Credit score in default prediction fr P2P lending
This research aims to investigate the factors and criteria influencing default in Peer to Peer (P2P) lending, with a focus on providing valuable insights for the future of Fintech and contributing to industry growth and sustainability. The study examines the intention to adopt P2P lending and its...
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| Format: | Final Year Project / Dissertation / Thesis |
| Published: |
2024
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| Online Access: | http://eprints.utar.edu.my/6745/ http://eprints.utar.edu.my/6745/1/202310%2D30_FYP_SIMHUIXIAN_202310%2D30_SIM_HUI_XIAN.pdf |
| _version_ | 1848886761638330368 |
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| author | Sim, Hui Xian |
| author_facet | Sim, Hui Xian |
| author_sort | Sim, Hui Xian |
| building | UTAR Institutional Repository |
| collection | Online Access |
| description | This research aims to investigate the factors and criteria influencing default in Peer to Peer (P2P) lending, with a focus on providing valuable insights for the future of
Fintech and contributing to industry growth and sustainability. The study examines the intention to adopt P2P lending and its implications on financial decision-making. Utilizing quantitative methods, the analysis incorporates variables including loan amount, interest rate, total open-to-buy on revolving bank cards, bank card
utilization rate, number of open revolving accounts, debt to income ratio (DTI), and revolving utilization rate. Data from a Kaggle dataset for the year 2018 comprising 445 samples with charge-offs, late payments of 16-30 days, and late payments of 31-120 days is analyzed. Results indicate a highly positive relationship with revolving utilization rate and negative relationships with the number of open
revolving accounts and total open-to-buy on revolving bank cards. The implications suggest enhancing credit monitoring within credit assessment processes and implementing alternative data for more accurate evaluations. By accessing FICO scores to assess creditworthiness based on consumer payment behaviour is recommended to improve loan approval processes.
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| first_indexed | 2025-11-15T19:43:38Z |
| format | Final Year Project / Dissertation / Thesis |
| id | utar-6745 |
| institution | Universiti Tunku Abdul Rahman |
| institution_category | Local University |
| last_indexed | 2025-11-15T19:43:38Z |
| publishDate | 2024 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | utar-67452024-08-21T08:21:33Z Credit score in default prediction fr P2P lending Sim, Hui Xian H Social Sciences (General) HT Communities. Classes. Races T Technology (General) This research aims to investigate the factors and criteria influencing default in Peer to Peer (P2P) lending, with a focus on providing valuable insights for the future of Fintech and contributing to industry growth and sustainability. The study examines the intention to adopt P2P lending and its implications on financial decision-making. Utilizing quantitative methods, the analysis incorporates variables including loan amount, interest rate, total open-to-buy on revolving bank cards, bank card utilization rate, number of open revolving accounts, debt to income ratio (DTI), and revolving utilization rate. Data from a Kaggle dataset for the year 2018 comprising 445 samples with charge-offs, late payments of 16-30 days, and late payments of 31-120 days is analyzed. Results indicate a highly positive relationship with revolving utilization rate and negative relationships with the number of open revolving accounts and total open-to-buy on revolving bank cards. The implications suggest enhancing credit monitoring within credit assessment processes and implementing alternative data for more accurate evaluations. By accessing FICO scores to assess creditworthiness based on consumer payment behaviour is recommended to improve loan approval processes. 2024 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/6745/1/202310%2D30_FYP_SIMHUIXIAN_202310%2D30_SIM_HUI_XIAN.pdf Sim, Hui Xian (2024) Credit score in default prediction fr P2P lending. Final Year Project, UTAR. http://eprints.utar.edu.my/6745/ |
| spellingShingle | H Social Sciences (General) HT Communities. Classes. Races T Technology (General) Sim, Hui Xian Credit score in default prediction fr P2P lending |
| title | Credit score in default prediction fr P2P lending |
| title_full | Credit score in default prediction fr P2P lending |
| title_fullStr | Credit score in default prediction fr P2P lending |
| title_full_unstemmed | Credit score in default prediction fr P2P lending |
| title_short | Credit score in default prediction fr P2P lending |
| title_sort | credit score in default prediction fr p2p lending |
| topic | H Social Sciences (General) HT Communities. Classes. Races T Technology (General) |
| url | http://eprints.utar.edu.my/6745/ http://eprints.utar.edu.my/6745/1/202310%2D30_FYP_SIMHUIXIAN_202310%2D30_SIM_HUI_XIAN.pdf |