Bankruptcy Prediction in the Polish Banking Industry Using Principal Component Analysis and BP Neural Network

With the rapid growth of the international banking industry, bank failures can lead to severe economic losses and social impacts. Although existing measures to address such failures are well-developed, timely prediction can significantly mitigate these effects. This study analyzes key indicators influencing bank failure through data analysis and correlation analysis, then develops a neural network-based risk prediction model to estimate failure probabilities. First, we extracted 64 indicators from the dataset, identified the most relevant indicators using the entropy weight method, and established a bank efficiency evaluation formula to determine the failure threshold. Next, we applied principal component analysis (PCA) to reduce dimensionality and derive a comprehensive scoring formula. Based on these findings, we constructed a machine learning model in MATLAB to predict bank failures. Finally, the model was used to predict the failure probabilities of all banks and identify 20 representative existing and failed banks. The developed models effectively predict bank failure risks and demonstrate strong applicability across different scenarios.