A Comparative Study of Machine Learning Models for Default Groups Clustering and Credit Scoring Prediction for Peer-to-peer Lending

وكيل مرتبط
Hewahi, Nabil Mahmood , مشرف الرسالة العلمية
اللغة
الأنجليزية
مدى
[1], 12, 110, [10] Pages
مكان المؤسسة
Sakhir, Bahrain
نوع الرسالة الجامعية
Theses (Master)
الجهه المانحه
"University of Bahrain, College of Science Environmental and Sustainable Development program
الملخص الإنجليزي
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.
المجموعة
المعرف
https://digitalrepository.uob.edu.bh/id/804eef4e-6e3a-46c7-9826-5f7df5f495b3