Machine learning methods for financial distress prediction: an analytical overview and implications for sustainability goals

Predicting financial difficulties provides valuable early warnings for companies, making it a significant area of study. Additionally, creditors and investors stand to benefit from being able to foresee financial distress. Machine learning (ML) models have emerged as essential tools for predicting financial trouble, leveraging expanding databases and processing capacity. This study undertakes a methodical examination of the literature to address the existing fragmentation in research on machine learning trends in financial distress prediction, while exploring their implications for decision-making effectiveness and sustainability outcomes. The originality of this paper is that no previous study has described all the methods in depth and their role in promoting sustainability, which makes a novel contribution to this study. It also employs content analysis to identify crucial themes and examines significant aspects of data preprocessing techniques. Furthermore, the study delves into key models in Financial Distress Prediction (FDP) and recent trends, exploring the advantages and disadvantages of various machine learning techniques to provide guidance for researchers, practitioners, and stakeholders in predicting financial distress and making well-informed decisions. In addition, the study highlights an emerging research gap by emphasizing the challenges and opportunities associated with applying machine learning techniques for financial distress prediction in developing countries, thereby extending the scope for future investigations.