In response to recent recurrent crises, innovative macroprudential policies (MaPs) have been framed and implemented to address the weaknesses of market-led microprudential mechanisms and enhance the stability of financial systems. However, the effectiveness of the tools used to implement MaPs remains a critical research question. Early warning signals (EWS) serve as indicators of potential future crises. This paper explores approaches for identifying EWS to optimize the impact of MaPs, particularly in light of advances in information technology. It provides a comprehensive review of academic studies that identify effective EWS by analyzing numerical data through econometric and machine learning (ML) methods or by extracting economic insights from text using deep learning (DL) techniques - innovative methods for financial supervision and regulation. The findings, considering current regulatory practices, highlight the benefits of ML-based approaches for processing large sets of numerical data and the growing potential of text-based methods for assessing economic expectations.