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 memorybased reasoning as a reject inference technique. SAS® Enterprise MinerTM software is used to perform the analysis. The paper discusses the technical concepts in reject inference and the methodology behind using memory-based reasoning as a reject inference technique. Several misclassification measures are reported to determine how well memory-based reasoning performs as a reject inference technique. In addition, a macro to determine how to pick the number of neighbors for the memory-based reasoning technique is given and discussed. This macro is implemented in a SAS® Enterprise MinerTM code node. OVERVIEW OF SCORECARDS Credit scorecard development is a method of modeling potential risk of credit applicants. It involves using different statistical techniques and past historical data to create a scorecard that financial institutions use to assess credit applicants in terms of risk. A scorecard model is built from a number of characteristic inputs. Each characteristic can have a number of attributes. In the example scorecard shown in Figure 1, age is a characteristic and “25<=AGE<33” is an attribute. Each attribute is associated with a number of scorecard points. These scorecard points are statistically assigned to differentiate risk, based on the predictive power of the variables, correlation between the variables, and business considerations. For example, in Figure 1, the credit application of a 32-year-old person, who owns his own home and has an annual income of $30,000 would be accepted for credit by this institution. The total score of an applicant is the sum of the scores for each attribute that is present in the scorecard. Smaller scores imply a higher risk of default, and vice versa. SAS Presents... Solutions SAS Global Forum 2010