Credit Scoring for SAS^® Enterprise Miner^™

Shorten your data prep time.

Save time and resources by accessing, transforming, cleansing and preparing all prerequisite data – including third-party bureau, application, bill-payment and collections data. Quickly and easily examine data sets of all sizes for patterns, anomalies and missing values. Built-in, interactive nodes offer many options for exploration, transformation, missing value imputation, outlier analysis and correlation analysis.

Develop scorecards quickly and easily.

Create and deploy credit scorecards for virtually all types of consumer lending products – accounts, cards, loans, mortgages – leading to better credit decisions and reduced losses. You can compute scorecard points for each attribute using either the WOE variables or the group variables that are exported as inputs for the logistic regression model, and you can manually assign scorecard points to attributes.

Understand relationships and behaviors.

Our solution purposely censors the data so you can more easily understand relationships and model nonlinear dependencies with linear models. This gives you control over the development process and provides insights into the behavior of risk predictors. The node also screens characteristics so that potentially predictive variables are used while other variables are not.

Automatically create target variables.

As a remedy for selection bias, unrealistic expectations and model overconfidence, we offer three industry-accepted ways to infer rejected data – fuzzy augmentation, parceling and hard cutoff. You can quickly make more robust estimates on how the credit risk model performs on both the known population and the entire "through-the-door" population.

• Flexible data preparation and management.
• Award-winning predictive analytics.
• Patented optimal rigorous binning method.
• Data partition node.

• Interactive grouping node.
• Scorecard node.
• Reject inference node.
• Seamless integration into SAS Credit Scoring for Banking.