Peer-to-peer lending: Evaluation of credit risk using Machine Learning

Detalhes bibliográficos
Autor(a) principal: Vila Verde, Francisca Viçoso
Data de Publicação: 2021
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/127084
Resumo: Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Risk Analysis and Management
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spelling Peer-to-peer lending: Evaluation of credit risk using Machine LearningMachine LearningCredit ScoringPeer-to-peer lendingEnsemble methodsSingle classifiersDissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Risk Analysis and ManagementPeer-to-peer lenders have transformed the credit market by being an alternative to traditional financial services and taking advantage of the most advanced analytics techniques. Credit scoring and accurate assessment of borrower’s creditworthiness is crucial to managing credit risk and having the capacity of adapting to current market conditions. The Logistic Regression has long been recognised as the benchmark model for credit scoring, so this dissertation aims to evaluate and compare its capabilities to predict loan defaults with other parametric and non-parametric methods, to assess the improvement in predictive power between the most modern techniques and the traditional models in a peer-to-peer lending context. We compare the performance of four different algorithms, the single classifiers Decision Trees and K-Nearest Neighbours, and the ensemble classifiers Random Forest and XGBoost against a benchmark model, the Logistic Regression, using six performance evaluation measures. This dissertation also includes a review of related work, an explanation of the pre-processing involved, and a description of the models. The research reveals that both XGBoost and Random Forest outperform the benchmark’s predictive capacity and that the KNN and the Decision Tree models have weaker performance compared to the benchmark. Hence, it can be concluded that it still makes sense to use this benchmark model, however, the more modern techniques should also be taken into consideration.Bravo, Jorge Miguel VenturaRUNVila Verde, Francisca Viçoso2021-11-03T11:56:35Z2021-10-282021-10-28T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/127084TID:202814378enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:07:12Zoai:run.unl.pt:10362/127084Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:46:02.289464Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Peer-to-peer lending: Evaluation of credit risk using Machine Learning
title Peer-to-peer lending: Evaluation of credit risk using Machine Learning
spellingShingle Peer-to-peer lending: Evaluation of credit risk using Machine Learning
Vila Verde, Francisca Viçoso
Machine Learning
Credit Scoring
Peer-to-peer lending
Ensemble methods
Single classifiers
title_short Peer-to-peer lending: Evaluation of credit risk using Machine Learning
title_full Peer-to-peer lending: Evaluation of credit risk using Machine Learning
title_fullStr Peer-to-peer lending: Evaluation of credit risk using Machine Learning
title_full_unstemmed Peer-to-peer lending: Evaluation of credit risk using Machine Learning
title_sort Peer-to-peer lending: Evaluation of credit risk using Machine Learning
author Vila Verde, Francisca Viçoso
author_facet Vila Verde, Francisca Viçoso
author_role author
dc.contributor.none.fl_str_mv Bravo, Jorge Miguel Ventura
RUN
dc.contributor.author.fl_str_mv Vila Verde, Francisca Viçoso
dc.subject.por.fl_str_mv Machine Learning
Credit Scoring
Peer-to-peer lending
Ensemble methods
Single classifiers
topic Machine Learning
Credit Scoring
Peer-to-peer lending
Ensemble methods
Single classifiers
description Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Risk Analysis and Management
publishDate 2021
dc.date.none.fl_str_mv 2021-11-03T11:56:35Z
2021-10-28
2021-10-28T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/127084
TID:202814378
url http://hdl.handle.net/10362/127084
identifier_str_mv TID:202814378
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
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reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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