Might You Wake Up and Find that Your Bank Has Collapsed?

Bank failures can have far-reaching consequences on customers, employees, investors, and the entire financial ecosystem. This is the problem we plan to address in this seminar by exploring the integration of machine learning modeling with the Altman Z-Score - a well-known bankruptcy prediction model - to assess the accuracy of predicting bank failures. Using a dataset of macroeconomic and microeconomic factors for 13 failed and 13 non-failed banks, we built and compared machine learning models like Random Forest, XGBoost, and Neural Networks to determine the most effective approach for predicting financial distress. In this presentation, we will dive into this interesting fusion of statistical analysis, computational techniques, and financial forecasting, offering actionable insights for proactive risk management and strategic decision-making in today's uncertain financial world.