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Research and Design of a Financial Intelligent Risk Control Platform Based on Big Data Analysis and Deep Machine Learning

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  • Shuochen Bi
  • Yufan Lian
  • Ziyue Wang

Abstract

In the financial field of the United States, the application of big data technology has become one of the important means for financial institutions to enhance competitiveness and reduce risks. The core objective of this article is to explore how to fully utilize big data technology to achieve complete integration of internal and external data of financial institutions, and create an efficient and reliable platform for big data collection, storage, and analysis. With the continuous expansion and innovation of financial business, traditional risk management models are no longer able to meet the increasingly complex market demands. This article adopts big data mining and real-time streaming data processing technology to monitor, analyze, and alert various business data. Through statistical analysis of historical data and precise mining of customer transaction behavior and relationships, potential risks can be more accurately identified and timely responses can be made. This article designs and implements a financial big data intelligent risk control platform. This platform not only achieves effective integration, storage, and analysis of internal and external data of financial institutions, but also intelligently displays customer characteristics and their related relationships, as well as intelligent supervision of various risk information

Suggested Citation

  • Shuochen Bi & Yufan Lian & Ziyue Wang, 2024. "Research and Design of a Financial Intelligent Risk Control Platform Based on Big Data Analysis and Deep Machine Learning," Papers 2409.10331, arXiv.org.
    • Handle: RePEc:arx:papers:2409.10331
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    References listed on IDEAS

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    1. Zheng Zhang & Yingsheng Ji & Jiachen Shen & Xi Zhang & Guangwen Yang, 2022. "Heterogeneous Information Network based Default Analysis on Banking Micro and Small Enterprise Users," Papers 2204.11849, arXiv.org, revised May 2022.
    2. Zhijun Chen, 2022. "Privacy Costs and Consumer Data Acquisition: An Economic Analysis of Data Privacy Regulation," Monash Economics Working Papers 2022-07, Monash University, Department of Economics.
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