Extraction of Knowledge with Population-Based Metaheuristics Fuzzy Rules Applied to Credit Risk

One of the goals of financial institutions is to reduce credit risk. Consequently they must properly select customers. There are a variety of methodologies for credit scoring, which analyzes a wide variety of personal and financial variables of the potential client. These variables are heterogeneous...

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Autores principales: Jimbo Santana, Patricia Rosalía, Lanzarini, Laura Cristina, Fernández Bariviera, Aurelio
Formato: Objeto de conferencia
Lenguaje:Español
Publicado: 2018
Materias:
Informática
VarPSO (Variable Particle Swarm Optimization)
FR (Fuzzy Rules)
credit risk
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/136159
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-136159
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Español
topic Informática
VarPSO (Variable Particle Swarm Optimization)
FR (Fuzzy Rules)
credit risk
spellingShingle Informática
VarPSO (Variable Particle Swarm Optimization)
FR (Fuzzy Rules)
credit risk
Jimbo Santana, Patricia Rosalía
Lanzarini, Laura Cristina
Fernández Bariviera, Aurelio
Extraction of Knowledge with Population-Based Metaheuristics Fuzzy Rules Applied to Credit Risk
topic_facet Informática
VarPSO (Variable Particle Swarm Optimization)
FR (Fuzzy Rules)
credit risk
description One of the goals of financial institutions is to reduce credit risk. Consequently they must properly select customers. There are a variety of methodologies for credit scoring, which analyzes a wide variety of personal and financial variables of the potential client. These variables are heterogeneous making that their analysis is long and tedious. This paper presents an alternative method that, based on the subject information, offers a set of classification rules with three main characteristics: adequate precision, low cardinality and easy interpretation. This is because the antecedent consists of a small number of attributes that can be modeled as fuzzy variables. This feature, together with a reduced set of rules allows obtaining useful patterns to understand the relationships between data, and make the right decisions for the financial institutions. The smaller the number of analyzed variables of the potential customer, the simpler the model will be. In this way, credit officers may give an answer to the loan application in the shorter time, achieving a competitive advantage for the financial institution. The proposed method has been applied to two databases from the UCI repository, and a database from a credit unions cooperative in Ecuador. The results are satisfactory, as highlighted in the conclusions. Some future lines of research are suggested.
format Objeto de conferencia
Objeto de conferencia
author Jimbo Santana, Patricia Rosalía
Lanzarini, Laura Cristina
Fernández Bariviera, Aurelio
author_facet Jimbo Santana, Patricia Rosalía
Lanzarini, Laura Cristina
Fernández Bariviera, Aurelio
author_sort Jimbo Santana, Patricia Rosalía
title Extraction of Knowledge with Population-Based Metaheuristics Fuzzy Rules Applied to Credit Risk
title_short Extraction of Knowledge with Population-Based Metaheuristics Fuzzy Rules Applied to Credit Risk
title_full Extraction of Knowledge with Population-Based Metaheuristics Fuzzy Rules Applied to Credit Risk
title_fullStr Extraction of Knowledge with Population-Based Metaheuristics Fuzzy Rules Applied to Credit Risk
title_full_unstemmed Extraction of Knowledge with Population-Based Metaheuristics Fuzzy Rules Applied to Credit Risk
title_sort extraction of knowledge with population-based metaheuristics fuzzy rules applied to credit risk
publishDate 2018
url http://sedici.unlp.edu.ar/handle/10915/136159
work_keys_str_mv AT jimbosantanapatriciarosalia extractionofknowledgewithpopulationbasedmetaheuristicsfuzzyrulesappliedtocreditrisk
AT lanzarinilauracristina extractionofknowledgewithpopulationbasedmetaheuristicsfuzzyrulesappliedtocreditrisk
AT fernandezbarivieraaurelio extractionofknowledgewithpopulationbasedmetaheuristicsfuzzyrulesappliedtocreditrisk
bdutipo_str Repositorios
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