Application of Data Mining Technology in Financial Risk Analysis

2018 ◽  
Vol 102 (4) ◽  
pp. 3699-3713 ◽  
Author(s):  
Maozhu Jin ◽  
Yanan Wang ◽  
Yucheng Zeng
Keyword(s):  
Data Mining ◽  
Risk Analysis ◽  
Financial Risk ◽  
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.

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.

Design and Realization on the Financial Early-Warning Analysis System of Colleges and Universities Based on Data Mining Technology

2014 ◽  
Vol 623 ◽  
pp. 229-233 ◽  
Author(s):  
De Jiang Qi ◽  
Hai Yan Hu
Keyword(s):  
Data Mining ◽  
Early Warning ◽  
Financial Management ◽  
Financial Risk ◽  
Warning System ◽  
Colleges And Universities ◽  
Mining Technology ◽  
Id3 Algorithm ◽  
Financial Management System ◽  
Analysis System

In this thesis, in order to solve the student arrearage problems in colleges and universities, risk weight factor is introduced to improve ID3 algorithm through the research on data mining technology and the combination with financial management system of colleges and universities so that ID3 decision-making tree algorithm can classify based on the risk weights of all the factors of the financial data; the early warning system scheme on the student arrearage problems in colleges and universities is designed so as to predict the high-risk defaulting students dynamically and accurately and lay scientific foundations for avoiding financial risk in colleges and universities.

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

2020 ◽  
Vol 2020 (1) ◽  
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

AbstractWith 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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