Risk Analysis of Mortgage Loan Default for Bank Customers and AI Machine Learning

Ming-Tsung Hung and Huai-Chun Lo

Journal of Applied Finance & Banking, 2024, vol. 14, issue 6, 3

Abstract: Risk Analysis of Mortgage Loan Default for Banks is a crucial issue. On one hand, it concerns the quality of the bank's credit decisions, and on the other hand, it affects the rights of homebuyers to obtain financial support. In 2008, the U.S. subprime mortgage crisis sparked a global financial meltdown. What began with the collapse of the housing market quickly spread throughout the global financial system, resulting in the failure of numerous banks and a widespread economic recession. Recently, the banking sector has increasingly leveraged Artificial Intelligence (AI) and Machine Learning (ML) to enhance decision-making processes, particularly in assessing the risk of mortgage loan defaults. This paper aims to explore the application of ML techniques to predict and analyze the risk of default among bank customers, thereby enabling financial institutions to make more accurate and informed lending decisions. Â

Keywords: Mortgage; Loan; Default; Risk; Analysis; AI; ML; K-MANS; LTV. (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.scienpress.com/Upload/JAFB%2fVol%2014_6_3.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spt:apfiba:v:14:y:2024:i:6:f:14_6_3

Access Statistics for this article

More articles in Journal of Applied Finance & Banking from SCIENPRESS Ltd
Bibliographic data for series maintained by Eleftherios Spyromitros-Xioufis ().