A Neural Network-Based Approach to Forecasting Bankruptcy Using Feature Importance Analysis

This research work addresses the critical problem of developing a reliable bankruptcy prediction model. Accurately predicting bankruptcy is essential to the stability of the economy, and existing methods have limitations that may lead to business failure. In this research, we aim to overcome these limitations and develop a more accurate bankruptcy forecasting method. To do this, we discuss the issue of unequal class sizes and the relative importance of various forms of misclassification. After identifying the most important financial characteristics using feature importance analysis, we employ neural networks to predict if a company will fail in the next years. To improve the model's performance, we adjust the parameters using Grid Search Cross Validation. The accuracy of bankruptcy predictions might be greatly increased with our suggested methodology, which would also increase economic stability.