The Role of Artificial Intelligence in Cybersecurity for Financial Fraud Detection and Prevention

Gbenga Akinyemi, Muyideen Omuya Momoh, Hayatu Idris Bulama, Mohammed Habib Abdullahi

Abstract


Financial fraud, particularly computer-environment-reliant fraud has been an ongoing global concern. Sources, both anecdotal and research, have consistently inundated the print media with concerning reports of cyber fraud various kinds running into billions of dollars yearly. From the forgoing, it is evident that traditional systems of fraud mitigation are fast losing its grips on the menace fraudulent activities. Traditional fraud mitigation parameters are rule based. They are targeted against meeting certain rule challenge. More so, criminals appear to keep changing their pattern of crimes to fool the system and stay undetected.  Rule-based traditional approaches are not effective against the current state of dynamism and persistence in fraudulent financial cybercrimes. AI and machine learning approaches have proven to be more resilient in tackling the today’s threat of cybercrimes as they evolve. The reason being that AI is equally an evolving mechanism which collects data from both historic and learning activities, while building a continuous model capable of acting proactively to arrest threats before they occur.  Hence, this is poised to demonstrate the importance of AI in fraud mitigation, given the current patterns of crimes marked by sophistication and persistence. This study analyzes how artificial intelligence contributes to strengthening cybersecurity, with a focus on detecting and preventing financial fraud. It reviews different AI techniques—including both supervised and unsupervised machine learning approaches—that examine transaction behaviors and identify anomalies indicative of fraudulent activity. The research underscores AI's capacity for real-time detection and automated response, which enhances the ability of financial institutions to address increasingly complex cyber threats. Incorporating AI into cybersecurity strategies offers a promising avenue for creating more secure financial environments and fostering greater customer confidence in digital banking.


Full Text:

PDF

References


Abiddin, N. Z., & Akinyemi, G. (2024). The Intersection Between E-Tax Administration, Sustainable Public Procurement and Sustainable Public Performance: A Review Conceptual Framework. Journal of Lifestyle and SDGs Review, 4(2), e02328-e02328.

Akinyemi, G. (2025). Unlocking Financial Security with Blockchain: Opportunities and Challenges. ATBU Journal of Science, Technology and Education, 13(2), 1-9.

Adeyeye, O. J., & Akanbi, I. (2024). Artificial intelligence for systems engineering complexity: a review on the use of AI and machine learning algorithms. Computer Science & IT Research Journal, 5(4), 787-808.

Alshantti, A. A. S. (2024). On the Applications of Machine Learning for Alleviating Challenges in the Financial Crime Domain.

Aslan, Ö., Aktuğ, S. S., Ozkan-Okay, M., Yilmaz, A. A., & Akin, E. (2023). A comprehensive review of cyber security vulnerabilities, threats, attacks, and solutions. Electronics, 12(6), 1333.

Ayamga, D. (2018). Telecommunication fraud prevention policies and implementation challenge.

Baduge, S. K., Thilakarathna, S., Perera, J. S., Arashpour, M., Sharafi, P., Teodosio, B., ... & Mendis, P. (2022). Artificial intelligence and smart vision for building and construction 4.0: Machine and deep learning methods and applications. Automation in Construction, 141, 104440.

Chamkar, A. S., Maleh, Y., & Gherabi, N. (2024). Security operation center. The Art of Cyber Defense: From Risk Assessment to Threat Intelligence, 271.

Chinedu, P. U., Nwankwo, W., Aliu, D., Shaba, S. M., & Momoh, M. O. (2020). Cloud security concerns: assessing the fears of service adoption. Archive of Science and Technology, 1(2), 164-174.

Daniel, A., Shaba, S. M., Momoh, M. O., Chinedu, P. U., & Nwankwo, W. (2021). A computer security system for cloud computing based on encryption technique. Computer Engineering and Applications, 10(1), 41-53.

Dara, J., & Gundemoni, L. (2006). Credit Card Security and E-Payment: Enquiry into credit card fraud in E-Payment.

Franjić, S. (2020). Cybercrime is very dangerous form of criminal behavior and cybersecurity. Emerging Science Journal, 4(18), 18-26.

Gams, M., Gu, I. Y. H., Härmä, A., Muñoz, A., & Tam, V. (2019). Artificial intelligence and ambient intelligence. Journal of Ambient Intelligence and Smart Environments, 11(1), 71-86.

Haldorai, A. (2023). A review on artificial intelligence in internet of things and cyber physical systems. Journal of Computing and Natural Science, 3(1), 012-023.

Hashim, H. A., Salleh, Z., Shuhaimi, I., & Ismail, N. A. N. (2020). The risk of financial fraud: a management perspective. Journal of Financial Crime, 27(4), 1143-1159.

Nwankwo, W., Chinedu, P. U., Daniel, A., Shaba, S. M., Muyideen, M. O., Nwankwo, C. P., ... & Uwadia, F. (2022, November). Educational FinTech: Promoting Stakeholder Confidence Through Automatic Incidence Resolution. In The International Symposium on Computer Science, Digital Economy and Intelligent Systems (pp. 947-963). Cham: Springer Nature Switzerland.

Patil, P. (2016). Artificial intelligence in cybersecurity. International journal of research in computer applications and robotics, 4(5), 1-5.

Samayamantri, L. S., Singhal, S., Krishnamurthy, O., & Regin, R. (2024). AI-Driven Multimodal Approaches to Human Behavior Analysis. In Advancing Intelligent Networks Through Distributed Optimization (pp. 485-506). IGI Global.

Sheta, S. V. (2021). Artificial Intelligence Applications in Behavioral Analysis for Advancing User Experience Design. Available at SSRN 5034079.

Sonal, S. (2022). Insurance Fraud Prevention Laws, a Need of Time: A Critical Analysis. Indian JL & Legal Rsch., 4, 1.

Zhang, C., & Lu, Y. (2021). Study on artificial intelligence: The state of the art and future prospects. Journal of Industrial Information Integration, 23, 100224.


Refbacks

  • There are currently no refbacks.