An exploratory analysis of methods for extracting credit risk rules

This paper performs a comparative analysis of two kind of methods for extracting credit 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...

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Detalles Bibliográficos
Autores principales: Jimbo Santana, Patricia, Villa Monte, Augusto, Rucci, Enzo, Lanzarini, Laura Cristina, Bariviera, Aurelio
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2016
Materias:
Ciencias Informáticas
credit scoring
classification rules
Learning Vector Quantization (LVQ)
Particle Swarm Optimization (PSO)
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/56769
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-56769
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
credit scoring
classification rules
Learning Vector Quantization (LVQ)
Particle Swarm Optimization (PSO)
spellingShingle Ciencias Informáticas
credit scoring
classification rules
Learning Vector Quantization (LVQ)
Particle Swarm Optimization (PSO)
Jimbo Santana, Patricia
Villa Monte, Augusto
Rucci, Enzo
Lanzarini, Laura Cristina
Bariviera, Aurelio
An exploratory analysis of methods for extracting credit risk rules
topic_facet Ciencias Informáticas
credit scoring
classification rules
Learning Vector Quantization (LVQ)
Particle Swarm Optimization (PSO)
description This paper performs a comparative analysis of two kind of methods for extracting credit 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 institutions, who want to decide credit 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.
format Objeto de conferencia
Objeto de conferencia
author Jimbo Santana, Patricia
Villa Monte, Augusto
Rucci, Enzo
Lanzarini, Laura Cristina
Bariviera, Aurelio
author_facet Jimbo Santana, Patricia
Villa Monte, Augusto
Rucci, Enzo
Lanzarini, Laura Cristina
Bariviera, Aurelio
author_sort Jimbo Santana, Patricia
title An exploratory analysis of methods for extracting credit risk rules
title_short An exploratory analysis of methods for extracting credit risk rules
title_full An exploratory analysis of methods for extracting credit risk rules
title_fullStr An exploratory analysis of methods for extracting credit risk rules
title_full_unstemmed An exploratory analysis of methods for extracting credit risk rules
title_sort exploratory analysis of methods for extracting credit risk rules
publishDate 2016
url http://sedici.unlp.edu.ar/handle/10915/56769
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