Application of Data Mining Technology in Financial Risk Analysis

Jin, Maozhu; Wang, Yanan; Zeng, Yucheng

doi:10.1007/s11277-018-5402-5

Cited by 30 publications

(15 citation statements)

References 9 publications

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“…Also, it helps to verify and collect the data, predict credit risk status, and detect fraud [24,25,56]. Jin et al [44], [47], Peji [60], and Hajizadeh et al [28] identified that data mining technology plays vital roles in risk managing and fraud detection.…”

Section: Big Data Implications On Internet Finance and Value Creationmentioning

confidence: 99%

Current landscape and influence of big data on finance

2020

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In the age of technological innovation, various types of data are available with the advance of information technologies, and data is seen as one of the most valuable commodities in managing automation systems [13, 68]. In this sense, financial markets and technological evolution have become related to every human activity in the past few decades. Big data technology has become an integral part of the financial services industry and will continue to drive future innovation [12]. Financial innovations are also considered the fastest emerging issues in financial services. More specifically, they cover a variety of financial businesses such as online peer-to-peer lending, crowd-funding platforms, SME finance, wealth management and asset management platforms, trading management, crypto-currency, money/remittance transfer, mobile payments platforms, and so on. All of these services create thousands of pieces of data every day. Therefore,

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Section: Big Data Implications On Internet Finance and Value Creationmentioning

confidence: 99%

Current landscape and influence of big data on finance

2020

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“…Since the emergence of data mining, as the advanced data manipulation, processing, and modeling approach, the interest in its usage in finance has grown exponentially, which generated the need for literature reviews that could provide a focused outline to advantages and disadvantages of data mining utilization in finance for the researchers and practitioners (Zhang and Zhou, 2004) [13]. Numerous literature reviews were conducting focusing to different aspects of data mining applications in various fields of finance, such as stock markets predictions (Hajizadeh, Ardakani and Shahrabi, 2010) [14], financial fraud detection (Ngai et al, 2011) [15], and financial risk analysis (Jin, Wang and Zeng, 2018) [16].…”

Section: Research Questionsmentioning

confidence: 99%

Text Mining for Big Data Analysis in Financial Sector: A Literature Review

Krstić²,

et al. 2019

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Big data technologies have a strong impact on different industries, starting from the last decade, which continues nowadays, with the tendency to become omnipresent. The financial sector, as most of the other sectors, concentrated their operating activities mostly on structured data investigation. However, with the support of big data technologies, information stored in diverse sources of semi-structured and unstructured data could be harvested. Recent research and practice indicate that such information can be interesting for the decision-making process. Questions about how and to what extent research on data mining in the financial sector has developed and which tools are used for these purposes remains largely unexplored. This study aims to answer three research questions: (i) What is the intellectual core of the field? (ii) Which techniques are used in the financial sector for textual mining, especially in the era of the Internet, big data, and social media? (iii) Which data sources are the most often used for text mining in the financial sector, and for which purposes? In order to answer these questions, a qualitative analysis of literature is carried out using a systematic literature review, citation and co-citation analysis.

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“…To study the risk spillover effect between the banking industry and the securities industry, first of all, the risks faced by the banking industry and the securities industry should be studied. On this basis, the mechanism of the risk spillover effect between the banking industry and the securities industry is elaborated [12]. This article will make a further theoretical analysis of the risk theory and the spillover effect between the banking industry and the securities industry.…”

Section: Related Workmentioning

confidence: 99%

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

Zhao

2020

J Wireless Com Network

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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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Application of Data Mining Technology in Financial Risk Analysis

Cited by 30 publications

References 9 publications

Current landscape and influence of big data on finance

Current landscape and influence of big data on finance

Text Mining for Big Data Analysis in Financial Sector: A Literature Review

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

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