Developing Early Warning Systems in the Era of Company Crisis Prevention

Abstract

This study explores the application of artificial intelligence (AI) in corporate crisis prevention, with a particular focus on the digital transformation and development of Early Warning Systems (EWS). Four major thematic areas are identified for further elaboration: (1) Double and Triple AI Models: the collaboration of multiple AI systems to generate synthetic data and scenarios for crisis recognition or prevention; (2) The Role of Edge AI: real-time financial forecasting and monitoring through local data processing for immediate alerts; (3) AI-Based Crisis Prevention Frameworks and Sustainable Corporate Governance: integrating AI into improving ESG (Environmental, Social, and Governance) performance and enabling more transparent decision-making; (4) SME-Friendly AI Solutions: EWS developments within the framework of digital transformation that apply to Small and Medium-sized Enterprises. The research employs a mixed-methods approach: a review of the literature, analysis of international and Hungarian case studies, and the development of an adaptable hypothetical model for the Hungarian corporate environment. The findings demonstrate that AI-supported EWS can identify corporate risks with significantly greater accuracy and consistency compared to traditional systems. However, human expertise remains crucial for the continuous learning of the models and the appropriate handling of alerts. The study offers recommendations for companies – especially SMEs – on the implementation of AI-driven early warning mechanisms, emphasising the importance of change management, regulatory compliance, and human oversight to ensure that the adoption of AI-based EWS results in sustainable and crisis-resilient operations. Finally, it presents the concept of a pilot model intended for application in Hungary, integrating the above innovative approaches and aligning with Hungary’s digital transformation and AI strategies.