Financial Risk Identification Methods in the Context of Big Data

Abstract

This paper comprehensively reviews major research advances in financial risk identification, a field of growing importance due to the increasing frequency of financial disruptions across global markets.It systematically examines two primary methodological approaches: text-based and structured-data-based modeling. Textbased techniques utilize natural language processing and sentiment analysis to extract early risk signals from unstructured sources like news reports, corporate filings, and social media. Conversely, structured data methods employ statistical models and machine learning algorithms—including deep learning and ensemble methods—to identify risk patterns from quantitative financial indicators such as stock volatility, credit ratings, and accounting ratios. Following a detailed synthesis of these approaches, the paper identifies current limitations including data fragmentation and detection delays. It consequently proposes future research directions centered on integrating emerging technologies like blockchain for data integrity and IoT for real-time monitoring, while advocating for greater cross-disciplinary convergence with fields like network science and behavioral economics to develop more robust, adaptive, and holistic risk identification frameworks.