A Monte Carlo simulation framework for reject inference

Abstract

Credit scoring is the process of determining whether applicants should be granted a financial loan. When a financial institution decides to create a credit scoring model for all applicants, the institution only has the known good/bad loan outcomes for accepted applicants. This causes inherent bias in the model We address a gap in the reject inference literature by developing a methodology to simulate rejected applicants. A methodology to illustrate how to simulate rejected applicants must be developed so that the reject inference techniques can be studied and appropriate reject inference techniques can be selected. This study uses a peer-to-peer financial loan information from accepted and rejected financial loan applicants to perform Monte Carlo simulation of rejected applicants. Using simulated data, the researchers compare the performance of three widely used reject inference techniques.