Abstract: The growing popularity and availability of online lending platforms have attracted more borrowers and lenders. There have been several studies focusing on analyzing loan risks in the financial industry, however, defaulting loans still remains an issue that needs more attention. Hence, this research aims to develop an intelligent prediction model that is able to predict risky loans and default borrowers, named the Default Borrowers Detection Model (DefBDet). We seek to help loan lending platforms to approve lending loans to those who are expected to comply with re-payments at the agreed time. Previous works developed a binary classification prediction model (either default or repaid loan). Repaid loans include loans being repaid on or after the loan deadline date. DefBDet, on the other hand, is a novel model, it can predict a loan status based on a multi-classification bases rather than a binary class bases. Hence, it can additionally identify expected late repaid loans, so that special conditions are assigned before loan being approved. This study employs seven different Machine Learning models, using a real-world dataset from 2009-2022 consisting of around 255k loan requests. Statistical measures such as Recall, Precision, and F-measure have been used for models' evaluation. Results show that Random Forest has achieved the highest performance of 85%.

Fooz Alghamdi and Nora Alkhamees, “DefBDet: An Intelligent Default Borrowers Detection Model” International Journal of Advanced Computer Science and Applications(IJACSA), 14(7), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140753

@article{Alghamdi2023,
title = {DefBDet: An Intelligent Default Borrowers Detection Model},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140753},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140753},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {7},
author = {Fooz Alghamdi and Nora Alkhamees}
}