A Comparative Study of Machine Learning Models for Default Groups Clustering and Credit Scoring Prediction for Peer-to-peer Lending
| Author | Almehaiza, Fatema Mohamed | |
|---|---|---|
| metadata.dc.contributor.department | "University of Bahrain, College of Science Environmental and Sustainable Development program | |
| metadata.dc.date.accessioned | 2026-09-20T15:44:19Z | |
| Publication date | 2023 | |
| Degree level | Master | |
| Abstract | Abstract: With the intervention of financial technology (FinTech), peer-to-peer (P2P) lending platforms have emerged as a disruptive force in the financial sector, directly connecting borrowers with lenders without the need for traditional intermediaries or collateral. P2P lending platforms heavily depend on the accuracy of creditworthiness predictions for borrowers through assessing the credit risk involved in lending. Machine Learning (ML) models provide the opportunity to improve credit scoring predictions and boost the overall efficiency and sustainability of P2P platforms. | |
| metadata.dc.identifier.other | https://digitalrepository.uob.edu.bh/id/804eef4e-6e3a-46c7-9826-5f7df5f495b3 | |
| metadata.dc.identifier.other | Drupal node 1942 | |
| metadata.dc.identifier.other | https://digitalrepository.uob.edu.bh/en/dar/comparative-study-machine-learning-models-default-groups-clustering-and-credit-scoring | |
| metadata.dc.identifier.uri | http://165.227.56.122/handle/123456789/6168 | |
| Language | en | |
| Link | https://digitalrepository.uob.edu.bh/en/_flysystem/fedora/2024-10/a-comparative-study-of-machine-learning-models-for-default-groups-clustering-and-credit-scori.pdf | |
| Title | A Comparative Study of Machine Learning Models for Default Groups Clustering and Credit Scoring Prediction for Peer-to-peer Lending | |
| Type | Master's Thesis | |
| College | College of Science | |
| Department | Environment and Sustainable Development Program |
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