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

Summary

This research proposes Financial Risk, a data-driven framework designed to improve financial risk prediction and control in the digital economy using distributed learning, dynamic contagion modeling, and interpretability mechanisms.

Abstract

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.

Introduction

In the digital economy era, corporate financial management will shift from experience-based practices to data-driven paradigms¹. Real-time transactions, IoT sensors, and cloud-based enterprise systems generate multidimensional financial data in a nonstop manner, opening up new vistas for precision forecasting, smart financing, and dynamic asset allocation². Still, financial data is disseminated across subsidiaries, supply chain partners, financial institutions, and regulators, and the increasingly strict legal requirements regarding data security and PIPL make centralized processing difficult. Under these circumstances, ....

Protocol

This protocol describes the steps to construct the Financial Risk framework for financial risk prediction and control in the digital economy and to reproduce the corresponding validation experiments. The protocol covers mathematical model formalization, framework module construction, workflow integration, dataset preparation, baseline setup, and evaluation metric definition, enabling reproducibility by researchers in the field. The Table of Materials summarizes all software, libraries/toolkits, hardware resources, and datasets required to reproduce this protocol, while Figure 1 provides the overall workflow for transparen....

Results

            Distributed financial decision optimization (RQ1)

Centralized DRL reached the highest cumulative revenue but at the cost of poor privacy protection with a low privacy score of 0.30. In contrast, the proposed JLR framework achieved a cumulative revenue of 60.8 billion yuan, only 7% lower than centralized DRL, while maintaining a high privacy score of 0.92. In addition, it outperformed Independent RL and FedSL on decision stability. Thus, the proposed JLR framework p.......

Discussion

The results demonstrate that the proposed framework effectively addresses three core challenges in distributed financial risk management: (i) privacy-preserving financial decision optimization, (ii) dynamic modeling of inter-enterprise risk contagion, and (iii) stakeholder-oriented interpretability under regulated data environments. Rather than treating these as isolated objectives, the framework couples joint reinforcement learning (JRL) with an adaptive graph neural network (AGNN) and a dual-channel explanation layer, .......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Financial Data Management Software	Wind Information Co., Ltd.	N/A	Provided quarterly financial data of 300 A-share companies (2018–2023)
GPU server	NVIDIA	Version A100	Hardware
High-Performance Computing Server	Dell Technologies	R7525	Used for running federated reinforcement learning and AGNN training experiments
Python		Version 3.1	Software
PyTorch	Meta AI	Version 2.1	Software
Ray RLlib		Version 2.6	Toolkit
SHAP		Latest Version	Library