Advanced personal bankruptcy prediction using tree-based deep learning models

Abstract

Due to the unstable economic conditions, worsened by the post-COVID-19 environment and persistent foreign wars in 2024, financial institutions have growing difficulties in accurately predicting customer default probability. This study examines the use of sophisticated tree-based deep learning and deep neural network models for forecasting personal bankruptcy. This research utilises a dataset of roughly 9,800 individuals from Vietnamese financial institutions, spanning from 2012 to 2022, to evaluate the efficacy of models including neural decision tree, deep forest, tabular convolutional neural networks (TBCNN), and neural oblivious decision ensembles (NODE). The results demonstrate that the Deep Forest model far surpasses its competitors, providing nearly flawless predicted accuracy and enhanced interpretability. The findings highlight the efficacy of tree-based deep learning and deep neural network models as effective instruments for financial risk management, especially in volatile and unpredictable economic environments.