Optimizing credit risk prediction in the financial sector using boosting algorithms: a comparative study with financial datasets

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This article analyzes credit risk in the financial sector and proposes a methodology to improve its prediction accuracy using boosting algorithms such as XGBoost, LightGBM, and Boosted Random Forest. Datasets from the UCI Machine Learning Repository were used, including Statlog German Credit Data, A...

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Detalles Bibliográficos
Autor: Villanueva Mora, Renzo Orlando
Formato: tesis de grado
Fecha de Publicación:2025
Institución:Universidad de Lima
Repositorio:ULIMA-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.ulima.edu.pe:20.500.12724/23390
Enlace del recurso:https://hdl.handle.net/20.500.12724/23390
Nivel de acceso:acceso abierto
Materia:Pendiente
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dc.title.en_EN.fl_str_mv Optimizing credit risk prediction in the financial sector using boosting algorithms: a comparative study with financial datasets
dc.title.alternative.en_EN.fl_str_mv Optimización de la predicción del riesgo crediticio en el sector financiero mediante algoritmos de boosting: un estudio comparativo con conjuntos de datos financieros
title Optimizing credit risk prediction in the financial sector using boosting algorithms: a comparative study with financial datasets
spellingShingle Optimizing credit risk prediction in the financial sector using boosting algorithms: a comparative study with financial datasets
Villanueva Mora, Renzo Orlando
Pendiente
https://purl.org/pe-repo/ocde/ford#2.02.04
title_short Optimizing credit risk prediction in the financial sector using boosting algorithms: a comparative study with financial datasets
title_full Optimizing credit risk prediction in the financial sector using boosting algorithms: a comparative study with financial datasets
title_fullStr Optimizing credit risk prediction in the financial sector using boosting algorithms: a comparative study with financial datasets
title_full_unstemmed Optimizing credit risk prediction in the financial sector using boosting algorithms: a comparative study with financial datasets
title_sort Optimizing credit risk prediction in the financial sector using boosting algorithms: a comparative study with financial datasets
author Villanueva Mora, Renzo Orlando
author_facet Villanueva Mora, Renzo Orlando
author_role author
dc.contributor.advisor.fl_str_mv Escobedo Cardenas, Edwin Jonathan
dc.contributor.author.fl_str_mv Villanueva Mora, Renzo Orlando
dc.subject.es_PE.fl_str_mv Pendiente
topic Pendiente
https://purl.org/pe-repo/ocde/ford#2.02.04
dc.subject.ocde.none.fl_str_mv https://purl.org/pe-repo/ocde/ford#2.02.04
description This article analyzes credit risk in the financial sector and proposes a methodology to improve its prediction accuracy using boosting algorithms such as XGBoost, LightGBM, and Boosted Random Forest. Datasets from the UCI Machine Learning Repository were used, including Statlog German Credit Data, Australian Credit Approval, and Bank Marketing. The methodology involved feature engineering, exploratory data analysis, and hyperparameter tuning. Additionally, a complementary strategy using K-means clustering was implemented to enhance the data. The results show that XGBoost outperforms the other models in various scenarios, and boosting-based methods deliver better performance than traditional approaches like decision trees and factorization machines—offering valuable insights for financial institutions.
publishDate 2025
dc.date.accessioned.none.fl_str_mv 2025-09-23T16:37:07Z
dc.date.available.none.fl_str_mv 2025-09-23T16:37:07Z
dc.date.issued.fl_str_mv 2025
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dc.publisher.none.fl_str_mv Universidad de Lima
dc.publisher.country.none.fl_str_mv PE
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spelling Escobedo Cardenas, Edwin JonathanVillanueva Mora, Renzo Orlando2025-09-23T16:37:07Z2025-09-23T16:37:07Z2025https://hdl.handle.net/20.500.12724/233900000000121541816This article analyzes credit risk in the financial sector and proposes a methodology to improve its prediction accuracy using boosting algorithms such as XGBoost, LightGBM, and Boosted Random Forest. Datasets from the UCI Machine Learning Repository were used, including Statlog German Credit Data, Australian Credit Approval, and Bank Marketing. The methodology involved feature engineering, exploratory data analysis, and hyperparameter tuning. Additionally, a complementary strategy using K-means clustering was implemented to enhance the data. The results show that XGBoost outperforms the other models in various scenarios, and boosting-based methods deliver better performance than traditional approaches like decision trees and factorization machines—offering valuable insights for financial institutions.Este artículo analiza el riesgo crediticio en el sector financiero y propone una metodología para predecirlo con mayor precisión mediante algoritmos de boosting como XGBoost, LightGBM y Boosted Random Forest. Se utilizaron datasets del repositorio UCI como Statlog German Credit Data, Australian Credit Approval, Bank Marketing, entre otros, aplicando técnicas de feature engineering, análisis exploratorio y ajuste de hiperparámetros. Además, se incorporó una estrategia adicional con K-means para enriquecer los datos. Los resultados muestran que XGBoost supera a los demás modelos en distintos escenarios, y que los métodos de boosting ofrecen mejor desempeño que enfoques tradicionales como árboles de decisión y máquinas de factorización, lo cual resulta valioso para las entidades financieras.application/pdfengUniversidad de LimaPEinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/4.0/Pendientehttps://purl.org/pe-repo/ocde/ford#2.02.04Optimizing credit risk prediction in the financial sector using boosting algorithms: a comparative study with financial datasetsOptimización de la predicción del riesgo crediticio en el sector financiero mediante algoritmos de boosting: un estudio comparativo con conjuntos de datos financierosinfo:eu-repo/semantics/bachelorThesisTesisreponame:ULIMA-Institucionalinstname:Universidad de Limainstacron:ULIMASUNEDUTitulo profesionalIngeniería de SistemasUniversidad de Lima. Facultad de IngenieríaIngeniero de Sistemashttps://orcid.org/0000-0003-2034-513X4521175561207672754378https://purl.org/pe-repo/renati/level#tituloProfesionalGuzman Jimenez, Rosario MarybelEscobedo Cardenas, Edwin JonathanQuintana Cruz, Hernan Alejandrohttps://purl.org/pe-repo/renati/type#tesisOIORIGINALT018_72754378_T.pdfT018_72754378_T.pdfDescargarapplication/pdf319855https://repositorio.ulima.edu.pe/bitstream/20.500.12724/23390/1/T018_72754378_T.pdf6a43d8a3015b9618d459066420174139MD51FA_72754378.pdfFA_72754378.pdfAutorizaciónapplication/pdf248503https://repositorio.ulima.edu.pe/bitstream/20.500.12724/23390/2/FA_72754378.pdf48045e3f2fadcd218ebd1e666007d40bMD52TURNITIN_DNI_72754378 - 20193654.pdfTURNITIN_DNI_72754378 - 20193654.pdfReporte de similitudapplication/pdf544088https://repositorio.ulima.edu.pe/bitstream/20.500.12724/23390/3/TURNITIN_DNI_72754378%20-%2020193654.pdf6b600cd4250817d28c3f7b0d44ed0466MD53TEXTT018_72754378_T.pdf.txtT018_72754378_T.pdf.txtExtracted texttext/plain13745https://repositorio.ulima.edu.pe/bitstream/20.500.12724/23390/4/T018_72754378_T.pdf.txt55947bb6c038eaf58ed2ec6c7c147c47MD54FA_72754378.pdf.txtFA_72754378.pdf.txtExtracted texttext/plain4323https://repositorio.ulima.edu.pe/bitstream/20.500.12724/23390/6/FA_72754378.pdf.txt8006eaadf78be844006ebd108415fc0dMD56TURNITIN_DNI_72754378 - 20193654.pdf.txtTURNITIN_DNI_72754378 - 20193654.pdf.txtExtracted texttext/plain17147https://repositorio.ulima.edu.pe/bitstream/20.500.12724/23390/8/TURNITIN_DNI_72754378%20-%2020193654.pdf.txt090bfce0a50eae8e44caf6b8f2fc1ca7MD58THUMBNAILT018_72754378_T.pdf.jpgT018_72754378_T.pdf.jpgGenerated Thumbnailimage/jpeg12120https://repositorio.ulima.edu.pe/bitstream/20.500.12724/23390/5/T018_72754378_T.pdf.jpgb43aff4f5cac5d73eed02d5af83a19c7MD55FA_72754378.pdf.jpgFA_72754378.pdf.jpgGenerated Thumbnailimage/jpeg21287https://repositorio.ulima.edu.pe/bitstream/20.500.12724/23390/7/FA_72754378.pdf.jpg0e2b72a978d2d37bb2458b5278c1e44bMD57TURNITIN_DNI_72754378 - 20193654.pdf.jpgTURNITIN_DNI_72754378 - 20193654.pdf.jpgGenerated Thumbnailimage/jpeg8897https://repositorio.ulima.edu.pe/bitstream/20.500.12724/23390/9/TURNITIN_DNI_72754378%20-%2020193654.pdf.jpg5622317d554cd63d791ec83479f15732MD5920.500.12724/23390oai:repositorio.ulima.edu.pe:20.500.12724/233902025-09-29 12:38:56.747Repositorio Universidad de Limarepositorio@ulima.edu.pe
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Optimizing credit risk prediction in the financial sector using boosting algorithms: a comparative study with financial datasets
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