THE ROLE OF ARTIFICIAL INTELLIGENCE IN PREDICTING FINANCIAL CRISES: A MACHINE LEARNING APPROACH

Abstract

Financial crises continue to be one of the most disorienting phenomena in the global economy due to the harm they cause to the banking systems, decrease production, high unemployment rates, and decline in confidence of investors. The recent developments in artificial intelligence (AI) and machine learning (ML) have increased the capacity of researchers and policymakers to identify early warning signals of financial instability. This qualitative research paper discusses the role of AI in predicting financial crises by reviewing and thematically analyzing the recent scholarly and institutional literature. The research concludes that machine learning models tend to be more effective in the crisis prediction compared to traditional econometric models since nonlinear relations and high-dimensional interaction and hidden patterns in macro-financial signals are more likely to be represented by machine learning models. Concurrently, there are significant issues of interpretability, data quality, model bias, and predictive versus causal gap. The paper concludes that AI is not supposed to replace traditional financial surveillance but enhance the existing early warnings measures using interpretable, transparent, and policy-relevant modeling frameworks.

Keywords : Artificial Intelligence, Machine Learning, Financial Crises, Early Warnings, Financial Stability, Qualitative Study