Machine learning techniques in bankruptcy prediction: A systematic literature review

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 mid-2023. Employing the PRISMA method, we reviewed 207 empirical studies on bankruptcy prediction. Prior extensive research has shown that the integration of more advanced techniques, such as hybrid model, enhances prediction accuracy and robustness, leading to more reliable bankruptcy forecasts. While financial ratios have traditionally played a central role in bankruptcy prediction models, this review places emphasis on the significance of incorporating non-financial ratios. Non-financial ratios capture qualitative and intangible factors such as management competence, corporate governance practices and market reputation. The inclusion of these non-financial ratios, alongside financial ratios in hybrid models, enables a more comprehensive evaluation of a firm's financial health and improves the accuracy of bankruptcy predictions. The review also addresses the challenges and limitations associated with hybrid models and the incorporation of non-financial ratios. Our research shows that there is a current trend toward the development of hybrid models that combine multiple methodologies and variables to improve bankruptcy prediction accuracy. Researchers are actively addressing the challenge of imbalanced datasets in bankruptcy prediction by exploring and developing specialized techniques for handling such data. Moreover, during the evaluation of bankruptcy prediction models, it is essential to consider a range of metrics, including sensitivity and specificity, along with other relevant metrics, to obtain a comprehensive assessment of model performance.