Credit Scoring for SAS® Enterprise Miner^™ Features List

Credit Scoring for SAS^® Enterprise Miner^™

Flexible data preparation and management

SAS data access, integration and management capabilities make it easier to prepare data across disparate systems and sources for analysis.
Credit risk models generalize well and produce superior outcomes.
Comprehensive variable selection techniques help lead to better credit risk modeling.

The most comprehensive set of advanced predictive and descriptive modeling algorithms, including scorecard, decision trees, neural networks, logistic regression, etc.
Assesses scorecard quality to determine the best course of action.

The SAS patented optimal rigorous binning method yields true optimal bins based on constraints defined by the user.

Credit scorecard and screening and binning of univariate characteristics.
Purposeful censoring of data and assessments on the strength of each characteristic individually as a predictor of performance.
Use of predefined groupings.
Prebinning through quantile or bucket methods.
Interval binning through optimal criterion, quantile, monotonic event rate and constrained optimal methods.
Interactive bin changes.
Supports interval targets.
Variable selection through Gini statistic or information value.
Response rate plots.
Gini statistic and information value plots.
Output of variable mappings.

Fits a logistic regression model and computes the scorecard points for each attribute.
Choose either the WOE variables or the group variables that are exported by the Interactive Grouping node as inputs for the logistic regression model.
Scorecard points of each attribute based on the coefficients of the logistic regression model.
Manually assign scorecard points to attributes.
Scaling of scorecard points, controlled by three Scaling Options properties.
Intercept-based scorecard.
Reverse scorecard.
Use of indeterminate values in gains charts.
Adverse characteristic analysis through neutral score or weighted average score methods.
Inputs for trade-off plots.
Score distribution charts.
Strategy curve.
Event frequency chart.
Scorecard strength chart.
Selection methods include backward, forward and stepwise.
Regression criteria include AIC, SBC, validation error, validation misclassification rate, cross-validation error, and cross-validation misclassification rate.

Provides remedies for the sample/population data selection bias through an augmented data set that represents the "through-the-door" population; this serves as training data set for a second scorecard model.
Inference methods include fuzzy, hard cutoff and parceling.
Classification charts of actual versus inferred.
Distribution plots of actual versus inferred.
Summary statistics of actual versus inferred.

SAS Credit Scoring is a comprehensive solution for developing, deploying and managing scorecards for operational and regulatory compliance that includes data management at the front end and model management at the back end.