Big Data analysis and Finance: a literature review

In this paper, we discuss the exploitation of Big Data in finance, particularly; we discuss financial opportunities to better management and challenges related to the emergence of big data. We review various works putting big data at the service of finance using analytical or predictive techniques. Furthermore, we recall some methods suitable to handle and extract relevant information from big data

The last five years of Big Data Research in Economics, Econometrics and Finance: Identification and conceptual analysis

Today, the Big Data term has a multidimensional approach where five main characteristics stand out: volume, velocity, veracity, value and variety. It has changed from being an emerging theme to a growing research area. In this respect, this study analyses the literature on Big Data in the Economics, Econometrics and Finance field. To do that, 1.034 publications from 2015 to 2019 were evaluated using SciMAT as a bibliometric and network analysis software. SciMAT offers a complete approach of the field and evaluates the most cited and productive authors, countries and subject areas related to Big Data. Lastly, a science map is performed to understand the intellectual structure and the main research lines (themes)

Identifying big data’s opportunities, challenges, and implications in finance

One of the latest innovations in business and technology is the use of big data, as daily data is generated by billions of events. The big data issue is now considered in the accountants and finance professionals’ field as one of the most important sources for the analysis of financial products and services. This study is very innovative, aiming our research to identify the opportunities, challenges, and implications of big data in the finance area. It is our purpose to find competitive advantages in extents on which big data brings visible benefits, also pointing out the challenges that a company may face in this field, as are the cases of customers' data security or customer satisfaction processes. The identification of this kind of dynamics allows us to conclude about the big advantages of big data on these analyses and big data’s deep impact on finance. Very particularly, the big data is now commonly used by financial institutions and banks for analytical purposes in financial markets contexts. We have conducted an exploratory survey of the existing literature to highlight such connections. In the last part of our study, we also propose some directions for future research.info:eu-repo/semantics/publishedVersio

Big Data and Artificial Intelligence in Digital Finance

This open access book presents how cutting-edge digital technologies like Big Data, Machine Learning, Artificial Intelligence (AI), and Blockchain are set to disrupt the financial sector. The book illustrates how recent advances in these technologies facilitate banks, FinTech, and financial institutions to collect, process, analyze, and fully leverage the very large amounts of data that are nowadays produced and exchanged in the sector. To this end, the book also describes some more the most popular Big Data, AI and Blockchain applications in the sector, including novel applications in the areas of Know Your Customer (KYC), Personalized Wealth Management and Asset Management, Portfolio Risk Assessment, as well as variety of novel Usage-based Insurance applications based on Internet-of-Things data. Most of the presented applications have been developed, deployed and validated in real-life digital finance settings in the context of the European Commission funded INFINITECH project, which is a flagship innovation initiative for Big Data and AI in digital finance. This book is ideal for researchers and practitioners in Big Data, AI, banking and digital finance

Digital Subjectivation and Financial Markets: Criticizing Social Studies of Finance with Lazzarato

The recently rising field of Critical Data Studies is still facing fundamental questions. Among these is the enigma of digital subjectivation. Who are the subjects of Big Data? A field where this question is particularly pressing is finance. Since the 1990s traders have been steadily integrated into computerized data assemblages, which calls for an ontology that eliminates the distinction between human sovereign subjects and non-human instrumental objects. The latter subjectivize traders in pre-conscious ways, because human consciousness runs too slow to follow the volatility of the market. In response to this conundrum Social Studies of Finance has drawn on Actor-Network Theory to interpret financial markets as technically constructed networks of human and non-human actors. I argue that in order to develop an explicitly critical data study it might be advantageous to refer to Maurizio Lazzarato’s theory of machinic subjugation instead. Although both accounts describe financial digital subjectivation similarly, Lazzarato has the advantage of coupling his description to a clear critique of and resistance to finance

Big Data and Artificial Intelligence in Digital Finance

This open access book presents how cutting-edge digital technologies like Big Data, Machine Learning, Artificial Intelligence (AI), and Blockchain are set to disrupt the financial sector. The book illustrates how recent advances in these technologies facilitate banks, FinTech, and financial institutions to collect, process, analyze, and fully leverage the very large amounts of data that are nowadays produced and exchanged in the sector. To this end, the book also describes some more the most popular Big Data, AI and Blockchain applications in the sector, including novel applications in the areas of Know Your Customer (KYC), Personalized Wealth Management and Asset Management, Portfolio Risk Assessment, as well as variety of novel Usage-based Insurance applications based on Internet-of-Things data. Most of the presented applications have been developed, deployed and validated in real-life digital finance settings in the context of the European Commission funded INFINITECH project, which is a flagship innovation initiative for Big Data and AI in digital finance. This book is ideal for researchers and practitioners in Big Data, AI, banking and digital finance

Big Data in Finance: Highlights from the Big Data in Finance Conference Hosted at the University of Michigan October 27-28, 2016

How can financial data be made more accessible and more secure, as well as more useful to regulators, market participants, and the public? As new data sets are created, opportunities emerge. Vast quantities of financial data may help identify emerging risks, enable market participants and regulators to see and better understand financial networks and interconnections, enhance financial stability, bolster consumer protection, and increase access to the underserved. Data can also increase transparency in the financial system for market participants, regulators and the public. These data sets, however, can raise significant questions about security and privacy; ensuring data quality; protecting against discrimination or privacy intrusions; managing, synthesizing, presenting, and analyzing data in usable form; and sharing data among regulators, researchers, and the public. Moreover, any conflicts among regulators and financial firms over such data could create opportunities for regulatory arbitrage and gaps in understanding risk in the financial system. The Big Data in Finance Conference, co-sponsored by the federal Office of Financial Research and the University of Michigan Center on Finance, Law, and Policy, and held at the University of Michigan Law School on October 27-28, 2016, covered a number of important and timely topics in the worlds of Big Data and finance. This paper highlights several key issues and conference takeaways as originally presented by the contributors and panelists who took part

Current landscape and influence of big data on finance

Big data is one of the most recent business and technical issues in the age of technology. Hundreds of millions of events occur every day. The financial field is deeply involved in the calculation of big data events. As a result, hundreds of millions of financial transactions occur in the financial world each day. Therefore, financial practitioners and analysts consider it an emerging issue of the data management and analytics of different financial products and services. Also, big data has significant impacts on financial products and services. Therefore, identifying the financial issues where big data has a significant influence is also an important issue to explore with the influences. Based on these concepts, the objective of this paper was to show the current landscape of finance dealing with big data, and also to show how big data influences different financial sectors, more specifically, its impact on financial markets, financial institutions, and the relationship with internet finance, financial management, internet credit service companies, fraud detection, risk analysis, financial application management, and so on. The connection between big data and financial-related components will be revealed in an exploratory literature review of secondary data sources. Since big data in the financial field is an extremely new concept, future research directions will be pointed out at the end of this study