Unité mixte de recherche 7235

Reject inference in application scorecards: evidence from France

Ha Thu Nguyen

[en]Credit scoring models are commonly developed using only accepted Known Good/Bad (G/B) applications, called KGB model, because we only know the performance of those accepted in the past. Obviously, the KGB model is not indicative of the entire through-the-door population, and reject inference precisely attempts to address the bias by assigning an inferred G/B status to rejected applications. In this paper, we discuss the pros and cons of various reject inference techniques, and pitfalls to avoid when using them. We consider a real dataset of a major French consumer finance bank to assess the effectiveness of the practice of using reject inference. To do that, we rely on the logistic regression framework to model probabilities to become good/bad, and then validate the model performance with and without sample selection bias correction. Our main results can be summarized as follows. First, we show that the best reject inference technique is not necessarily the most complicated one: reweighting and parceling provide more accurate and relevant results than fuzzy augmentation and Heckman’s two-stage correction. Second, disregarding rejected applications significantly impacts the forecast accuracy of the scorecard. Third, as the sum of standard errors dramatically reduces when the sample size increases, reject inference turns out to produce an improved representation of the population. Finally, reject inference appears to be an effective way to reduce overfitting in model selection.[/en]


lundi 20 mars 2023

Law, Institutions and Economics in Nanterre (LIEN)

Ambre Nicolle (LMU Munich)

En salle 614 et en distanciel

Competition and value capture in platform markets: Implications for complementor strategy

mardi 21 mars 2023

Recherche et Economie et Socioéconomie Politique, des Institutions et des Régulations (RESPIR)

André Orléan (Paris School of Economics)

Toutes les valeurs sont des espèces d’un même genre

mercredi 22 mars 2023

Économies du monde musulman

Hicham Benamirouche (CREAD, Alger) | Mongi Marzoug (ancien ministre tunisien de l’énergie)

La sécurité énergétique dans la région MENA: une proposition d’évaluation | La transition énergétique dans les pays du MENA

jeudi 23 mars 2023


Benjamin Monnery

Salle 401-402 à 12h

Does the International Criminal Court Reduce Violence Against Civilians?

lundi 27 mars 2023

Professeurs invités

Axel Gautier

jeudi 30 mars 2023

Groupe de travail « Intelligence artificielle »

Paola Tubaro (CREST)

Salle : G614B

Artificial intelligence, labour transformations, and inconspicuous inequalities: women’s work on digital ‘micro-tasking’ platforms

jeudi 30 mars 2023


Morel Tien

Migration et synchronisation des cycles

lundi 3 avril 2023

Law, Institutions and Economics in Nanterre (LIEN)

Stefania Marcassa (CY Cergy)

En salle 614 et en distanciel


Inscription aux Newsletters