Authors : Adeyinka Orelaja; Aboaba Veronica Oluwabusola

Volume/Issue : Volume 10 - 2025, Issue 5 - May

Google Scholar : https://tinyurl.com/mv7zv2tw

DOI : https://doi.org/10.38124/ijisrt/25may2071

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The complexity and velocity of financial market activities have heightened the risk of sophisticated fraudulent practices. Traditional rule-based surveillance systems often struggle to adapt to evolving threat patterns, resulting in delayed detection and increased financial and reputational risks. With the advent of artificial intelligence (AI) and machine learning (ML), financial institutions and regulators are now positioned to proactively identify anomalies and mitigate risks through predictive modeling approaches. This paper investigates the transformative role of AI and predictive modeling in modern fraud detection within financial markets. The research evaluates the effectiveness of supervised and unsupervised learning models for dynamic fraud detection and risk scoring. Furthermore, the paper proposes a predictive fraud detection framework designed to provide real-time risk assessments, enhance regulatory compliance, and enable faster investigative actions. Ultimately, this study advocates for the strategic adoption of AI technologies to fortify financial market integrity against current and future fraud threats.

Keywords : Artificial Intelligence, Compliance Monitoring, Anomaly Detection, Data Analytics, Financial Fraud.

References :

1. Aziz LA, Andriansyah Y. The role artificial intelligence in modern banking: an exploration of AI-driven approaches for enhanced fraud prevention, risk management, and regulatory compliance. Reviews of Contemporary Business Analytics. 2023 Aug;6(1):110-32.
2. Elumilade OO, Ogundeji IA, Ozoemenam G, Omokhoa HE, Omowole BM. Leveraging financial data analytics for business growth, fraud prevention, and risk mitigation in markets. Gulf Journal of Advanced Business Research. 2025;3(3).
3. Shen Q. AI-driven financial risk management systems: Enhancing predictive capabilities and operational efficiency. Applied and Computational Engineering. 2024 Jul 25; 69:134-9.
4. Boinapalli NR. AI-Driven Predictive Analytics for Risk Management in Financial Markets. Silicon Valley Tech Review. 2023;2(1):41-53.
5. Gangani CM. AI in Insurance: Enhancing Fraud Detection and Risk Assessment. International IT Journal of Research, ISSN: 3007-6706. 2024 Oct 20;2(4):226-36.
6. Alabi M, Ang AW. AI-Driven Financial Risk Management: Detecting Anomalies and Predicting Market Trends. Research Gate. 2024 Jul 6.
7. Chukwunweike JN, Chikwado CE, Ibrahim A, Adewale AA Integrating deep learning, MATLAB, and advanced CAD for predictive root cause analysis in PLC systems: A multi-tool approach to enhancing industrial automation and reliability. World Journal of Advance Research and Review GSC Online Press; 2024. p. 1778–90. Available from: https://dx.doi.org/10.30574/wjarr.2024.23.2.2631
8. Oko-Odion C. AI-Driven Risk Assessment Models for Financial Markets: Enhancing Predictive Accuracy and Fraud Detection.
9. Daiya H. AI-Driven Risk Management Strategies in Financial Technology. Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023. 2024 Jul 11;5(1):194-216.
10. Noah GU. Interdisciplinary strategies for integrating oral health in national immune and inflammatory disease control programs. Int J Comput Appl Technol Res. 2022;11(12):483-498. doi:10.7753/IJCATR1112.1016.
11. Shabir G, Khalid N. AI-Powered Fraud Detection and Risk Assessment: The Future of Financial Services [Internet].
12. Okeke CMG. Evaluating company performance: the role of EBITDA as a key financial metric. Int J Comput Appl Technol Res. 2020;9(12):336–349
13. Chukwunweike Joseph, Salaudeen Habeeb Dolapo. Advanced Computational Methods for Optimizing Mechanical Systems in Modern Engineering Management Practices. International Journal of Research Publication and Reviews. 2025 Mar;6(3):8533-8548. Available from: https://ijrpr.com/uploads/V6ISSUE3/IJRPR40901.pdf
14. Cook A. AI in Financial Services: Risk Management and Fraud Detection. AI Tech International Journal, ISSN: 3079-4749. 2023 Sep 20;1(1):1-7.
15. Anthony OC, Oluwagbade E, Bakare A, Animasahun B. Evaluating the economic and clinical impacts of pharmaceutical supply chain centralization through AI-driven predictive analytics: comparative lessons from large-scale centralized procurement systems and implications for drug pricing, availability, and cardiovascular health outcomes in the U.S. Int J Res Publ Rev. 2024 Oct;5(10):5148-5161. Available from: https://ijrpr.com/uploads/V5ISSUE10/IJRPR34458.pdf
16. Iseal S, Joseph O, Joseph S. AI in Financial Services: Using Big Data for Risk Assessment and Fraud Detection [Internet]. 2025
17. Ajani OL. Extraction and validation of database of urban and non-urban points from remote sensing data. International Journal of Computer Applications Technology and Research. 2018;7(12):449-472.
18. Sadiya H, Shah H. Predictive Analytics and AI Integration: Revolutionizing AML and Fraud Detection in Financial Services.
19. Ajani OL. Leveraging remotely sensed data for identifying underserved communities: A project-based approach. International Journal of Computer Applications Technology and Research. 2017;6(12):519-532. Available from: https://ijcat.com/volume6/issue12.
20. Balakrishnan A. Leveraging artificial intelligence for enhancing regulatory compliance in the financial sector. International Journal of Computer Trends and Technology. 2024 May 14.
21. Akmal U, Shah Q. Predictive Analytics and AI Integration in Fraud Detection and Risk Assessment for Financial Services.
22. Emi-Johnson Oluwabukola, Fasanya Oluwafunmibi, Adeniyi Ayodele. Predictive crop protection using machine learning: A scalable framework for U.S. Agriculture. Int J Sci Res Arch. 2024;15(01):670-688. Available from: https://doi.org/10.30574/ijsra.2024.12.2.1536
23. Nweze M, Avickson EK, Ekechukwu G. The Role of AI and Machine Learning in Fraud Detection: Enhancing Risk Management in Corporate Finance.
24. Ajani OL. Mapping the digital divide: Using GIS and satellite data to prioritize broadband expansion projects. World Journal of Advanced Research and Reviews. 2025;26(01):2159-2176. doi: https://doi.org/10.30574/wjarr.2025.26.1.1304.
25. Patil D. Artificial Intelligence in Financial Services: Advancements in Fraud Detection, Risk Management, And Algorithmic Trading Optimization. Risk Management, And Algorithmic Trading Optimization (November 20, 2024). 2024 Nov 20.
26. Olagunju E. Integrating AI-driven demand forecasting with cost-efficiency models in biopharmaceutical distribution systems. Int J Eng Technol Res Manag [Internet]. 2022 Jun 6(6):189. Available from: https://doi.org/10.5281/zenodo.15244666
27. Vallarino D. AI-Powered Fraud Detection in Financial Services: GNN, Compliance Challenges, and Risk Mitigation. Compliance Challenges, and Risk Mitigation (March 07, 2025). 2025 Mar 7.
28. Emi-Johnson Oluwabukola, Nkrumah Kwame, Folasole Adetayo, Amusa Tope Kolade. Optimizing machine learning for imbalanced classification: Applications in U.S. healthcare, finance, and security. Int J Eng Technol Res Manag. 2023 Nov;7(11):89. Available from: https://doi.org/10.5281/zenodo.15188490
29. Ejiofor OE. A comprehensive framework for strengthening USA financial cybersecurity: integrating machine learning and AI in fraud detection systems. European Journal of Computer Science and Information Technology. 2023;11(6):62-83.
30. Olagunju E. Integrating AI-driven demand forecasting with cost-efficiency models in biopharmaceutical distribution systems. Int J Eng Technol Res Manag [Internet]. 2022 Jun 6(6):189. Available from: https://doi.org/10.5281/zenodo.15244666
31. Balcıoğlu YS. Revolutionizing Risk Management AI and ML Innovations in Financial Stability and Fraud Detection. InNavigating the Future of Finance in the Age of AI 2024 (pp. 109-138). IGI Global.
32. Chukwunweike J, Lawal OA, Arogundade JB, Alade B. Navigating ethical challenges of explainable AI in autonomous systems. International Journal of Science and Research Archive. 2024;13(1):1807–19. doi:10.30574/ijsra.2024.13.1.1872. Available from: https://doi.org/10.30574/ijsra.2024.13.1.1872.
33. Ahmad AS. Application of big data and artificial intelligence in strengthening fraud analytics and cybersecurity resilience in global financial markets. International Journal of Advanced Cybersecurity Systems, Technologies, and Applications. 2023 Dec 7;7(12):11-23.
34. Reddy P, Muthyala S. Predictive Financial Modeling Using Ai: Enhancing Risk Management in The Banking Sector. International Journal of Computer Science Engineering. 2023;11.
35. Omopariola B, Aboaba V. Advancing financial stability: The role of AI-driven risk assessments in mitigating market uncertainty. Int J Sci Res Arch. 2021;3(2):254-70.
36. Islam T, Islam SM, Sarkar A, Obaidur A, Khan R, Paul R, Bari MS. Artificial Intelligence in Fraud Detection and Financial Risk Mitigation: Future Directions and Business Applications. International Journal for Multidisciplinary Research. 2024.
37. Vashishth TK, Sharma V, Kaushik V, Bagar VK, Sharma R, Kumar R. Importance of Using AI and ML in the Financial Sector for Risk Prevention and Management. InUtilizing AI and Machine Learning in Financial Analysis 2025 (pp. 509-530). IGI Global Scientific Publishing.
38. Dey R, Roy A, Akter J, Mishra A, Sarkar M. AI-driven machine learning for fraud detection and risk management in US healthcare billing and insurance. Journal of Computer Science and Technology Studies. 2025 Feb 12;7(1):188-98.
39. Ogunmokun AS, Balogun ED, Ogunsola KO. A Conceptual Framework for AI-Driven Financial Risk Management and Corporate Governance Optimization.
40. Javaid HA. Ai-driven predictive analytics in finance: Transforming risk assessment and decision-making. Advances in Computer Sciences. 2024 Jun 11;7(1).
41. Singh P. AI and Financial Risk Management Revolutionizing Risk Assessment and Mitigation. InArtificial Intelligence for Financial Risk Management and Analysis 2025 (pp. 373-404). IGI Global Scientific Publishing.
42. Paramasivan A. Enhancing customer trust in card payments: AI-based risk management models. IJLRP-International Journal of Leading Research Publication. 2024;5(10).