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Two Harrisburg University of Science and Technology faculty members are among the co-authors of a study examining credit scoring models.

The article, A Monte Carlo simulation framework for reject inference, appears in the Journal of the Operational Research Society, DOI: 10.1080/01605682.2022.2057819. 

Philip Grim II, instructor of Computer Science, and Mark Newman, corporate faculty for Analytics, co-authored the article with Dr. Billie Anderson, University of Missouri Kansas City (UMKC), and J. Michael Hardin, Samford University, Birmingham, AL.

A copy of the study is found online at,

The study notes that 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.

The researchers 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.


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