Application of Business Analytics Tools in Improving Banking Operations in London

Sholokwu; Boniface Monday

doi:10.56201/ijbfr.v10.no7.2024.pg31.47

📄 Download Paper

Application of Business Analytics Tools in Improving Banking Operations in London

Sholokwu, Boniface Monday

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.

Keywords

Business AnalyticsCost Management Customer ServiceFinancial Institutions

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.

Application of Business Analytics Tools in Improving Banking Operations in London

Abstract

Keywords

References

More Articles from IIARD INTERNATIONAL JOURNAL OF BANKING AND FINANCE RESEARCH