Abstract
The integration of Business Analytics tools into banking operations has emerged as a pivotal
strategy for financial institutions seeking to enhance efficiency, customer service, and overall
performance. This study delves into the application of business analytics tools in improving
banking operations within the dynamic financial landscape of London. As the banking sector
increasingly relies on data-driven insights, this research investigates how these analytics
technologies influence customer service, staff motivation, and cost management. Through an
exploration of London's financial institutions, the study provides valuable insights into how
business analytics influences operational processes, customer interactions, and strategic
decision-making. Secondary data collection was used as the technique of data collection for
this investigation and using data provided through review of financial institution reports and
other studies. The chosen data analysis method for this study was a thematic analysis and
spearman’s rank order correlation coefficient. The study revealed that: employing Business
Analytics tools have significantly increased the banking operations in London through
customer service delivery, protection of the customer's investments, staff productivity and
motivation. It was recommended that banks should invest in Advanced analytics technology to
improve customer service delivery, Quality assurance & data governance to protect client
investments and data, Skill development and continuous staff training to improve productivity,
Customer-centric analytics to improve customer service delivery, Collaboration and
knowledge sharing to reduce bank spending.
References
Ajah, I.A. & Nweke, H.F (2019). Big data and business analytics: Trends, platforms, success
factors and applications. Big Data and Cognitive Computing, 3(2), p.32. https://www.mdpi.com/2504-2289/3/2/32
Ali, B.J. & Anwar, G. (2021). An Empirical Study of Employees' Motivation and Its Influence on Job Satisfaction. [online] papers.ssrn.com.
Available at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3822723.
Cedersund, M. (2023). Artificial Intelligence in banking: the future of the banking work
environment. [online] www.theseus.fi. Available at:
https://www.theseus.fi/handle/10024/803304 [Accessed 17 Jul. 2023].
Davenport, T.H.(2018). From analytics to artificial intelligence. Journal of Business
Analytics, 1(2), pp.73-
https://www.tandfonline.com/doi/abs/10.1080/2573234X.2018.1543535
Mardiana, S. (2020). Modifying Research Onion for Information Systems Research. Solid
State Technology, 63(4), pp.5304-5313
Masri, N.E. & Suliman, A. (2019). Talent Management, Employee Recognition and
Performance in the Research Institutions. Studies in Business and Economics, 14(1),
pp.127–140. doi https://doi.org/10.2478/sbe-2019-0010.
Melnikovas, A. (2018). Towards an Explicit Research Methodology: Adapting Research Onion
Model for Futures Studies. Journal of Futures Studies, 23(2).
Mhlanga, D. (2020). Industry 4.0 in Finance: The Impact of Artificial Intelligence (AI) on
Digital Financial Inclusion. International Journal of Financial Studies, [online] 8(3),
p.45. doi https://doi.org/10.3390/ijfs8030045.
Nateghi, R. & Aven, T. (2021). Risk Analysis in the Age of Big Data: The Promises and
Pitfalls. Risk Analysis. doi https://doi.org/10.1111/risa.13682.
Pantea, K. (2022). Handbook of Research on Consumer Behavior Change and Business
Analytics in the Socio-Digital Era. [online] Google Books. IGI Global.
Perkins, N.B. (2023). Spreading a Digital Disease: The Circuit Split on Data Breaches and Its
Effects on the Health Sector. Indiana Health Law Review, [online] 20(2), pp.435–459.
doi https://doi.org/10.18060/27442.
Rahman, Md. M. (2023). The Effect of Business Intelligence on Bank Operational Efficiency
and Perceptions of Profitability. FinTech, 2(1), pp.99–119. doi
https://doi.org/10.3390/fintech2010008.
Scott, L. (2014). Figure 2: The research onion (Saunders et al., 2012). [online] ResearchGate.
Available at: https://www.researchgate.net/figure/The-research-onion-Saunders-et-al-
2012_fig2_282912642.
Shirazi, F. & Mohammadi, M. (2018). A big business analytics model for customer churn
prediction in the retiree segment. International Journal of Information Management.
doi https://doi.org/10.1016/j.ijinfomgt.2018.10.005.
The Bank Of London. (n.d.). The Bank of London: Home. [online] Available at:
https://thebankoflondon.com/.
Wandera, R. (2022). Customer Acceptance Analysis of Islamic Bank of Indonesia Mobile
Banking Using Technology Acceptance Model (TAM). IJIIS: International Journal of
Informatics and Information Systems, 5(2), pp.92–100. doi https://doi.org/10.47738/ijiis.v5i2.132.
