Building Credit Scorecards Using Credit Scoring for SAS Enterprise Miner

Building Credit Scorecards Using Credit Scoring for SAS(R) Enterprise Miner(TM)

Building Credit Scorecards Using Credit Scoring for SAS® Enterprise MinerTM

Reject Inference Techniques Implemented in Credit Scoring for SAS® Enterprise MinerTM

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...

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...

Matrix Sequential Hybrid Credit Scorecard Based on Logistic Regression and Clustering

The Basel II Accord pointed out benefits of credit risk management through internal models to estimate Probability of Default (PD). Banks use default predictions to estimate the loan applicants’ PD. However, in practice, PD is not useful and banks applied credit scorecards for their decision making process. Also the competitive pressures in lending industry forced banks to use profit scorecards...