Research on Data-Driven Financial Risk Prediction Model Based on Cloud Computing

This article aims to explore a data-driven financial risk prediction model based on cloud computing technology. This model achieves accurate identification and prediction of corporate financial risks by integrating advanced algorithms and knowledge graph technology. This article proposes a new risk prediction algorithm model that combines multiple methods such as machine learning, deep learning, and statistical analysis to learn patterns from historical data and predict future financial risks. In addition, the application of knowledge graph enables the model to better understand the internal connections of financial data and improve the accuracy of predictions. Finally, through system simulation experiments, this paper verifies the effectiveness and stability of the proposed model in practical applications. Experimental results show that this model can significantly improve the accuracy of financial risk prediction and provide powerful decision support for enterprises. This research not only enriches the theoretical system in the field of financial risk management, but also provides new ideas and technical means for the practice of financial risk management and control of enterprises.

I. Introduction

With the development of information technology, especially the rise of cloud computing technology, the traditional financial risk management model is facing unprecedented opportunities for change. Cloud computing, with its powerful data processing capabilities and flexible resource allocation, provides new solutions for data-driven financial risk prediction. The design of financial risk management and control systems has always been a hot research topic. Literature [1] proposes a financial risk management system that integrates multiple data sources. The system achieves centralized management and efficient processing of data through a cloud computing platform, solving the problems of data dispersion and low processing efficiency in traditional systems. However, the risk prediction function of the system still needs to be strengthened. Literature [2] focuses on the innovation of risk prediction algorithms and proposes a prediction model based on machine learning. This model is trained based on historical data and can more accurately predict future financial risks. However, the adaptability of this model in complex and ever-changing financial market environments still needs further verification. Literature [3] discusses how to apply knowledge graphs to financial risk management. By constructing a financial knowledge graph, it realizes the mining and understanding of deep relationships in financial data. This approach greatly improves the accuracy and depth of risk predictions. However, the construction and maintenance costs of knowledge graphs are high, and how to reduce costs while ensuring effects is an issue that needs to be solved urgently. Literature [4] conducted a simulation test on the risk prediction model by constructing a virtual financial market environment. However, the construction of simulation environments is often limited by realistic conditions. How to reflect market conditions more truly is the focus of future research. In summary, although existing research has made certain progress in the design of financial risk management and control systems, risk prediction algorithm models, knowledge graph applications, and system simulation, there are still many challenges and room for improvement. This article will build a more comprehensive and efficient data-driven financial risk prediction model based on previous research and combined with the advantages of cloud computing technology. This model will comprehensively use advanced risk prediction algorithms, fully utilize the advantages of knowledge graphs, and continuously optimize through system simulation, in order to provide enterprises with more accurate financial risk management tools.