Reject inference in application scorecards: evidence from France

By H. Nguyen. 2016

9 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Amanda Coston
2. Ashesh Rambachan
3. Alexandra Chouldechova
4. Robert Jenssen
5. Michael C. Kampffmeyer
6. Rogelio Andrade Mancisidor
7. Kjersti Aas
8. Claudia Czado

Citations in the corpus

• Characterizing Fairness Over the Set of Good Models Under Selective Labels (2021, background): “This is also called the “ known good-bad ” (KGB) approach [33, 34].”
• Deep generative models for reject inference in credit scoring (2019, methodology): “tted model using only the accepted applications. Finally, the parcelling (fuzzy parceling) method ts a new (weighted) logistic regression using both accepted and rejected applications. The results in [38] do not show higher model performance using the reject inference methods. However, the parameter”