Preparing for Big Data in Financial Services: Infrastructure, Talent, and Strategy

The financial services industry is experiencing a significant transformation driven by big data technologies and advanced analytics. This article examines how financial institutions are adapting their infrastructure, talent strategies, and operational frameworks to effectively leverage data-driven insights. It explores the critical components of robust data architecture, including cloud computing, data lakes, and real-time processing capabilities. The discussion extends to talent acquisition challenges and organizational models that support effective data science implementation. The article further investigates how advanced machine learning techniques are being applied across various financial domains and emphasizes the importance of aligning data initiatives with strategic business objectives. Finally, it addresses the regulatory and ethical considerations unique to financial data applications, highlighting governance frameworks that balance innovation with compliance requirements.

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Publication Details

Journal
European Journal of Computer Science and Information Technology
Published
2025-04-15
DOI
https://doi.org/10.37745/ejcsit.2013/vol13n16110
Primary Topic
FinTech, Crowdfunding, Digital Finance
Type
article
Field-Weighted Citation Impact
0.00
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article

Preparing for Big Data in Financial Services: Infrastructure, Talent, and Strategy

Parul Purwar
European Journal of Computer Science and Information Technology
FinTech, Crowdfunding, Digital Finance
article

Preparing for Big Data in Financial Services: Infrastructure, Talent, and Strategy

Parul Purwar
article en

Abstract

The financial services industry is experiencing a significant transformation driven by big data technologies and advanced analytics. This article examines how financial institutions are adapting their infrastructure, talent strategies, and operational frameworks to effectively leverage data-driven insights. It explores the critical components of robust data architecture, including cloud computing, data lakes, and real-time processing capabilities. The discussion extends to talent acquisition challenges and organizational models that support effective data science implementation. The article further investigates how advanced machine learning techniques are being applied across various financial domains and emphasizes the importance of aligning data initiatives with strategic business objectives. Finally, it addresses the regulatory and ethical considerations unique to financial data applications, highlighting governance frameworks that balance innovation with compliance requirements.

European Journal of Computer Science and Information TechnologyVol. 13(16)
Northwestern University (US)
Openalex Percentile: Top 84%
FinTech, Crowdfunding, Digital Finance
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