Bayesian Statistics for Loan Default

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.