IIARD International Institute of Academic Research and Development

IIARD INTERNATIONAL JOURNAL OF BANKING AND FINANCE RESEARCH (IJBFR )

E-ISSN 2695-1886
P-ISSN 2672-4979
VOL. 10 NO. 7 2024
DOI: 10.56201/ijbfr.v10.no7.2024.pg61.74

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Big Data Management in Commercial Banking: A Developmental Appraisal of Relevant Underpinning Theories

EFUNTADE, Olubunmi Omotayo, Ph.D. EFUNTADE, Alani Olusegun, Ph.D.

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Abstract

This is a conceptual and theoretical review paper that explore big data which is referred to large volumes of structured, semi-structured, and unstructured data that inundates a business on a day- to-day basis and management in banking sector of Nigeria. Big data draws heavily from information theory, which quantifies the amount of uncertainty involved in the value of a random variable. Big data analytics helps banks identify and mitigate various risks, including credit, market, operational, and regulatory risks. By analyzing historical data and real-time transactions, banks can detect fraud, predict default rates, and assess the creditworthiness of borrowers more accurately. Machine learning and data mining propose that by applying algorithms and statistical techniques to big data, banks can uncover patterns, trends, and relationships that can inform decision-making processes, improve risk management, and enhance customer experience. Statistical methods and probability distributions are used to analyze and make sense of large datasets, enabling data scientists to draw meaningful conclusions from the data. Statistical and probability theory propose that by applying mathematical models and methods to large datasets, banks can gain insights into customer behavior, market trends, and financial risks. It is recommended that banks should establish a robust data governance framework and upgrade data infrastructure to harness the power of big data to drive innovation, improve decision-making, and enhance competitiveness in the digital era.

keywords:

Banking sector, data mining theory, information theory, Nigeria, statistical theory,

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References:

Ahamad, D., Akhtar, M. & Hameed, S. A. (2019). A review and analysis of big data and
mapreduce. International Journal of Advance Trends Computer Science Engineering,8(1),1-3.
Ahmad, J., Muhammad, K., Lloret, J., & Baik, S. W. (2018). Efficient conversion of deep features
to compact binary codes using fourier decomposition for multimedia big data. IEEE
Transactions on Industrial Informatics,14(7),3205-3215.
Akter, S., & Wamba, S. F. (2016). Big data analytics in E-commerce: A systematic review and
agenda for future research. Electronic Markets,26(2),173-194.
Alzakholi, O., Shukur, H., Zebari, R., Abas, S., & Sadeeq, M. (2020). Comparison among cloud
technologies and cloud performance. Journal of Applied Sciences, Technology &
Trends,1(2),40-47.
Boone, C. A., Skipper, J. B., & Hazen, B. T. (2017). A framework for investigating the role of big
data in service parts management. Journal of Cleaner Production,153, 687-691.
Côrte-Real, N., Ruivo, P., Oliveira, T., & Popovi?, A. (2019). Unlocking the drivers of big data
analytics value in firms. Journal of Business Research,97,160-173.
Mhlanga, D. (2020). Industry 4.0 in ?nance: The impact of arti?cial intelligence (AI) on digital
?nancial inclusion. International Journal of Financial Studies,8,45-55.
Mhlanga, D. (2021). Financial inclusion in emerging economies: The application of machine
learning and arti?cial intelligence in credit risk assessment. International Journal of
Financial Studies,9,39-47.
Mhlanga, D. (2022). The role of arti?cial intelligence and machine learning amid the COVID-19
pandemic: What lessons are we learning on 4IR and the sustainable development goals.
International Journal of Environmental Research and Public Health,19,1879-1888.
Oussous, A., Benjelloun, F. Z., Lahcen, A. A., & Belfkih,S. (2018). Big data technologies: A
survey. Journal of King Saud University-Computer and Information Sciences,30(4),431-
Richins, G., Stapleton, A., Stratopoulos, T. C., & Wong, C. (2017). Big data analytics: Opportunity
or threat for the accounting profession?. Journal of Information Systems,31(3), 63-79.
Seddon, J. J., & Currie, W. L. (2017). A model for unpacking big data analytics in high-frequency
trading. Journal of Business Research,70,300-307.
Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017). Critical analysis of Big Data
challenges and analytical methods. Journal of Business Research,70,263-286.
Sookhak, M., Gani, A., Khan, M. K., & Buyya, R. (2017). Dynamic remote data auditing for
securing big data storage in cloud computing. Information Sciences,380,101-116.
Sun, F., Huang, G. B., Wu, Q. J., Song, S., & Wunsch, D. C. II. (2017). Efficient and rapid machine
learning algorithms for big data and dynamic varying systems. IEEE Transactions on
Systems, Man, and Cybernetics Systems,47(10), 2625-2626.
Zhao, R., Liu, Y., Zhang, N., & Huang, T. (2017). An optimization model for green supply chain
management by using a big data analytic approach. Journal of Cleaner
Production,142,1085-1097.
Zhou, L., Pan, S., Wang, J., & Vasilakos, A. V. (2017). Machine learning on big data:
Opportunities and challenges. Neurocomputing,237,350-361.

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