The statements made and views expressed herein are solely those of the authors and do not necessarily represent o¢ cial policies, statements, or views of the O¢ ce of the Comptroller of the Currency or its sta¤ or of Fannie Mae or its sta¤. Acknowledgement: We are grateful to our colleagues for many helpful comments and discussions, and especially to Regina Villasmil, curator of the OCC/RAD consumer credit database. 2 U.S. Department of the Treasury, O¢ ce of the Comptroller of the Currency, Risk Analysis Division.

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Lenders use rating and scoring models to rank credit applicants on their expected performance. The models and approaches are numerous. We explore the possibility that estimates generated by models developed with data drawn solely from extended loans are less valuable than they should be because of selectivity bias. We investigate the value of "reject inference"-methods that use a rejected applicant's characteristics, rather than loan performance data, in scoring Disclaimer: Portions of this paper were completed while Larson was employed by the O¢ ce of the Comptroller of the Currency. Views expressed herein are those of the authors, and do not necessarily represent the views of Fannie Mae, the O¢ ce of the Comptroller of the Currency, the U.S. Department of the Treasury, or their sta¤ s. Acknowledgements: We thank colleagues and workshop participants at Cornell University, the OCC and Syracuse University, as well as the referees, for helpful comments. 1 model development. In the course of making this investigation, we also discuss the advantages of using parametric as well as nonparametric modeling. These issues are discussed and illustrated in the context of a simple stylized model.