L'accord de crédits aux particuliers à l'aide de méthodes non paramétriques

Abstract

The dissertation tackles the problem of credit scoring. This term is used to describe statistical methods used for classifying applicants for credit into good and bad risk classes. Such methods have become increasingly important with the dramatic growth in consumer credit in recent years. In addition, the advances in computer technology permits to treat large data sets. The credit area is a natural one for application of statistical ideas. In this dissertation we describe the construction of a credit scoring system using $k$ nearest neighbour method and another using nonparametric estimation of density, in particular kernel estimator. We compare results with two methods of the SAS software.

Date of Award	Jun 1999
Original language	French
Supervisor	Jean Paul Rasson (Supervisor)