Repositorio Institucional de la Universidad de Lima

El Repositorio Institucional Ulima centraliza, preserva y difunde en acceso abierto la producción académica, científica y cultural de la Universidad de Lima.

Repositorio Institucional de la Universidad de Lima

El Repositorio Institucional Ulima centraliza, preserva y difunde en acceso abierto la producción académica, científica y cultural de la Universidad de Lima.

Artículo

Optimizing Credit Risk Prediction in the Financial Sector Using Boosting Algorithms: A Comparative Study with Financial Datasets

Altmetric
Resumen
Credit risk is a significant concern for financial institutions. Despite advances in predictive models, there is still room for improvement in accurately assessing credit risk. This study focuses on developing a methodological process to predict credit risk in the financial sector using algorithms based on boosting techniques, such as XGBoost, LightGBM and Boosted Random Forest. We found that datasets with good accessibility and an appropriate variable distribution are contained in the UCI Machine Learning Repository. These datasets are potential to outperform results with different metrics, such as the F-Score and the Area Under the Curve. The datasets used include Statlog German Credit Data, Statlog Australian Credit Approval, Bank Marketing, Credit Approval, and South German Credit Data. The approach involves feature engineering, exploratory data analysis, and hyperparameter tuning. Furthermore, we propose a new strategy that involves adding a column based on an unsupervised algorithm such as Kmeans. Our results indicate that XGBoost has better performance than LightGBM and Boosted Random Forest in different scenarios. Finally, the performance of these boosting-based models is superior to that of Boosted Decision Trees and Factorization Machine models from previous studies. These findings are important for financial institutions seeking an effective methodology to improve credit risk prediction rate.
Editor
Instituto Politecnico Nacional
Fecha
2025
Cita bibliográfica

Villanueva Mora, R. O., & Escobedo Cárdenas, E. J. (2025). Optimizing Credit Risk Prediction in the Financial Sector Using Boosting Algorithms: A Comparative Study with Financial Datasets. Instituto Politecnico Nacional. https://hdl.handle.net/20.500.12724/24465

Villanueva Mora RO, Escobedo Cárdenas EJ. Optimizing Credit Risk Prediction in the Financial Sector Using Boosting Algorithms: A Comparative Study with Financial Datasets. Instituto Politecnico Nacional; 2025. Disponible en: https://hdl.handle.net/20.500.12724/24465

@misc{20.500.12724/24465,
	author = "Villanueva Mora, Renzo Orlando and Escobedo Cárdenas, Edwin Jhonatan",
	title = "Optimizing Credit Risk Prediction in the Financial Sector Using Boosting Algorithms: A Comparative Study with Financial Datasets",
	publisher = "Instituto Politecnico Nacional",
	year = "2025",
	url = "https://hdl.handle.net/20.500.12724/24465",
}
  • dc.contributor.author
    Villanueva Mora, Renzo Orlando
    Escobedo Cárdenas, Edwin Jhonatan
  • dc.contributor.other
    Escobedo Cárdenas, Edwin Jhonatan
  • dc.contributor.student
    Villanueva Mora, Renzo Orlando (Ingeniería de Sistemas)
  • dc.date.accessioned
    2026-03-02T20:53:06Z
  • dc.date.available
    2026-03-02T20:53:06Z
  • dc.date.issued
    2025
  • dc.description.abstract
    Credit risk is a significant concern for financial institutions. Despite advances in predictive models, there is still room for improvement in accurately assessing credit risk. This study focuses on developing a methodological process to predict credit risk in the financial sector using algorithms based on boosting techniques, such as XGBoost, LightGBM and Boosted Random Forest. We found that datasets with good accessibility and an appropriate variable distribution are contained in the UCI Machine Learning Repository. These datasets are potential to outperform results with different metrics, such as the F-Score and the Area Under the Curve. The datasets used include Statlog German Credit Data, Statlog Australian Credit Approval, Bank Marketing, Credit Approval, and South German Credit Data. The approach involves feature engineering, exploratory data analysis, and hyperparameter tuning. Furthermore, we propose a new strategy that involves adding a column based on an unsupervised algorithm such as Kmeans. Our results indicate that XGBoost has better performance than LightGBM and Boosted Random Forest in different scenarios. Finally, the performance of these boosting-based models is superior to that of Boosted Decision Trees and Factorization Machine models from previous studies. These findings are important for financial institutions seeking an effective methodology to improve credit risk prediction rate.
  • dc.format
    html
  • dc.identifier.doi
    https://doi.org/10.13053/CyS-29-2-5173
  • dc.identifier.isni
    0000000121541816
  • dc.identifier.issn
    2007-9737
  • dc.identifier.journal
    Computacion y Sistemas
  • dc.identifier.scopusid
    2-s2.0-105010729421
  • dc.identifier.uri
    https://hdl.handle.net/20.500.12724/24465
  • dc.identifier.wosid
    WOS:001526785800018
  • dc.language.iso
    eng
  • dc.publisher
    Instituto Politecnico Nacional
  • dc.publisher.country
    MX
  • dc.relation.ispartof
    urn:issn: 2007-9737
  • dc.rights
    info:eu-repo/semantics/openAccess
  • dc.rights.uri
    https://creativecommons.org/licenses/by-nc/4.0/
  • dc.subject
    Pendiente
  • dc.subject.ocde
    https://purl.org/pe-repo/ocde/ford#2.02.04
  • dc.title
    Optimizing Credit Risk Prediction in the Financial Sector Using Boosting Algorithms: A Comparative Study with Financial Datasets
  • dc.type
    info:eu-repo/semantics/article
  • dc.type.other
    Artículo (Scopus / Web of Science)
ALICIA
Renati
La Referencia
GoogleScholar