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Activity Number:
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465
- Privacy, Confidentiality, and Disclosure Limitation
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Type:
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Contributed
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Date/Time:
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Thursday, August 6, 2020 : 10:00 AM to 2:00 PM
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Sponsor:
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Business and Economic Statistics Section
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Abstract #312313
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Title:
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A Monte Carlo Simulation Study for Reject Inference
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Author(s):
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Billie Anderson* and Mark Newman and Phil Grim and J. Michael Hardin
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Companies:
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Harrisburg University and Harrisburg University and Harrisburg University and Samford University
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Keywords:
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credit scoring;
reject inference;
Monte Carlo simulation
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Abstract:
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Credit scoring is the process of determining whether applicants should be granted a financial loan or not. When a financial institution wants to create a credit scoring model for all applicants, the institution only has the known good/bad loan outcome for the accepted applicants; this causes an inherent bias in the model. Reject inference is the process of inferring a good/bad loan outcome to the rejected applicants so that the updated model will be representative of all loan applicants, accepted and rejected. A gap in the reject inference literature is a methodology of simulating a rejected applicant. There is a need for a methodology to be developed to illustrate how to simulate a rejected applicant, so that the reject inference techniques can be studied, and appropriate reject inference techniques can be selected. This paper used peer-to-peer financial loan information from accepted and rejected applicants from Lending Club to perform a Monte Carlo simulation of rejected applicants. Using the simulated data, the researchers compare the performance pf three widely used reject inference techniques.
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Authors who are presenting talks have a * after their name.
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