TY - JOUR
T1 - Bayesian Statistics for Loan Default
AU - Tham, Allan W.
AU - Kakamu, Kazuhiko
AU - Liu, Shuangzhe
N1 - Funding Information:
Kazuhiko Kakamu\u2019s research was supported by JSPS KAKENHI (grant numbers: JP20H00080 and JP20K01590).
Publisher Copyright:
© 2023 by the authors.
PY - 2023/3/15
Y1 - 2023/3/15
N2 - 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.
AB - 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.
KW - Bayesian
KW - data analytics
KW - loan application
KW - prediction
UR - http://www.scopus.com/inward/record.url?scp=85151000416&partnerID=8YFLogxK
U2 - 10.3390/jrfm16030203
DO - 10.3390/jrfm16030203
M3 - Article
AN - SCOPUS:85151000416
SN - 1911-8066
VL - 16
SP - 1
EP - 20
JO - Journal of Risk and Financial Management
JF - Journal of Risk and Financial Management
IS - 3
M1 - 203
ER -