The Use of Machine Learning Combined with Data Mining Technology in Financial Risk Prevention

2021 ◽  
Author(s):  
Bo Gao
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Financial Risk ◽  
Risk Prevention ◽  
Mining Technology

scholarly journals Application of Data Mining in the Guidance of Sports Training

2013 ◽  
Vol 765-767 ◽  
pp. 1518-1523
Author(s):  
Fan Hui Meng ◽  
Qing Li Li
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Experimental Data ◽  
Data Mining ◽  
Statistical Learning ◽  
Athletic Performance ◽  
Sports Training ◽  
Mining Technology ◽  
Broad Application ◽  
Use Of Data

Data mining is the techniques of finding the potential law from the data by machine learning and statistical learning .This paper focuses on a number of problems existed in the currents ports training, discusses the application principle of the data mining technology in sports training, and applies the critical neural networks for forecasting the performances of the athletes .Experimental data show that prediction of athletic performance by the use of neural network has very good approximation ability. It shows a broad application space of the use of data mining technology.

Research on the Application of Computer Data Mining Technology in the Financial Risk Management

2014 ◽  
Vol 513-517 ◽  
pp. 1940-1943
Author(s):  
Li Hong Yu ◽  
Ya Li Xu ◽  
Lin Dai
Keyword(s):  
Data Mining ◽  
Risk Management ◽  
Financial Risk ◽  
Customer Relationship ◽  
Financial Industry ◽  
Mining Technology ◽  
Typical Application ◽  
Computer Data ◽  
Financial Risk Management ◽  
Implicit Data

The computer data mining technology plays an important role in the financial risk management. It can extract the implicit data that people don't know in advance, in the mean time, and potentially useful information and knowledge for managers to provide decision-making reference. This paper introduces the concept of data mining, the process and main technology first, and then introduces the typical application of data mining in the financial risk management, such as customer relationship management, credit risk assessment and financial crisis early warning analysis. At last, it has a summary to provide the risk management for the financial industry.

Study on the Financial Audit Risk Prevention Based on Data Mining Technology

2014 ◽  
Vol 608-609 ◽  
pp. 351-354
Author(s):  
Bi Zhu Qin
Keyword(s):  
Data Mining ◽  
Financial Crisis ◽  
Global Economy ◽  
Risk Prevention ◽  
Financial Industry ◽  
Mining Technology ◽  
Audit Risk ◽  
Modern Economy ◽  
Financial Audit ◽  
Audit Technology

Along with the development of the modern economy, the financial industry is becoming more and more important. However, the global economy has begun to decline after the financial crisis occurred, so that the financial industry is seriously hit, the financial audit risks are greatly increased, and also the security problems of financial audit have gradually become the focus in the eyes of people. In this paper, the risks in the current financial audit and the necessary measures of preventing these risks through data mining technology are analyzed in depth from the perspective of data mining technology as advanced audit technology.

scholarly journals Research on Distributed Data Stream Mining of Financial Risk Based on Double Privacy Protection

2020 ◽  
Author(s):  
Yuhao Zhao
Keyword(s):  
Data Mining ◽  
Data Streams ◽  
Privacy Protection ◽  
Data Stream ◽  
Financial Risk ◽  
Data Stream Mining ◽  
Distributed Data ◽  
Stream Mining ◽  
Mining Technology ◽  
Distributed Data Streams

Abstract With the advancement of network technology and large-scale computing, distributed data streams have been widely used in the application of financial risk analysis. However, while data mining reveals financial models, it also increasingly poses a threat to privacy. Therefore, how to prevent privacy leakage during the efficient mining process poses new challenges to the data mining technology. This article is mainly aimed at the current privacy data leakage in financial data mining, combined with existing data mining technology to study data mining and privacy protection. First, a data mining model for dual privacy protection is defined, which can better meet the characteristics of distributed data streams while achieving privacy protection effects. Secondly, a privacy-oriented data stream mining algorithm is proposed, which uses random interference technology to effectively protect the original sensitive data. Finally, the analysis and discussion of the algorithm in this paper through simulation experiments show that the algorithm is feasible and effective, and can better adapt to the distributed data flow distribution and dynamic characteristics, while achieving better privacy protection effects, effectively Reduced communication load.

scholarly journals Research on wireless distributed financial risk data stream mining based on dual privacy protection

2020 ◽  
Author(s):  
Yuhao Zhao
Keyword(s):  
Data Mining ◽  
Data Streams ◽  
Privacy Protection ◽  
Data Stream ◽  
Financial Risk ◽  
Data Stream Mining ◽  
Distributed Data ◽  
Stream Mining ◽  
Mining Technology ◽  
Distributed Data Streams

Abstract With the advancement of network technology and large-scale computing, distributed data streams have been widely used in the application of financial risk analysis. However, while data mining reveals financial models, it also increasingly poses a threat to privacy. Therefore, how to prevent privacy leakage during the efficient mining process poses new challenges to the data mining technology. This article is mainly aimed at the current privacy data leakage in financial data mining, combined with existing data mining technology to study data mining and privacy protection. First, a data mining model for dual privacy protection is defined, which can better meet the characteristics of distributed data streams while achieving privacy protection effects. Secondly, a privacy-oriented data stream mining algorithm is proposed, which uses random interference technology to effectively protect the original sensitive data. Finally, the analysis and discussion of the algorithm in this paper through simulation experiments show that the algorithm is feasible and effective, and can better adapt to the distributed data flow distribution and dynamic characteristics, while achieving better privacy protection effects, effectively Reduced communication load.

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