Features List

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.

SAS Enterprise Miner: Award-winning predictive analytics

  • 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.

Patented optimal rigorous binning method

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

Data partition node

  • Partitions data into training, validation and/or test data sets.
  • Data sets based on simple random, cluster or stratified random sample.

Interactive grouping node

  • 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.

Scorecard node

  • 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.

Reject inference node

  • 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.

Seamless integration into SAS Credit Scoring

  • 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.