Development and Application of Enterprise Financial Risk Analysis System Based on Data Mining Technology

DOI

10.2991/978-94-6463-262-0_9How to use a DOI?

Keywords

data mining; Enterprise financial risk; Machine learning algorithm; Hadoop; Computer software application

Abstract

With the rapid rise of the digital economy, the overall informatization level of enterprises is getting higher and higher, and massive data information is transformed into productivity, which gives enterprises opportunities for development and brings new challenges to internal financial management. Faced with numerous financial risk problems, traditional risk management tools and technologies have obvious shortcomings in application scope, identification efficiency and control ability. In this regard, this paper puts forward a set of construction scheme of enterprise financial risk analysis system based on data mining technology, aiming at making use of the practical advantages of digital information technologies such as big data and machine learning, and putting forward new solutions for enterprise financial risk management. The system takes Hadoop cluster as the data management and processing server, MapReduce as the data mining engine, and combines Javaweb technology to form a comprehensive application service platform integrating online application, intelligent processing, visual analysis and other functions. Practice has proved that the system constructs the corresponding enterprise financial risk identification and measurement model through data mining algorithms such as Logistic, which meets the enterprise's demand for financial risk management and improves the enterprise's ability to resist risks.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Ming Liu
AU  - Guoqiang Wu
PY  - 2023
DA  - 2023/10/09
TI  - Development and Application of Enterprise Financial Risk Analysis System Based on Data Mining Technology
BT  - Proceedings of the 3rd International Conference on Management Science and Software Engineering (ICMSSE 2023)
PB  - Atlantis Press
SP  - 75
EP  - 81
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-262-0_9
DO  - 10.2991/978-94-6463-262-0_9
ID  - Liu2023
ER  -