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

Area of Science:

• Financial Technology
• Data Science
• Risk Management

Background:

• Digital economy necessitates data-driven financial management.
• Key challenges include data privacy, risk contagion, and decision transparency.
• Existing frameworks struggle to balance these competing demands.

Purpose of the Study:

• To present a novel Financial Risk framework.
• To optimize distributed financial decisions, model risk contagion, and enhance transparency.
• To provide a practical paradigm for digital economy financial risk management.

Main Methods:

• Joint Reinforcement Learning (JRL) for distributed decision optimization.
• Adaptive Graph Neural Network (AGNN) for real-time contagion effect modeling.
• Dual-channel interpretation layer for enhanced decision transparency.

Main Results:

• JRL achieved 60.8 billion yuan cumulative revenue with a 0.92 privacy score.
• AGNN demonstrated 0.89 AUC and stabilized errors within two hours post-shock.
• Interpretation layer achieved 85% accuracy using an average of 2.8 key features.

Conclusions:

• The Financial Risk framework effectively balances privacy, efficiency, risk control, and interpretability.
• The framework offers a practical paradigm for financial risk management in the digital economy.
• Experimental validation on real and simulated data confirms the framework's efficacy.