An Overview of the Use of Deep Learning Algorithms to Predict Bankruptcy

The financial forecasting of different firms in the area of financial status aims to determine whether the company will go bankrupt in the near future or not. This is a critical problem for these companies. Several companies have shown a strong interest in this area, particularly since they are concerned about the future of their companies from a financial perspective and want to determine whether or not they will go out of business. Therefore, in the work that we have done, we have presented three well-known technologies of deep learning in conjunction with ensemble classifiers and boosting ensemble classifiers for the purpose of failure prediction. During our investigation, we used an uneven dataset consisting of businesses from Spain, Poland, and Taiwan. In addition to this, we applied approaches such as oversampling, hybrid balancing, and clustering-based balancing to get rid of the inconsistent data. When taking into account a real-life financial dataset with an appropriate amount of complexity, it was discovered that the MLP-6L model with the SOMTE-ENN balancing approach had the most remarkable performance when measured against the metrics.