Reject inference methods in credit scoring

Ehrhardt, A.; Biernacki, Christophe; Vandewalle, Vincent; Heinrich, Philippe; Beben, S.

Document type :

Article dans une revue scientifique: Article original

DOI :

10.1080/02664763.2021.1929090

PMID :

35707081

Permalink :

http://hdl.handle.net/20.500.12210/90050

Title :

Reject inference methods in credit scoring

Author(s) :

Ehrhardt, A. [Auteur]
Biernacki, Christophe [Auteur]

Laboratoire Paul Painlevé - UMR 8524 [LPP]
Vandewalle, Vincent [Auteur]

METRICS : Evaluation des technologies de santé et des pratiques médicales - ULR 2694
Heinrich, Philippe [Auteur]

Laboratoire Paul Painlevé - UMR 8524 [LPP]
Beben, S. [Auteur]

Journal title :

Journal of Applied Statistics

Abbreviated title :

J. Appl. Stat.

Volume number :

Pages :

p. 2734-2754

Publication date :

2021

ISSN :

0266-4763

English keyword(s) :

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

HAL domain(s) :

Sciences du Vivant [q-bio]

English abstract : [en]

The granting process is based on the probability that the applicant will refund his/her loan given his/her characteristics. This probability, also called score, is learnt based on a dataset in which rejected applicants are ...
Show more >The granting process is based on the probability that the applicant will refund his/her loan given his/her characteristics. This probability, also called score, is learnt based on a dataset in which rejected applicants are excluded. Thus, the population on which the score is used is different from the learning population. Many “reject inference” methods try to exploit the data available from the rejected applicants in the learning process. However, most of these methods are empirical and lack of formalization of their assumptions, and of their expected theoretical properties. We formalize such hidden assumptions in a general missing data setting for some of the most common reject inference methods. It reveals that hidden modelling is mostly incomplete, thus prohibiting to compare existing methods within the general model selection mechanism (except by financing “non-fundable” applicants). So, we assess performance of the methods on both simulated data and real data (from CACF, a major European loan issuer). Unsurprisingly, no method seems uniformly dominant. Both these theoretical and empirical results not only reinforce the idea to carefully use the classical reject inference methods but also to invest in future research works for designing model-based reject inference methods (without financing “non-fundable” applicants).Show less >

Language :

Anglais

Audience :

Internationale

Popular science :

Non

Administrative institution(s) :

Université de Lille
CHU Lille

Collections :

METRICS : Evaluation des technologies de santé et des pratiques médicales - ULR 2694

Submission date :

2023-11-15T03:52:42Z
2024-04-08T14:37:17Z

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Version	Link	Modification date
2	20.500.12210/90050*	2024-04-08T14:30:20Z
1	20.500.12210/90050.1	2023-11-15T03:52:42Z

Reject inference methods in credit scoring

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Reject inference methods in credit scoring

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RIS