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Does reject inference really improve the performance of application scoring models?

Citations

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Cited by:

  1. Charitou, Andreas & Dionysiou, Dionysia & Lambertides, Neophytos & Trigeorgis, Lenos, 2013. "Alternative bankruptcy prediction models using option-pricing theory," Journal of Banking & Finance, Elsevier, vol. 37(7), pages 2329-2341.
  2. Tom, Daniel M. Ph.D., 2024. "Reject Inference," OSF Preprints hq4k6, Center for Open Science.
  3. Zhiyong Li & Xinyi Hu & Ke Li & Fanyin Zhou & Feng Shen, 2020. "Inferring the outcomes of rejected loans: an application of semisupervised clustering," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(2), pages 631-654, February.
  4. Matthew Harding & Gabriel F. R. Vasconcelos, 2022. "Managers versus Machines: Do Algorithms Replicate Human Intuition in Credit Ratings?," Papers 2202.04218, arXiv.org.
  5. Mengnan Song & Jiasong Wang & Suisui Su, 2022. "Towards a Better Microcredit Decision," Papers 2209.07574, arXiv.org.
  6. Bücker, Michael & van Kampen, Maarten & Krämer, Walter, 2013. "Reject inference in consumer credit scoring with nonignorable missing data," Journal of Banking & Finance, Elsevier, vol. 37(3), pages 1040-1045.
  7. Ha Thu Nguyen, 2016. "Reject inference in application scorecards: evidence from France," Working Papers hal-04141601, HAL.
  8. J Banasik & J Crook, 2010. "Reject inference in survival analysis by augmentation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(3), pages 473-485, March.
  9. Gero Szepannek, 2022. "An Overview on the Landscape of R Packages for Open Source Scorecard Modelling," Risks, MDPI, vol. 10(3), pages 1-33, March.
  10. Dongwoo Kim, 2023. "Can investors’ collective decision-making evolve? Evidence from peer-to-peer lending markets," Electronic Commerce Research, Springer, vol. 23(2), pages 1323-1358, June.
  11. Pulina, Manuela & Paba, Antonello, 2010. "A discrete choice approach to model credit card fraud," MPRA Paper 20019, University Library of Munich, Germany.
  12. Crook, Jonathan N. & Edelman, David B. & Thomas, Lyn C., 2007. "Recent developments in consumer credit risk assessment," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1447-1465, December.
  13. Banasik, John & Crook, Jonathan, 2007. "Reject inference, augmentation, and sample selection," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1582-1594, December.
  14. Monir El Annas & Badreddine Benyacoub & Mohamed Ouzineb, 2023. "Semi-supervised adapted HMMs for P2P credit scoring systems with reject inference," Computational Statistics, Springer, vol. 38(1), pages 149-169, March.
  15. Ha-Thu Nguyen, 2016. "Reject inference in application scorecards: evidence from France," EconomiX Working Papers 2016-10, University of Paris Nanterre, EconomiX.
  16. Wu, I-Ding & Hand, David J., 2007. "Handling selection bias when choosing actions in retail credit applications," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1560-1568, December.
  17. Kozodoi, Nikita & Lessmann, Stefan & Alamgir, Morteza & Moreira-Matias, Luis & Papakonstantinou, Konstantinos, 2025. "Fighting sampling bias: A framework for training and evaluating credit scoring models," European Journal of Operational Research, Elsevier, vol. 324(2), pages 616-628.
  18. Lieli, Robert P. & White, Halbert, 2010. "The construction of empirical credit scoring rules based on maximization principles," Journal of Econometrics, Elsevier, vol. 157(1), pages 110-119, July.
  19. Dorfleitner, G. & Just-Marx, S. & Priberny, C., 2017. "What drives the repayment of agricultural micro loans? Evidence from Nicaragua," The Quarterly Review of Economics and Finance, Elsevier, vol. 63(C), pages 89-100.
  20. Rogelio A. Mancisidor & Michael Kampffmeyer & Kjersti Aas & Robert Jenssen, 2019. "Deep Generative Models for Reject Inference in Credit Scoring," Papers 1904.11376, arXiv.org, revised Sep 2021.
  21. Tang, Haoxin & Liang, Decui, 2025. "Multi-view reject inference for semi-supervised credit scoring with consistency training and three-way decision," Omega, Elsevier, vol. 133(C).
  22. Luisa ANDERLONI & Daniela VANDONE, 2010. "Risk of over-indebtedness and behavioural factors," Departmental Working Papers 2010-25, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
  23. David J. Hand, 2018. "Statistical challenges of administrative and transaction data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(3), pages 555-605, June.
  24. J Banasik & J Crook, 2005. "Credit scoring, augmentation and lean models," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(9), pages 1072-1081, September.
  25. Chen, Liao & Jia, Ning & Jiao, Zhixian & Zhao, Hongke & Cui, Runbang & Wang, Huimin, 2025. "A semi-supervised reject inference framework with hierarchical heterogeneous networks for credit scoring," International Journal of Forecasting, Elsevier, vol. 41(3), pages 920-939.
  26. Calabrese, Raffaella & Osmetti, Silvia Angela & Zanin, Luca, 2024. "Sample selection bias in non-traditional lending: A copula-based approach for imbalanced data," Socio-Economic Planning Sciences, Elsevier, vol. 95(C).
  27. repec:osf:osfxxx:hq4k6_v1 is not listed on IDEAS
  28. Kiefer, Nicholas M. & Larson, C. Erik, 2006. "Specification and Informational Issues in Credit Scoring," Working Papers 06-11, Cornell University, Center for Analytic Economics.
  29. Dong-Her Shih & Ting-Wei Wu & Po-Yuan Shih & Nai-An Lu & Ming-Hung Shih, 2022. "A Framework of Global Credit-Scoring Modeling Using Outlier Detection and Machine Learning in a P2P Lending Platform," Mathematics, MDPI, vol. 10(13), pages 1-13, June.
  30. Shen, Feng & Zhang, Xin & Wang, Run & Lan, Dao & Zhou, Wei, 2022. "Sequential optimization three-way decision model with information gain for credit default risk evaluation," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1116-1128.
  31. João Fernandes, 2005. "Corporate Credit Risk Modeling: Quantitative Rating System And Probability Of Default Estimation," Finance 0505013, University Library of Munich, Germany.
  32. Y Kim & S Y Sohn, 2007. "Technology scoring model considering rejected applicants and effect of reject inference," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(10), pages 1341-1347, October.
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