In view of the rapid development of the digital economy, the risks of financial investment in the retail industry are becoming increasingly complex and dynamic. Traditional risk management methods are unable to cope with the challenges brought by multi-source heterogeneous data. This paper focuses on building a financial investment risk management system driven by big data, and systematically explores the practical application mechanism of big data technology in risk identification, assessment and early warning. By building a fusion data processing and risk prediction system, introducing machine learning algorithms to build a multidimensional model for the financial data, market dynamics and unstructured information of retail enterprises, and then conducting empirical analysis on a large retail group, the research results show that the big data system has effectively improved the accuracy and timeliness of risk identification. Compared with traditional methods, the early warning ability has increased by more than 30%, which has played a positive role in the steady development of the financial affairs of the retail industry. From a methodological perspective, this paper adds rich content to the risk management research system, and provides the industry with an operational technical path and decision support tools.

Xinzhuo Fan. Financial Investment Risk Management System Driven by Big Data and Its Empirical Analysis in the Retail Industry. Socio-Economic Statistics Research (2025), Vol. 6, Issue 1: 64-73. https://doi.org/10.38007/SESR.2025.060107.

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