Improving Credit Risk Scorecards With Memory-Based Reasoning to Reject Inference With SAS Enterprise Miner
Many business elements are used to develop credit scorecards. Reject inference, related to the issue of sample bias, is one of the key processes required to build relevant application scorecards and is vital in creating successful scorecards. Reject inference is used to assign a target class (that is, a good or bad designation) to applications that were rejected by the financial institution and to applicants who refused the financial institution's offer. This paper uses real-world data to present an example of using memory-based reasoning as a reject inference technique. SAS Enterprise Miner software is used to perform the analysis.