Bayesian Statistics for Loan Default

Allan W. Tham (), Kazuhiko Kakamu and Shuangzhe Liu
Additional contact information
Allan W. Tham: Faculty of Science and Technology, University of Canberra, Bruce, ACT 2602, Australia
Kazuhiko Kakamu: Graduate School of Economics, Nagoya City University, Yamanohata 1, Mizuho-cho, Mizuho-ku, Nagoya 467-8501, Japan
Shuangzhe Liu: Faculty of Science and Technology, University of Canberra, Bruce, ACT 2602, Australia

JRFM, 2023, vol. 16, issue 3, 1-20

Abstract: Bayesian inference has gained popularity in the last half of the twentieth century thanks to the wider applications in numerous fields such as economics, finance, physics, engineering, life sciences, environmental studies, and so forth. In this paper, we studied some key benefits of Bayesian inference and how they can be used in predicting loan default in the banking sector. Various traditional classification techniques are also presented to draw comparisons primarily in terms of the ease of interpretability and model performance. This paper includes the use of non-informative priors to attempt to arrive to the convergence of posterior distribution. Finally, with the Bayesian techniques proven to be an alternative to the classical approaches, the paper attempted to demonstrate that Bayesian techniques are indeed powerful in financial data analytics and applications.

Keywords: Bayesian; data analytics; loan application; prediction (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1911-8074/16/3/203/pdf (application/pdf)
https://www.mdpi.com/1911-8074/16/3/203/ (text/html)

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:gam:jjrfmx:v:16:y:2023:i:3:p:203-:d:1098396

Access Statistics for this article

JRFM is currently edited by Ms. Chelthy Cheng

More articles in JRFM from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().