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Development of Credit Scoring Applications Using SAS Enterprise Miner - CSEM71

This course teaches learners how to build a credit scorecard, from start to finish, using SAS Enterprise Miner 7.1 and the methodology recommended by leading credit and financial experts.

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1 day - Class room

Learn how to:

  • use the SAS Enterprise Miner Interactive Grouping node to select the predictive variables using Information Value and calculate Weight of Evidence values
  • use the SAS Enterprise Miner Scorecard node to build a preliminary scorecard using the appropriate scaling methodology
  • perform reject inference techniques such as hard cut-off augmentation, parceling, and fuzzy augmentation using the SAS Enterprise Miner Reject Inference node in order to augment the scorecard by using rejected applicants
  • determine how well the scorecard performs using scorecard diagnostic tools such as ROC and Lift charts.

Who should attend:

Risk analysts, credit modelers, credit scorecard developers, credit managers, credit analysts, and business analysts in banks and other financial institutions who are responsible for development of scorecards and credit-scoring applications


Before attending this course, learners should have a working knowledge of the statistics of finance and scorecard development, as well as basic skills using SAS Enterprise Miner. Learners can gain knowledge of scorecard development by completing the Credit Scorecard Development and Implementation course. For skills using SAS Enterprise Miner, Learners should complete the Applied Analytics Using SAS Enterprise Miner course or have equivalent skills.

Course Contents:

Scorecard Development Using SAS Enterprise Miner

  • credit scoring background
  • the scorecard development process using SAS Enterprise Miner

Accessing and Preparing Data for Scorecard Development

  • creating a SAS Enterprise Miner project and diagram
  • defining a data source
  • developmental and validation data sets

SAS Enterprise Miner Interactive Grouping Node

  • initial characteristic analysis
  • interval variable binning options (self-study)
  • special code options (self-study)
  • grouping options (self-study)

Scorecard Node

  • scorecard development
  • adverse characteristics (self-study)

Reject Inference Node

  • Reject Inference techniques using SAS Enterprise Miner
  • Reject Inference Property Panel options (self-study)

Software Addressed

This course addresses the following software product(s): SAS for Enterprise Risk Management, SAS Enterprise Miner.

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