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Related Experiment Video

Updated: Sep 8, 2025

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

Published on: August 22, 2018

Reject inference methods in credit scoring.

Adrien Ehrhardt^1,2,3, Christophe Biernacki^2,3, Vincent Vandewalle^2,4

¹Groupe de Recherche Opérationnelle, Groupe Crédit Agricole, Montrouge, France.

Journal of Applied Statistics

|June 16, 2022

Summary

This summary is machine-generated.

Loan scoring models trained on accepted applicants may be biased. This study formalizes reject inference methods, revealing hidden assumptions and incomplete modeling, suggesting careful application and further research into model-based approaches.

Keywords:

Reject inference credit risk data augmentation scorecard scoring semi-supervised learning

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Area of Science:

Statistics
Machine Learning
Credit Risk Analysis

Background:

Credit scoring models are crucial for loan granting.
Models are typically trained on data excluding rejected applicants.
This creates a discrepancy between training and deployment populations.

Purpose of the Study:

To formalize assumptions of common reject inference methods.
To evaluate existing methods under a unified missing data framework.
To assess the performance of these methods on simulated and real data.

Main Methods:

Formalization of reject inference methods within a missing data framework.
Performance assessment using simulated datasets.
Empirical evaluation on real-world loan application data from CACF.

Main Results:

Existing reject inference methods often rely on incomplete hidden modeling.
No single method demonstrated uniform dominance across all scenarios.
Theoretical formalization highlighted limitations in comparing methods.

Conclusions:

Classical reject inference methods require careful consideration due to their empirical nature.
Further research is needed to develop robust model-based reject inference techniques.
Improved methods can lead to more accurate credit risk assessment.