Multiple Imputation as a Missing Data Approach to Reject Inference on Consumer Credit Scoring
by David J. Fogarty.
This paper analyzes the importance of using proper techniques for the reject inference to develop consumer credit scoring. The focus is treating reject inference as a missing data problem and using model-based imputation techniques as a way to enhance the information inferred from the rejects over that of traditional approaches when developing credit scorecards. An overview and comparison of the standard missing data approaches to reject inference are provided. Multiple imputation is also discussed as a method of reject inference which can potentially reduce some of the biases which can occur from using some of the traditional missing data techniques. A quantitative analysis is then provided to confirm the hypothesis that model-based multiple imputation is an enhancement over traditional missing data approaches to reject inference.
Multiple Imputation, Missing Data, Analysis of Missing Data, Reject Inference, Consumer Credit Scoring
David J Fogarty, davidF1967@email.uophx.edu
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