Abstract
Amid the rapid digitalization of the banking sector, the level of threats related to cyber risks, fraud, and data breaches is rising significantly, highlighting the need to implement effective tools to prevent them. This article examines Early Warning System (EWS) as a key element in ensuring the financial security of banks in the digital environment. The aim of the study is to analyze modern approaches to building an EWS and to develop a conceptual model capable of enhancing the resilience of second-tier banks to external and internal threats. The paper summarizes existing scientific approaches to the classification of digital risks and examines key threat indicators and mechanisms for their real-time monitoring. Particular attention is paid to the application of artificial intelligence and big data analysis technologies for detecting anomalies and predicting potential attacks. An early warning system architecture is proposed, comprising data collection and processing, an analytical module, a risk assessment system, and an alert mechanism. The research results show that the implementation of an EWS significantly improves the effectiveness of risk management, reduces the likelihood of financial losses, and ensures the sustainable operation of banks in the context of digital transformation.