Artificial Intelligence for Fraud Detection and Portfolio Optimization: Insights from UK Financial Institutions
DOI:
https://doi.org/10.56294/ai2025437Keywords:
Artificial Intelligence, Fraud Detection, Portfolio Optimization, Financial Institutions, United KingdomAbstract
Introduction: Artificial intelligence has become an essential tool in modern financial services, particularly in the areas of fraud detection and investment management. Increasing financial crimes and the growing complexity of market environments have created a need for advanced technological solutions capable of supporting more accurate and timely decision-making in financial institutions.
Objective: This study examines the role of artificial intelligence in enhancing fraud detection and portfolio optimization within financial institutions in the United Kingdom.
Method: A quantitative, observational survey was conducted among one hundred and fifty banking professionals from five major financial institutions. The data collection instrument was developed by the researchers using information derived from twenty peer-reviewed academic sources and industry publications. Data were analysed using descriptive statistics to identify general patterns and tendencies in respondents’ perceptions.
Results: The results show that artificial intelligence contributes meaningfully to strengthening fraud prevention mechanisms and improving investment management processes, particularly through enhanced analytical capacity and improved risk interpretation.
Conclusion: The study further demonstrates that artificial intelligence supports more secure, efficient, and responsive financial operations, indicating its relevance for the future of financial services in the United Kingdom.
References
1. Smith, S. (2020). Blockchain, artificial intelligence and financial services. Springer International Publishing.
2. OECD. (2021). OECD Business and Finance Outlook 2021: AI in Business and Finance. OECD. https://doi.org/10.1787/ba682899-en DOI: https://doi.org/10.1787/ba682899-en
3. Phua, C., Lee, V. C., Smith, K., & Gayler, R. W. (2010). A comprehensive survey of data mining-based fraud detection research. Data Mining and Knowledge Discovery, 30(3), 557-582.
4. Jones, R., & Brown, L. (2019). AI and Machine Learning in Finance. Journal of Financial Technology, 12(2), 87-104.
5. Aziz, S., Dowling, M., Hammami, H., & Piepenbrink, A. (2022). Machine learning in finance: A topic modeling approach. European Financial Management, 28(3), 744–770. https://doi.org/10.1111/eufm.12326 DOI: https://doi.org/10.1111/eufm.12326
6. Olatunbosun, I. E., & Olatunbosun, A. R. (2025). Artificial Intelligence and Risk Management in Financial Institutions: Evidence from the UK Banking Sector. EthAIca, 4, 436. https://doi.org/10.56294/ai2025436 DOI: https://doi.org/10.56294/ai2025436
7. Cirqueira, D., Nedbal, D., Helfert, M., & Bezbradica, M. (2020). Scenario-based requirements elicitation for user-centric explainable ai., 321-341. https://doi.org/10.1007/978-3-030-57321-8_18 DOI: https://doi.org/10.1007/978-3-030-57321-8_18
8. Chen, S., & Wang, Q. (2021). The Impact of AI on Decision-Making in Financial Institutions. International Journal of Finance and Economics, 42(4), 556-573.
9. Dwadasi, A. (2024). Artificial intelligence and machine learning in financial services: risk management and fraud detection. J. Electrical Systems, 20(6s), 1418-1424. DOI: https://doi.org/10.52783/jes.2929
10. Dwivedi, Y., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., & Williams, M. (2021). Artificial intelligence (ai): multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002 DOI: https://doi.org/10.1016/j.ijinfomgt.2019.08.002
11. Cruz, M. G. (2019). Artificial Intelligence in Financial Risk Management. Journal of Financial Technology, 22(4), 67-78.
12. Johnson, M. (2018). The United Kingdom as a Global Financial Hub. Financial Review, 33(1), 15-28.
13. La Torre, M., Mango, F., Cafaro, A., & Leo, S. (2020). Does the ESG index affect stock return? Evidence from the Eurostoxx50. Sustainability, 12(16), 6387. DOI: https://doi.org/10.3390/su12166387
14. Brown, J., & White, S. (2020). Unraveling the Consequences of AI in Financial Management. Finance Research Quarterly, 18(1), 23-38.
15. Nastoska, A., Jancheska, B., Rizinski, M., & Trajanov, D. (2025). Evaluating Trustworthiness in AI: Risks, Metrics, and Applications Across Industries. Electronics, 14(13), 2717. DOI: https://doi.org/10.3390/electronics14132717
16. Dillman, D. A., Smyth, J. D., & Christian, L. M. (2014). Internet, Phone, Mail, and Mixed-Mode Surveys: The Tailored Design Method. John Wiley & Sons. DOI: https://doi.org/10.1002/9781394260645
17. Saunders, M., Lewis, P., & Thornhill, A. (2019). Research Methods for Business Students. Pearson.
18. Pallant, J. (2021). SPSS Survival Manual. McGraw Hill. DOI: https://doi.org/10.4324/9781003117452
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Iyanu Emmanuel Olatunbosun, Abosede Rebecca Olatunbosun (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.
