In the wave of digital transformation, enterprise risk management faces challenges such as a sharp increase in data dimensions and accelerating risk transmission. Traditional risk control methods rely on human experience and sampling analysis, which fall short when it comes to real-time processing of multi-source heterogeneous data, let alone penetrating hidden risks in complex business scenarios. Big data analytics, through the integration of structured and unstructured data, constructs a framework capable of dynamically assessing risks, thereby providing robust support for the entire chain from risk identification to predictive decision-making. Current research primarily focuses on the integration of technology and business, but issues such as data governance deficiencies and organizational adaptation lags constrain the realization of its value. Therefore, there is an urgent need to explore a practical paradigm that balances efficiency and security, driving enterprise risk management towards intelligent and forward-looking upgrades.
Li Yuanheng (Wed,) studied this question.