Loan default prediction using machine learning algorithms: a systematic literature review 2020 -2023

Soomro, Anam and Zakariyah, Habeebullah and Aftab, S.M.A. and Muflehi, Mohamad and Shah, Asadullah and Meraj, Syeda (2024) Loan default prediction using machine learning algorithms: a systematic literature review 2020 -2023. Pakistan Journal of Life and Social Sciences, 24 (2). pp. 6234-6353. ISSN 1727-4915 E-ISSN 2221-7630

PDF (Article) - Published Version Restricted to Repository staff only Download (800kB) | Request a copy

Abstract

This study conducts a systematic literature review (SLR) on the prediction of loan defaults using machine learning algorithms (MLAs) from 2020 to 2023. It critically examines the transition from traditional statistical models to advanced ML techniques in assessing credit risk, with a focus on the banking sector's need for reliable default prediction methods. The review highlights the predominance of the Random Forest algorithm for its superior handling of complex datasets and predictive accuracy across various studies. Additionally, it identifies Kaggle as a crucial source for research datasets, underlining the importance of accessible and comprehensive data in developing effective predictive models. The paper also outlines future research directions, emphasizing the integration of big data analytics, the application of sophisticated ensemble methods, and the potential of deep learning technologies. Acknowledging certain limitations such as the study's temporal focus and database selection criteria, it calls for ongoing research to explore emerging trends and methodologies. The findings aim to guide researchers and practitioners in enhancing loan default prediction models, contributing to more effective credit risk management strategies.

Item Type:	Article (Journal)
Uncontrolled Keywords:	Loan default prediction Machine learning algorithms Microfinance banks
Subjects:	T Technology > T Technology (General) > T10.5 Communication of technical information
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology > Department of Information System Kulliyyah of Information and Communication Technology > Department of Information System
Depositing User:	Prof Asadullah Shah
Date Deposited:	01 Oct 2024 11:28
Last Modified:	01 Oct 2024 14:23
URI:	http://irep.iium.edu.my/id/eprint/114757

Actions (login required)

View Item