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Abstract
Introduction
Data Integration
Data Quality Management
Data Analytics
Conclusion
References
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Psychology
Cognitive Psychology

Research Challenges in Financial Data Modeling and Analysis

Profile image of Sanjiv DasSanjiv Das

2017, Big data

https://doi.org/10.1089/BIG.2016.0074
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23 pages

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Abstract

Significant research challenges must be addressed in the cleaning, transformation, integration, modeling, and analytics of Big Data sources for finance. This article surveys the progress made so far in this direction and obstacles yet to be overcome. These are issues that are of interest to data-driven financial institutions in both corporate finance and consumer finance. These challenges are also of interest to the legal profession as well as to regulators. The discussion is relevant to technology firms that support the growing field of FinTech.

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Publishing House "Scientific and Educational Initiative"

Economic Consultant, 2024

Introduction. The use of big data in the financial sector is relevant for enhancing operational efficiency, risk management, fraud prevention, and customer experience. This study is aimed at providing a theoretical conceptualization of big data, identifying its benefits, and assessing the risks associated with its adoption by financial institutions. Materials and methods. The materials used in the study were as follows: peer-reviewed journal publications in finance, economics, and data analytics; reports from financial institutions and consulting firms on big data applications in finance. When processing information, methods of theoretical analysis, classification, and synthesis were used. Results. It has been revealed that big data allows for enhanced analytical research quality, predictive modeling of economic trends and market fluctuations, comprehensive market dynamics analysis, medical data analytics for improved diagnostics and treatment selection, predictive maintenance in manufacturing through sensor data analysis, development of socio-economic programs at governmental levels, fraud and corruption detection in financial systems, etc. The analysis substantiates both the rapid evolution of big data technologies and their strategic value for financial sector applications. Through literature review, the authors propose a novel definition of big data technology specific to financial institutions, incorporating distinctive features and advantages relevant to this sector. Examination of current big data implementations in finance reveals core benefits alongside existing limitations of these technologies. Conclusion. Big data applications in finance contribute to process optimization and cost reduction, advanced risk management capabilities, personalized service offerings, fraud detection and prevention, regulatory compliance enhancement. These technologies facilitate more accurate market trend forecasting and datadriven decision making.

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Big Data in Finance
Mao Ye

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.

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Big Data Analytics in the Banking Sector: Guidelines and Lessons Learned from the CaixaBank Case
Ilias Spais

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...

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