Credit risk assessment is at the core of modern economies. Traditionally, it is measured by
statistical methods and manual auditing. Recent advances in financial artificial intelligence …

This paper surveys the extant literature on machine learning, artificial intelligence, and deep
learning mechanisms within the financial sphere using bibliometric methods. We considered …

Many bankruptcy prediction models for small and medium-sized enterprises (SMEs) are built
using accounting-based financial ratios. This study proposes a bankruptcy prediction model …

Credit risk assessment plays a key role for correctly supporting financial institutes in defining
their bank policies and commercial strategies. Over the last decade, the emerging of social …

Financial ratios play an important role in revealing corporate financial soundness, a role
which helps to maintain the competitive position of an enterprise, with the achievement of …

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

There is often a significant class imbalance in credit scoring datasets, mainly in portfolios of
secured loans such as mortgage loans. A class imbalance occurs when the number of non …

Enterprise credit risk prediction in the supply chain context is an important step for decision
making and early credit crisis warnings. Improving the prediction performance of this task is …

Personal credit scoring is a challenging issue. In recent years, research has shown that
machine learning has satisfactory performance in credit scoring. Because of the advantages …

The class imbalance problem occurs when one class far outnumbers the other classes,
causing most traditional classifiers perform poorly on the minority classes. To tackle this …