Use of Machine Learning in Early Warning Systems for Financial Crises Within the Global Financial System

Renato Salvador Coutinho, Paulo Maurício Selig, Aran B. Tcholakian Morales and Fernando Ostuni Gauthier

Chapter 3 in AI-Driven Revolution:Transforming the Business Landscape, 2025, pp 39-63 from World Scientific Publishing Co. Pte. Ltd.

Abstract: The aim of this chapter is to map the empirical knowledge to verify whether early warning systems (EWS) that use machine learning (ML) techniques show better predictive performance in forecasting financial crises compared to those developed using traditional techniques based on the practices of institutions in the global financial system. To uncover trends and practices related to the topic, we identified a need to explore the so-called gray literature (GL) through the multivocal literature review (MLR) method, given that the referenced literature is predominantly found in the databases of monetary authorities and supranational institutions of the financial system. This knowledge extraction allowed us to find indications and a reasonable consensus that the predictive capacity of EWS for financial crises is leveraged by ML. Consequently, the application of these techniques to develop EWS represents, for macroprudential policymakers, a huge opportunity for improving evidence-based decision-making.

Keywords: Artificial Intelligence; Data Analytics; AI; Digital Landscape; Organizational Strategies; AI Technologies; Machine Learning; Natural Language Processing; Robotics; Digital Transformation; Business Models; Efficiency; Value Propositions; Advanced Analytics; Predictive Modelling; Customer Experiences; AI-driven; Ethical AI; Data Privacy; Algorithmic Bias; Regulation Compliance; Responsible AI; Sustainable AI; Practical Applications; Business Innovation; Emerging Technologies; Industry 4.0; High Tech; Ethics Regulation; Business Leadership; Pattern Recognition; Information Technology; Entrepreneurs; Management (search for similar items in EconPapers)
JEL-codes: L1 L2 L21 L26 M1 (search for similar items in EconPapers)
Date: 2025
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