Abstract
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.
Highlights
The financial sector generates a vast amount of data like customer data, logs from their financial products, transaction data that can be used in order to support decision making, together with external data, like social media data and data from websites
In order to highlight the various aspects of the use of textual mining in banking and finance, this study aims to answer three research questions: (i) What is the intellectual core of the field? (ii) Which text mining 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?
The second research question is posed as Which text mining techniques are used in the financial sector for textual mining, especially in the era of the Internet, big data and social media? in order to capture the venues for the future research as well as to provide the practitioners with the outlook on how to use text sources available to them online, and in their organizations, the third question is posed as: Which data sources are the most often used for text mining in the financial sector, and for which purposes?
Summary
The financial sector generates a vast amount of data like customer data, logs from their financial products, transaction data that can be used in order to support decision making, together with external data, like social media data and data from websites. Turner et al (2012) [2] in the Executive Report prepared for the IBM Institute for Business Value indicate that 71% of banking and financial institutions use big data analytics for generating a competitive advantage relevant for their organizations. The same authors state that in 2010 there were 36% of such banking and financial institution, indicating the increase of 97% in two years. This increase points out the relevance of big data technologies in today’s business for long-standing business challenges in the banking and financial sector. Applications of big data in the financial sector are various, including social media analysis, web analytics, risk management, fraud detection, and security intelligence. One of the possible roads to extract information from the vast amount of big data is text mining or text analytics (Pejic-Bach et al, 2019) [3]
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