AI In Predicting Financial Crises
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Gemini said This paper addresses the critical challenge of predicting financial crises in an increasingly complex and interconnected global economy. While traditional econometric tools—such as logit and probit regressions—have historically provided a baseline for risk assessment, they often struggle to capture the non-linear relationships and systemic contagions inherent in modern financial markets. This study evaluates whether Artificial Intelligence (AI) and Machine Learning (ML) models can provide more accurate early warning signals compared to classical statistical methods. Using a quantitative research design, the study compares a traditional Logistic Regression model against three AI frameworks: Random Forest, Support Vector Machines (SVM), and Artificial Neural Networks (ANN). The models were tested using macroeconomic indicators such as GDP growth, credit-to-GDP ratios, and the VIX volatility index. The results demonstrate that AI models significantly outperform traditional methods, with the Artificial Neural Network achieving the highest accuracy (88%) compared to the logistic regression (72%). These findings suggest that AI can enhance macroprudential supervision and financial stability by identifying early signs of instability that linear models miss. However, the study concludes that while AI offers superior predictive power, the "black box" nature of these models requires them to be used as a complement to, rather than a replacement for, transparent economic theory.
Keywords
Artificial Intelligence, Early Warning Systems (EWS), Econometrics, Financial Crisis Prediction, Financial Stability, Machine Learning, Macroeconomics, Neural Networks, Random Forest, Systemic Risk