<![CDATA[Financial distress is a condition of declining financial health of a company that can develop gradually and lead to business failure if not detected early. With the increasing complexity of the business environment and the limitations of conventional statistical methods, Artificial Intelligence/AI is increasingly being adopted in the development of early warning systems (EWS) to predict financial distress. This study aims to examine the development of AI-based EWS research, identify the most widely used algorithms, and evaluate the effectiveness of AI models compared to conventional methods in predicting financial distress. The method used is a comprehensive systematic literature review of 15 relevant scientific articles. The results show that the paradigm has shifted from statistical models to machine learning and deep learning. Random Forest and Artificial Neural Network are the most widely used algorithms and have better predictive performance. This study offers a conceptual synthesis of the progress, effectiveness, and challenges of applying AI in predicting financial distress and opens opportunities for further research on the development of contextual and interpretative EWS.]]>
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