Credit Scoring Papers and Presentations

Creating Interval Target Scorecards with Credit Scoring for SAS Enterprise Miner
Maldonado, Miguel; Haller, Susan; Czika, Wendy; Siddiqi, Naeem; SAS Institute, Inc. 2013
This paper discusses several new methods available in Credit Scoring for SAS Enterprise Miner that help build scorecards that are based on interval targets.

Building Loss Given Defaults Scorecard Using Weight of Evidence Bins in SAS Enterprise Miner
Van Berkel, Anthony, Bank of Montreal; Siddiqi, Naeem, SAS Institute, Inc. 2012
The Credit Scoring add-on in SAS Enterprise Miner is widely used to build binary target (good, bad) scorecards for probability of default. The process involves grouping variables using weight of evidence, and then performing logistic regression to produce predicted probabilities. This paper will demonstrate how to use the same tools to build binned variable scorecards for Loss Given Default, explaining the theoretical principles behind the method and use actual data to demonstrate how it was done.

Improving Credit Risk Scorecards with Memory-Based Reasoning to Reject Inference with SAS Enterprise Miner
Anderson, Billie; Haller, Susan; Siddiqi, Naeem; Cox, James; Duling, David ; SAS Institute, Inc. 2010
The paper discusses the technical concepts in reject inference and the methodology behind using memory-based reasoning as a reject inference technique.

Reject Inference Techniques Implemented in Credit Scoring for SAS Enterprise Miner
Anderson, Billie; Haller, Susan; Siddiqi, Naeem; SAS Institute, Inc. 2009
This paper discusses the technical concepts in reject inference and the methodology behind the reject inference algorithms that are available in Credit Scoring for SAS Enterprise Miner.

SAS/OR: Rigorous Constrained Optimized Binning for Credit Scoring
Oliveira, Ivan; Chari, Manoj; Haller, Susan; SAS Institute, Inc. 2008
This paper presents a new process that enhances the formulation and solution approach in the SAS system during the so-called “binning” phase by exploiting SAS/OR optimization capabilities to approach the problem from a mathematically rigorous perspective.

Papers are in Portable Document Format (PDF) and can be viewed with the free Adobe Acrobat Reader.

Powerpoint presentations and SAS programs can be downloaded as zip files.