An exploratory analysis of methods for extracting cbkp_redit risk rules

Abstract:

This paper performs a comparative analysis of two kind of methods for extracting cbkp_redit risk rules. On one hand we have a set of methods based on the combination of an optimization technique initialized with a neural network. On the other hand there are partition algorithms, based on trees. We show results obtain on two real databases. The main findings are that the set of rules obtained by the first set of methods give a set of rules with a reduced cardinality, with an acceptable precision regarding classification. This is a desirable property for financial in stitutions, who want to decide cbkp_redit approval face to face with customers. Bank employees who daily deal with retail customers can be easily trained for selecting the best customers, by using this kind of solutions.

Año de publicación:

2016

Keywords:

Fuente:

google