The Role of Big Data, Data Science and Data Analytics in Financial Engineering

Chakravaram, Venkamaraju; Rao, G Vidya Sagar; Jangirala, Srinivas; Ratnakaram, Sunitha

doi:10.1145/3341620.3341630

Cited by 9 publications

(3 citation statements)

References 6 publications

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“…The study emphasizes the role of big data in creating financially engineered products and services, enhancing fraud detection, risk management, and customer experience. The integration of big data with blockchain technology, AI, and machine learning tools has revolutionized the BFSI sector, offering a digital experience to stakeholders and customers alike (Chakravaram, 2020).…”

Section: Exploration Of Current Trends In Big Data Analytics In the F...mentioning

confidence: 99%

Big data analytics in the financial services industry: Trends, challenges, and future prospects: A review

Samuel Aderemi,

David Olanrewaju Olutimehin,

Uchenna Innocent Nnaomah

et al. 2024

Int. J. Sci. Technol. Res. Arch

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This paper provides a comprehensive review of the evolving role of big data analytics within the financial services industry, highlighting current trends, inherent challenges, and future prospects. The objective is to synthesize existing research and practices to understand how big data analytics is transforming financial services, including banking, insurance, and investment sectors. Through a systematic examination of peer-reviewed articles, industry reports, and case studies, the research methodology encompasses both qualitative and quantitative analyses to offer insights into the adoption, implementation, and impact of big data analytics. Key findings reveal that big data analytics is pivotal in driving innovation, enhancing customer experiences, and improving operational efficiency in financial services. It plays a critical role in risk management, fraud detection, personalized financial advice, and algorithmic trading, among other areas. However, the industry faces significant challenges, including data privacy concerns, regulatory compliance, technological infrastructure requirements, and the need for skilled professionals. The paper concludes that while big data analytics offers substantial opportunities for the financial services industry, realizing its full potential requires addressing existing challenges through strategic investments in technology, human capital, and data governance. The future prospects of big data analytics in financial services are promising, contingent upon the industry's ability to adapt to rapid technological advancements and evolving regulatory landscapes. This review underscores the importance of continued research and collaboration between industry stakeholders to leverage big data analytics for sustainable growth and competitiveness.

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Section: Exploration Of Current Trends In Big Data Analytics In the F...mentioning

confidence: 99%

Big data analytics in the financial services industry: Trends, challenges, and future prospects: A review

Samuel Aderemi,

David Olanrewaju Olutimehin,

Uchenna Innocent Nnaomah

et al. 2024

Int. J. Sci. Technol. Res. Arch

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“…A 2015 Organisation for Economic Co-operation and Development (OECD) report identified "data-driven innovation" (DDI) as having a central driving role in twenty-first century economies, defining DDI as "the use of data and analytics to improve and foster new products, processes, organisational methods and markets" [Emphasis from original report]. Data science deployments are still what might be called first-generation, but their impact in many areas are already being felt: global sustainability [13], power and energy systems [4], biological and biomedical systems [37], health sciences and health informatics [9], finance and insurance [7], smart cities [32],…”

Section: Introductionmentioning

confidence: 99%

Data Science: A Systematic Treatment

Özsu

2023

Preprint

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Fig. 1. Data Science Word CloudThere has been an increasing recognition of the value of data and of data-based decision making. As a consequence, the development of data science as a field of study has intensified in recent years. However, there is no systematic and comprehensive treatment and understanding of data science. This article describes a systematic and end-to-end framing of the field based on an inclusive definition.It identifies the core components making up the data science ecosystem, presents its lifecycle modeling the development process, and argues its interdisciplinarity.

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“…Data science has played a major role in business, finance, healthcare, science, engineering, etc. [1,2]. In these fields, data can be studied, analyzed, and used to benefit society.…”

Section: Introductionmentioning

confidence: 99%

Data Science in Finance: Challenges and Opportunities

Zheng,

Gildea,

Chai

et al. 2023

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Data science has become increasingly popular due to emerging technologies, including generative AI, big data, deep learning, etc. It can provide insights from data that are hard to determine from a human perspective. Data science in finance helps to provide more personal and safer experiences for customers and develop cutting-edge solutions for a company. This paper surveys the challenges and opportunities in applying data science to finance. It provides a state-of-the-art review of financial technologies, algorithmic trading, and fraud detection. Also, the paper identifies two research topics. One is how to use generative AI in algorithmic trading. The other is how to apply it to fraud detection. Last but not least, the paper discusses the challenges posed by generative AI, such as the ethical considerations, potential biases, and data security.

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The Role of Big Data, Data Science and Data Analytics in Financial Engineering

Cited by 9 publications

References 6 publications

Big data analytics in the financial services industry: Trends, challenges, and future prospects: A review

Big data analytics in the financial services industry: Trends, challenges, and future prospects: A review

Data Science: A Systematic Treatment

Data Science in Finance: Challenges and Opportunities

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