A Data-Driven Framework for Financial Risk Prediction and Control in the Digital Economy

In the era of digital economy, financial management is shifting toward data-driven decision-making. The three most important challenges are: (a) distributed data privacy constraints, (b) rapid contagion of financial risks across interconnected enterprises, and (c) transparent decision logic for multiple stakeholders. Financial Risk tackles these challenges with a framework that includes Joint Reinforcement Learning (JRL) for distributed financial decision optimization, an Adaptive Graph Neural Network (AGNN) for modeling real-time contagion effects, and a dual-channel interpretation layer to enhance transparency. Experiments were conducted using quarterly financial data from 2018 to 2023 of 300 Chinese A-share listed companies, as well as a simulated distributed dataset. The key findings indicate that JRL achieved a cumulative revenue of 60.8 billion yuan (with a privacy score of 0.92), while the AUC of AGNN reached 0.89 and stabilized errors within two hours after policy shocks. The performance of the interpretation layer has reached 85% accuracy at an average of 2.8 key features. All these findings demonstrate that the Financial Risk framework balances privacy, efficiency, risk control, and interpretability, and offers a practical paradigm for financial risk management in the digital economy.