Default Determinant Factors in Peer-to-Peer Lending

Bibliographic Details
Main Author: Albuquerque, Ana Rita Figueiredo
Publication Date: 2023
Format: Master thesis
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/152140
Summary: Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Information Analysis and Management
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spelling Default Determinant Factors in Peer-to-Peer LendingRiskBig DataP2P LendingDefault PredictionMachine LearningLogistic RegressionDecision TreeRandom ForestDissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Information Analysis and ManagementThe decision to grant a loan depends on the lender’s evaluation of the borrower’s ability to repay. Loan default in online banking has been a relevant research topic in recent years as lending has expanded to online platforms and mobile applications where it is performed on a Peer-to-Peer (P2P) basis. The present study considers a merge of two “Lending club loan data” versions; one contains loans issued through 2007–2015 and another version through 2012–2020. The merge of these two datasets with removing the duplicates gave us a dataset consisting of approximately 2,925,493 borrower records and 142 features, which comprises the period between 2007 and the 3rd quarter of 2020. In addition, and to ensure the effectiveness of the modelling, a “Prosper” dataset was analysed, consisting of approximately 1,113,937 borrower records and 81 features, comprising the period between 2006 and 1st quarter of 2014. For both periods, a set of macroeconomic variables were modelled to identify whether these would impact the loan repayment. Given its high underlying risk, this form of lending is a relevant area to study how the various characteristics of the obligor may influence its future repayment behaviour. The core of this dissertation is to understand, through machine learning techniques, the variables that may warn the lender about a potential default and thus make the transaction less risky. This study started with a systematic literature review and tried to summarize the most common algorithms used in other studies and their characteristics. Through our analysis, we conclude that the borrower assessment variables are significant predictors, translating into the effectiveness of the credit risk assessment performed by the platforms. In addition, it is observed that the short-term interest rate and GDP are significant for both datasets, being of most relevance in the smaller universe, the Prosper dataset.Ashofteh, AfshinRUNAlbuquerque, Ana Rita Figueiredo2023-04-102026-04-10T00:00:00Z2023-04-10T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/152140TID:203268652enginfo:eu-repo/semantics/embargoedAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-22T18:11:03Zoai:run.unl.pt:10362/152140Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:41:21.851594Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Default Determinant Factors in Peer-to-Peer Lending
title Default Determinant Factors in Peer-to-Peer Lending
spellingShingle Default Determinant Factors in Peer-to-Peer Lending
Albuquerque, Ana Rita Figueiredo
Risk
Big Data
P2P Lending
Default Prediction
Machine Learning
Logistic Regression
Decision Tree
Random Forest
title_short Default Determinant Factors in Peer-to-Peer Lending
title_full Default Determinant Factors in Peer-to-Peer Lending
title_fullStr Default Determinant Factors in Peer-to-Peer Lending
title_full_unstemmed Default Determinant Factors in Peer-to-Peer Lending
title_sort Default Determinant Factors in Peer-to-Peer Lending
author Albuquerque, Ana Rita Figueiredo
author_facet Albuquerque, Ana Rita Figueiredo
author_role author
dc.contributor.none.fl_str_mv Ashofteh, Afshin
RUN
dc.contributor.author.fl_str_mv Albuquerque, Ana Rita Figueiredo
dc.subject.por.fl_str_mv Risk
Big Data
P2P Lending
Default Prediction
Machine Learning
Logistic Regression
Decision Tree
Random Forest
topic Risk
Big Data
P2P Lending
Default Prediction
Machine Learning
Logistic Regression
Decision Tree
Random Forest
description Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Information Analysis and Management
publishDate 2023
dc.date.none.fl_str_mv 2023-04-10
2023-04-10T00:00:00Z
2026-04-10T00:00:00Z
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/10362/152140
TID:203268652
url http://hdl.handle.net/10362/152140
identifier_str_mv TID:203268652
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/embargoedAccess
eu_rights_str_mv embargoedAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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