Research Challenges in Financial Data Modeling and Analysis

Big data is shaking up the finance industry and could have a big impact on future research. In this special issue, we look at how big data is a combination of three things: it's big, it's big, and it's complex. We also look at how new research can use these features to tackle big questions in different areas of finance, like corporate finance, market structure, and asset prices. Plus, we have some ideas for what future research could look like. Big data is a huge part of the financial industry, with hundreds of millions of transactions happening every day. It's a growing problem for data management and analytics, so it's important to understand which financial issues big data has a big effect on. Based on these ideas, the goal of this paper was to present the current state of big data in finance as well as how different financial sectors are impacted by it. In particular, we looked at how internet finance, financial management, and internet credit service providers are impacted by big data, as well as how fraud detection, risk analysis, and financial application management are affected.

Pressacademia, 2021

Purpose-Digital infrastructure and technology advancements are steering the innovations in financial sector globally. The technology and data driven aspect has fueled the Fintech sector, evolving at the tangent of mighty finance sector and revolutionary technology domain, especially the digital technologies. The purpose of this paper is to show that most FinTech innovations, are significantly driven by big data analytics and its efficient implementation. Methodology-The use of latest ICT technologies lightens up the finance operations and services to exponential levels. Big data analytics is new and requires comprehensive studies as a research field specially in the finance domain. The intent here is to study an adoption model specially IT diffusion mode to Big data analytics that could detect key success predictors. The study tests the model for adoption of big data as novel technology and the related issues. The paper also presents a review of academic journals, literature, to study the diffusion and adoption of big data in to the finance domain. Findings-The research reflects a significant interest and utility about Big data analytics value that epitomizes the rise of Fintech phenomenon. Big data analytics may provide some competencies to the organizations that may consider its several dimensions along with its framework in the pre-adoption phase or adoption phase or implementation or diffusion phase. The research also attempts to describe the several dimensions of Big data analytics as a new technology. This shall be of good interest to the researchers, professionals, academicians and policy-makers. Conclusion-The paper first defines big data to consolidate the different discourse and literature on big data. We also reflect the point that predictive-analytics (with structured data) overshadows other forms: descriptive and prescriptive analytics (with unstructured data) which constitutes more than 90% of big data. We also reflected on analytics techniques for unstructured data: audio, video, and social media data, as well as predictive analytics. In the analysis and testing part we also performed the testing of the IT diffusion model which concludes that there are significant relationships among IT-planning, IT-implementation and IT-diffusion.

The Review of Financial Studies, 2021

Big data is revolutionizing the finance industry and has the potential to significantly shape future research in finance. This special issue contains papers following the 2019 NBER-RFS Conference on Big Data. In this introduction to the special issue, we define the “big data” phenomenon as a combination of three features: large size, high dimension, and complex structure. Using the papers in the special issue, we discuss how new research builds on these features to push the frontier on fundamental questions across areas in finance—including corporate finance, market microstructure, and asset pricing. Finally, we offer some thoughts for future research directions.

2021

A large number of EU organizations already leverage Big Data pools to drive value and investments. This trend also applies to the banking sector. As a specific example, CaixaBank currently manages more than 300 different data sources (more than 4 PetaBytes of data and increasing) and more than 700 internal and external active users and services are processing them every day. In order to harness value from such high-volume and high-variety of data, banks need to resolve several challenges, such as finding efficient ways to perform Big Data analytics and to provide solutions that help to increase the involvement of bank employees, the true decision makers. In this book chapter, we describe how these challenges are resolved by the self-service solution developed within the I-BiDaaS project. In more detail, we present three CaixaBank use cases, namely i) Analysis of relationships through IP addresses; ii) Advanced Analysis of bank transfer payment in financial terminal; and iii) Enhance...

New Horizons for a Data-Driven Economy, 2016

Open Journal of Science and Technology

Big data is a form of data with increased volume, difficult to analyze, process, and store using traditional database technologies. It has long been adopted in business and finance where a large number of bank transaction are executed daily. The emergence of big data in banking industry results to large proportion of technical improvements in the industry. However, its processing causes disruption in the banking industry. Big data analytics is the process that involves using algorithms and software tools to extract useful business information from the dataset. This study adopts big data analytics process to investigates the disruption due to big data processing in the banking industry. The study identifies, acquired, and extracted dataset of the banking industry which was analyzed using MapReduce based fraud committed due to processing of large amount of data. findings show that government employee commit more crime in comparison with the private sector employees. Finally, based on ...

Educational Administration Theory and Practice journal, 2024

In the rapidly evolving landscape of financial technology (Fintech), the advent of big data analytics has revolutionized credit risk assessment and fraud detection processes. This review research paper provides a comprehensive examination of the application of big data analytics in Fintech, focusing specifically on its role in credit risk assessment and fraud detection. By synthesizing a diverse array of academic literature, industry reports, and empirical studies, this paper offers insights into the latest developments, challenges, and future directions in this dynamic field. The review begins by elucidating the fundamental principles of big data analytics and its relevance to Fintech. It explores the key characteristics of big data, including volume, velocity, variety, and veracity, and discusses how these characteristics are leveraged to extract actionable insights for credit risk assessment and fraud detection. The paper critically evaluates the methodologies and techniques employed in big data analytics, such as machine learning algorithms, natural language processing, and network analysis, highlighting their strengths and limitations in the context of Fintech applications. Subsequently, the review delves into the specific applications of big data analytics in credit risk assessment and fraud detection. It examines how predictive analytics models are used to assess creditworthiness, identify default risks, and personalize lending decisions. Additionally, the paper investigates the role of anomaly detection algorithms and behavioral analytics in detecting fraudulent activities and mitigating financial risks. Furthermore, the review discusses the challenges and ethical considerations associated with the use of big data analytics in Fintech. Issues such as data privacy, algorithmic bias, and regulatory compliance are explored, emphasizing the need for responsible and transparent use of data-driven technologies in financial services. This review research paper underscores the transformative potential of big data analytics in Fintech, particularly in the domains of credit risk assessment and fraud detection. By harnessing the power of big data, Fintech companies can make more informed lending decisions, enhance fraud detection capabilities, and ultimately foster financial inclusion. However, it also highlights the importance of addressing ethical concerns and regulatory challenges to ensure the responsible and equitable use of big data analytics in the financial industry.

IJARCCE-ISSN (Online) 2278-1021-International Journal of Advanced Research in Computer and Communication Engineering, 2021

Over the past two decades, the financial sector has seen a shift in how individuals and businesses operate. The adoption of AI and Big Data analytics has transformed the manner in which financial institutions operate, and how they interact with customers and other institutions. Several researchers opine that the financial sector will see further transformation due to the increased adoption of technologies that enable humans to dedicate more time to innovating and performing sophisticated tasks. On a daily basis, the financial sector tracks billions of market events and generates massive and diverse amounts of data that fall under the category of Big Data. AI models and algorithms that utilize Big Data to spot patterns and glean insights in order to make decisions in areas such as portfolio strategy and fraud detection have become increasingly common in the finance sector. Organizations in the financial sector have a varying level of capability and competency with respect to the adoption and utilization of these technologies. The utilization of these technologies and the implications of their use have become the subject of debate in the financial sector. The goal of this paper is to report on the applications of AI and Big Data analytics in finance, and the ethical, organizational, and legal repercussions of the use of these technologies.

2016

In the recent years banking and financial markets are trying to learn how Big Data can help to transform their processes and organizations, improving customer intelligence, reducing risks, and meeting regulatory objectives. The collection and the analysis of new legislations, understanding if they are introducing new aspects with potential impacts on different fields, could be the basis of a system able to give support in the strategic decision making process and to evaluate the potential impacts on both management and strategic activities. Here we want to present NormaSearch, a Big Data application developed by Exprivia, an international leading company in Italy in the process consulting, technology services and information technology solutions. NormaSearch is able to analyse specifical information taken from the web, both in a structured and unstructured form, and its application in the financial fields.

The banking industry, with a large customer base and their use of mobile and other emerging technologies, has seen a surge in transactions leading to rapid generation of huge amount of data. This large amount of data presents great opportunities to the banking industry. At the same time, the industry faces huge challenges in managing the plethora technologies that are available to execute Big Data projects. Based on initial investigation, there is a gap in literature that clearly examines how the banking industry is leveraging the potentials of Big Data and challenges being encountered. Using a case study, this study seeks to fill the gap by investigating, at a more granular level, how the Banking industry is using and managing Big Data. Findings will contribute to knowledge and practice by increasing our understanding of Big Data implementation and management techniques from Banking Industry's perspective.