Feature Selection to Optimize Credit Banking Risk Evaluation Decisions for the Example of Home Equity Loans

Abstract: Measuring credit risk is essential for financial institutions because there is a high risk level associated with incorrect credit decisions. The Basel II agreement recommended the use of advanced credit scoring methods in order to improve the efficiency of capital allocation. The latest Ba...

Descripción completa

Detalles Bibliográficos
Autores: VACA LAMATA, MARTA, Perez Martin, Agustin, Pérez-Torregrosa, Agustín, Rabasa, Alejandro
Tipo de recurso: artículo
Fecha de publicación:2020
País:España
Institución:Universidad Miguel Hernández de Elche
Repositorio:REDIUMH. Depósito Digital de la UMH
OAI Identifier:oai:dspace.umh.es:11000/34875
Acceso en línea:https://hdl.handle.net/11000/34875
Access Level:acceso abierto
Palabra clave:credit scoring
feature selection
big data
data mining
CDU::3 - Ciencias sociales::33 - Economía
id ES_53f44efd1eaa6f8d156fd1e68ea1a04d
oai_identifier_str oai:dspace.umh.es:11000/34875
network_acronym_str ES
network_name_str España
repository_id_str
spelling Feature Selection to Optimize Credit Banking Risk Evaluation Decisions for the Example of Home Equity LoansVACA LAMATA, MARTAPerez Martin, AgustinPérez-Torregrosa, AgustínRabasa, Alejandrocredit scoringfeature selectionbig datadata miningCDU::3 - Ciencias sociales::33 - EconomíaAbstract: Measuring credit risk is essential for financial institutions because there is a high risk level associated with incorrect credit decisions. The Basel II agreement recommended the use of advanced credit scoring methods in order to improve the efficiency of capital allocation. The latest Basel agreement (Basel III) states that the requirements for reserves based on risk have increased. Financial institutions currently have exhaustive datasets regarding their operations; this is a problem that can be addressed by applying a good feature selection method combined with big data techniques for data management. A comparative study of selection techniques is conducted in this work to find the selector that reduces the mean square error and requires the least execution time.MDPIDepartamentos de la UMH::Estudios Económicos y Financieros202520252020info:eu-repo/semantics/articleapplication/pdf16application/pdfhttps://hdl.handle.net/11000/34875reponame:REDIUMH. Depósito Digital de la UMHinstname:Universidad Miguel Hernández de ElcheInglés811https://doi.org/10.3390/math8111971info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/oai:dspace.umh.es:11000/348752026-05-27T13:36:21Z
dc.title.none.fl_str_mv Feature Selection to Optimize Credit Banking Risk Evaluation Decisions for the Example of Home Equity Loans
title Feature Selection to Optimize Credit Banking Risk Evaluation Decisions for the Example of Home Equity Loans
spellingShingle Feature Selection to Optimize Credit Banking Risk Evaluation Decisions for the Example of Home Equity Loans
VACA LAMATA, MARTA
credit scoring
feature selection
big data
data mining
CDU::3 - Ciencias sociales::33 - Economía
title_short Feature Selection to Optimize Credit Banking Risk Evaluation Decisions for the Example of Home Equity Loans
title_full Feature Selection to Optimize Credit Banking Risk Evaluation Decisions for the Example of Home Equity Loans
title_fullStr Feature Selection to Optimize Credit Banking Risk Evaluation Decisions for the Example of Home Equity Loans
title_full_unstemmed Feature Selection to Optimize Credit Banking Risk Evaluation Decisions for the Example of Home Equity Loans
title_sort Feature Selection to Optimize Credit Banking Risk Evaluation Decisions for the Example of Home Equity Loans
dc.creator.none.fl_str_mv VACA LAMATA, MARTA
Perez Martin, Agustin
Pérez-Torregrosa, Agustín
Rabasa, Alejandro
author VACA LAMATA, MARTA
author_facet VACA LAMATA, MARTA
Perez Martin, Agustin
Pérez-Torregrosa, Agustín
Rabasa, Alejandro
author_role author
author2 Perez Martin, Agustin
Pérez-Torregrosa, Agustín
Rabasa, Alejandro
author2_role author
author
author
dc.contributor.none.fl_str_mv Departamentos de la UMH::Estudios Económicos y Financieros
dc.subject.none.fl_str_mv credit scoring
feature selection
big data
data mining
CDU::3 - Ciencias sociales::33 - Economía
topic credit scoring
feature selection
big data
data mining
CDU::3 - Ciencias sociales::33 - Economía
description Abstract: Measuring credit risk is essential for financial institutions because there is a high risk level associated with incorrect credit decisions. The Basel II agreement recommended the use of advanced credit scoring methods in order to improve the efficiency of capital allocation. The latest Basel agreement (Basel III) states that the requirements for reserves based on risk have increased. Financial institutions currently have exhaustive datasets regarding their operations; this is a problem that can be addressed by applying a good feature selection method combined with big data techniques for data management. A comparative study of selection techniques is conducted in this work to find the selector that reduces the mean square error and requires the least execution time.
publishDate 2020
dc.date.none.fl_str_mv 2020
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/11000/34875
url https://hdl.handle.net/11000/34875
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv 8
11
https://doi.org/10.3390/math8111971
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv Attribution-NonCommercial-NoDerivatives 4.0 Internacional
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.format.none.fl_str_mv application/pdf
16
application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:REDIUMH. Depósito Digital de la UMH
instname:Universidad Miguel Hernández de Elche
instname_str Universidad Miguel Hernández de Elche
reponame_str REDIUMH. Depósito Digital de la UMH
collection REDIUMH. Depósito Digital de la UMH
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869408151766302720
score 15,812455

Similares en LA Referencia