated with a number of scorecard points. These scorecard points are statistically assigned to differentiate risk, based on the predictive power of the characteristic variables, correlation between the variables, and business considerations.

For example, using the Example Scorecard in Figure 1.1, an applicant who is 35, makes $38,000, and is a homeowner would be accepted for credit by this financial institution’s scorecard. The total score of an applicant is the sum of the scores for each attribute that is present in the scorecard. Lower scores imply a higher risk of default, and higher scores imply a lower risk of default.

Credit Scoring for SAS Enterprise Miner contains the following nodes, which are added to your SAS Enterprise Miner toolbar to support scorecard development:

Interactive Grouping — groups input variables into bins before the credit scorecard is built. An initial, automatic grouping can provide optimal splits, but this node enables you to regroup the variables through an interactive interface. It also has the capability to screen or select variables.
Scorecard — uses the grouped variables as inputs in a logistic regression model and usually follows the Interactive Grouping node. In addition, it scales the regression parameters to compute score points and the resulting scorecard. Finally, the Scorecard node performs score and characteristic (variable) analysis that helps in understanding the scorecard, and aids in crafting a score-based strategy.
Reject Inference — offers three standard, industry-accepted methods for inferring the performance of the rejected applicant data by the use of a model that is built on the accepted applicants.
Credit Exchange — enables the use of scorecards in SAS Credit Risk for Banking. Because it plays no part in the development of the scorecard, coverage of this node is beyond the scope of this tutorial.