A Big Data-Driven Information Model for Enterprise Financial Risk Management: Model Development and Empirical Validation

This study proposed the “Big Data-Driven Financial Risk Management Information Model” to address the growing complexity of enterprise financial risk in digital and globalized environments. It was motivated by limitations in existing models, such as fragmented data governance, lack of system integration, and weak empirical validation. The model employed a four-layer architecture (data, processing, analysis, and decision support) that incorporated machine learning, natural language processing, and time series analysis to identify, predict, and respond to financial risks in real time. Empirical validation using data from a medium-sized manufacturing enterprise showed the model achieved 87.3% prediction accuracy, thereby significantly outperforming traditional approaches. These findings confirmed the model's robustness in financial risk monitoring. The approach promises to transform enterprise decision-making from reactive to proactive by offering a replicable pathway for intelligent and adaptive financial governance in the era of big data.