The purpose of our systematic review is to examine the recently published literature on the
Internet of Manufacturing Things (IoMT), and integrate the insights it configures on deep …

The main objective of this systematic literature review is to unveil the prevailing trend of
employing cutting-edge models for bankruptcy prediction for a period spanning from 2012 to …

External stakeholders require accurate and explainable financial distress prediction (FDP)
models. Complex machine learning algorithms offer high accuracy, but most of them lack …

The area of corporate bankruptcy prediction attains high economic importance, as it affects
many stakeholders. The prediction of corporate bankruptcy has been extensively studied in …

Machine learning models have increasingly been used in bankruptcy prediction. However,
the observed historical data of bankrupt companies are often affected by data imbalance …

The risk of corporate financial distress negatively affects the operation of the enterprise itself
and can change the financial performance of all other partners that come into close or wider …

The issue of prediction of financial state, or especially the threat of the financial distress of
companies, is very topical not only for the management of the companies to take the …

The current research aims to launch effective accounting fraud detection models using
imbalanced ensemble learning algorithms for China A‐Share listed firms. Based on a …

The primary objective of this research is to detect accounting fraud in Chinese family firms
through the utilization of imbalanced ensemble learning algorithms. It serves as the first …

There is an increasing interest in machine learning for bankruptcy prediction with more and
more researchers contributing to the literature. Although there is a considerable amount of …