Machine learning XAI for early loan default prediction

Abstract: Early default prediction with predictive models is of crucial importance for financial institutions, Fintech or Peer to Peer (P2P) lending platforms, as it allows them to effectively mitigate the potential risks associated with customer or debtor defaults, anticipating before this becomes...

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Detalhes bibliográficos
Autores: Monje, Leticia, Carrasco González, Ramón Alberto, Sánchez-Montañes, Manuel
Tipo de documento: artigo
Data de publicação:2025
País:España
Recursos:Universidad Complutense de Madrid (UCM)
Repositório:Docta Complutense
Idioma:inglês
OAI Identifier:oai:docta.ucm.es:20.500.14352/132721
Acesso em linha:https://hdl.handle.net/20.500.14352/132721
Access Level:Acceso aberto
Palavra-chave:004.85
519.2
336.77
Peer to peer lending
Machine learning
Early default risk
XAI
Fuzzy
Inteligencia artificial (Informática)
Estadística
1203.04 Inteligencia Artificial
1209 Estadística
1209.14 Técnicas de Predicción Estadística
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oai_identifier_str oai:docta.ucm.es:20.500.14352/132721
network_acronym_str ES
network_name_str España
repository_id_str
spelling Machine learning XAI for early loan default predictionMonje, LeticiaCarrasco González, Ramón AlbertoSánchez-Montañes, Manuel004.85519.2336.77Peer to peer lendingMachine learningEarly default riskXAIFuzzyInteligencia artificial (Informática)Estadística1203.04 Inteligencia Artificial1209 Estadística1209.14 Técnicas de Predicción EstadísticaAbstract: Early default prediction with predictive models is of crucial importance for financial institutions, Fintech or Peer to Peer (P2P) lending platforms, as it allows them to effectively mitigate the potential risks associated with customer or debtor defaults, anticipating before this becomes a major problem. This proactive approach serves to avoid the consequent impact on provisions and, subsequently, on the institution's capital. On the other hand, advanced predictive models are often less interpretable than traditional models such as probit (Abdou & Pointon, 2011) and logistic regression (Bolton, 2009; Liu et al. 2024). Due to this lower explainability, our goal was to develop a methodology that allows building an advanced predictive model together with a linguistically interpretable explanation useful for decision making from large volumes of data. For this purpose, our case study was the loan dataset of Lending Club, the largest P2P lending platform in the world. As a result, we obtained a model based on the eXtreme Gradient Boosting (XGBoost) together with its linguistic interpretation using a surrogate model and the 2-tuple fuzzy linguistic model Monje et al., (Mathematics 10:1428, 2022). This model allows us to identify five risk categories (very low, low, medium, high and very high).Springer NatureUniversidad Complutense de Madrid20252025-06-0320252025-06-03journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/20.500.14352/132721reponame:Docta Complutenseinstname:Universidad Complutense de Madrid (UCM)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:docta.ucm.es:20.500.14352/1327212026-06-02T12:44:21Z
dc.title.none.fl_str_mv Machine learning XAI for early loan default prediction
title Machine learning XAI for early loan default prediction
spellingShingle Machine learning XAI for early loan default prediction
Monje, Leticia
004.85
519.2
336.77
Peer to peer lending
Machine learning
Early default risk
XAI
Fuzzy
Inteligencia artificial (Informática)
Estadística
1203.04 Inteligencia Artificial
1209 Estadística
1209.14 Técnicas de Predicción Estadística
title_short Machine learning XAI for early loan default prediction
title_full Machine learning XAI for early loan default prediction
title_fullStr Machine learning XAI for early loan default prediction
title_full_unstemmed Machine learning XAI for early loan default prediction
title_sort Machine learning XAI for early loan default prediction
dc.creator.none.fl_str_mv Monje, Leticia
Carrasco González, Ramón Alberto
Sánchez-Montañes, Manuel
author Monje, Leticia
author_facet Monje, Leticia
Carrasco González, Ramón Alberto
Sánchez-Montañes, Manuel
author_role author
author2 Carrasco González, Ramón Alberto
Sánchez-Montañes, Manuel
author2_role author
author
dc.contributor.none.fl_str_mv Universidad Complutense de Madrid
dc.subject.none.fl_str_mv 004.85
519.2
336.77
Peer to peer lending
Machine learning
Early default risk
XAI
Fuzzy
Inteligencia artificial (Informática)
Estadística
1203.04 Inteligencia Artificial
1209 Estadística
1209.14 Técnicas de Predicción Estadística
topic 004.85
519.2
336.77
Peer to peer lending
Machine learning
Early default risk
XAI
Fuzzy
Inteligencia artificial (Informática)
Estadística
1203.04 Inteligencia Artificial
1209 Estadística
1209.14 Técnicas de Predicción Estadística
description Abstract: Early default prediction with predictive models is of crucial importance for financial institutions, Fintech or Peer to Peer (P2P) lending platforms, as it allows them to effectively mitigate the potential risks associated with customer or debtor defaults, anticipating before this becomes a major problem. This proactive approach serves to avoid the consequent impact on provisions and, subsequently, on the institution's capital. On the other hand, advanced predictive models are often less interpretable than traditional models such as probit (Abdou & Pointon, 2011) and logistic regression (Bolton, 2009; Liu et al. 2024). Due to this lower explainability, our goal was to develop a methodology that allows building an advanced predictive model together with a linguistically interpretable explanation useful for decision making from large volumes of data. For this purpose, our case study was the loan dataset of Lending Club, the largest P2P lending platform in the world. As a result, we obtained a model based on the eXtreme Gradient Boosting (XGBoost) together with its linguistic interpretation using a surrogate model and the 2-tuple fuzzy linguistic model Monje et al., (Mathematics 10:1428, 2022). This model allows us to identify five risk categories (very low, low, medium, high and very high).
publishDate 2025
dc.date.none.fl_str_mv 2025
2025-06-03
2025
2025-06-03
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.14352/132721
url https://hdl.handle.net/20.500.14352/132721
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer Nature
publisher.none.fl_str_mv Springer Nature
dc.source.none.fl_str_mv reponame:Docta Complutense
instname:Universidad Complutense de Madrid (UCM)
instname_str Universidad Complutense de Madrid (UCM)
reponame_str Docta Complutense
collection Docta Complutense
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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