PREDICTING FINANCIAL CRISIS: THE POTENTIAL OF AI IN GLOBAL MARKETS

Abstract

This paper reviews how artificial intelligence (AI) and machine learning (ML) techniques have been incorporated into the prediction of financial crises and evaluates their performance relative to econometric models. Most of the existing literature has relied on macro-financial indicators and regression approaches, but the inability to solve nonlinearities, regime changes, and high-dimensional datasets has motivated the use of AI techniques. The review surveys the conceptual and empirical literature of financial crisis forecasting, discusses model classes, and reports new developments in deep learning, hybrid models, and data fusion. Special emphasis is placed on the use of supervised and unsupervised learning, recurrent neural networks (RNN), long short-term memory (LSTM) networks, and transformer architectures, alongside the use of alternative data such as sentiment analysis and media narratives. A separate section assesses the use of scenario-driven geopolitical stress testing in portfolio risk management. In conclusion, the review describes the gaps in methodology and develops new avenues for research in model credibility, generalization across countries and cultures, and real-time update systems for forecasts. This work enhances the academic discourse around crisis forecasting while also enabling financial authorities, institutional market players, and policy decision-makers to devise tools to alert them of potential crises in the context of modern sophisticated and globalized financial systems.