Blockchain and AI in Fintech: A Dual Approach to Fraud Mitigation
DOI:
https://doi.org/10.57159/jcmm.4.2.25193Keywords:
Fintech Systems, Blockchain Technology, Artificial Intelligence, Fraud MitigationAbstract
This study reviews fraud mitigation in the fintech sector due to the dual application of blockchain and artificial intelligence. Furthermore, the immutability ledger that supports transparency and accountability on the blockchain, and they successfully contain the risk of data and unauthorized financial transactions to some extent. In correlating millions of transactional data items in real-time, applying machine learning algorithms, it detects fraud and predicts it with high reliability. The proposed system describes an AI-based fraud detection system, namely the data acquisition and preparation phase, the training of AI models phase, and the monitoring phase. The advantages of AI-based fraud detection include high accuracy rates, instant monitoring in which a subsequent reaction can be initiated, minimal or no false positives, and cost efficiencies. This survey evaluates and examines current FinTech fraud detection methods, emphasizing the synergistic application of artificial intelligence and blockchain technologies. This study emphasizes implementation based on cost issues, its suitability for expansion and its legal concerns. It is applied to change many businesses, increase efficiency, decrease fraud, and bring innovation to the field of financial technologies.
References
P. Kamuangu, “Advancements of AI and machine learning in fintech industry (2016-2020),” Journal of Economics, Finance and Accounting Studies, vol. 6, pp. 23–31, Jan 2024.
C. Gitobu and N. J. Ogetonto, “Harnessing artificial intelligence (AI) and blockchain technology for the advancement of finance technology (fintech) in businesses,” in Proceedings of London International Conferences, no. 11, pp. 196– 210, Nov 2024.
S. R. Addula, K. Meduri, G. S. Nadella, and H. Gonaygunta, “AI and blockchain in finance: Opportunities and challenges for the banking sector,” IJARCCE, vol. 13, Feb 2024.
M. V. Jhansi, “Mediating effect of artificial intelligence and blockchain technology in finance: Opportunities and challenges,” Decision Making: Applications in Management and Engineering, vol. 8, Jan 2024.
N. O. Angela, N. I. Atoyebi, N. A. Soyele, and N. E. Ogunwobi, “Enhancing fraud detection and prevention in fintech: Big data and machine learning approaches,” World Journal of Advanced Research and Reviews, vol. 24, no. 2, pp. 2301–2319, 2024.
N. P. O. Shoetan and N. B. T. Familoni, “Transforming fintech fraud detection with advanced artificial intelligence algorithms,” Finance & Accounting Research Journal, vol. 6, no. 4, pp. 602–625, 2024.
Q. Hanjie, Z. Liu, B. Huang, Y. Zhuang, H. Tang, and E. Liu, “Blockchain for finance: A survey,” IET Blockchain, 2024.
Q. Yao, “Supervision of blockchain-based new FMIS,” in Blockchain-based New Financial Infrastructures: Theory, Practice and Regulation, pp. 171–181, Springer, 2022.
B. E. Abikoye, W. Adelusi, S. C. Umeorah, A. O. Adelaja, and C. Agorbia-Atta, “Integrating risk management in fintech and traditional financial institutions through AI and machine learning,” Journal of Economics Management and Trade, vol. 30, no. 8, pp. 90–102, 2024.
A. Kumari and N. C. Devi, “The impact of fintech and blockchain technologies on banking and financial services,”
Technology Innovation Management Review, vol.. 12, no. 1/2, 2022.
M. Paramesha, N. Rane, and J. Rane, “Artificial intelligence, machine learning, deep learning, and blockchain in financial and banking services: A comprehensive review,” Journal, vol. 1, no. 2, pp. 51–67, 2024.
N. O. Odeyemi, N. C. C. Okoye, N. O. C. Ofodile, N. O. B. Adeoye, N. W. A. Addy, and N. A. O. Ajayi-Nifise, “Integrating AI with blockchain for enhanced financial services security,” Finance & Accounting Research Journal, vol. 6, no. 3, pp. 271–287, 2024.
A. R. Kunduru, “Artificial intelligence advantages in cloud fintech application security,” Central Asian Journal of Mathematical Theory and Computer Sciences, vol. 4, no. 8, pp. 48–53, 2023.
N. O. A. Adigun et al., “Enhancing carbon markets with fintech innovations: The role of artificial intelligence and blockchain,” World Journal of Advanced Research and Reviews, vol. 23, no. 2, pp. 579–586, 2024.
P. Roszkowska, “Fintech in financial reporting and audit for fraud prevention and safeguarding equity investments,”
Journal of Accounting & Organizational Change, vol. 17, no. 2, pp. 164–196, 2020.
O. Mandych, T. Staverska, and O. Maliy, “Integration of artificial intelligence into the blockchain and cryptocurrency market,” Modeling the Development of the Economic Systems, no. 4, pp. 61–66, 2023.
G. Lăzăroiu, M. Bogdan, M. Geamănu, L. Hurloiu, L. Luminit, and R. S. Tefănescu, “Artificial intelligence algorithms and cloud computing technologies in blockchain-based fintech management,” Oeconomia Copernicana, vol. 14, no. 3, pp. 707–730, 2023.
N. O. A. Bello and N. K. Olufemi, “Artificial intelligence in fraud prevention: Exploring techniques and applications challenges and opportunities,” Computer Science & IT Research Journal, vol. 5, no. 6, pp. 1505–1520, 2024.
N. T. O. Sanyaolu, N. A. G. Adeleke, N. C. F. Azubuko, and N. O. S. Osundare, “Exploring fintech innovations and their potential to transform the future of financial services and banking,” International Journal of Scholarly Research in Science and Technology, vol. 5, pp. 054–072, Sep 2024.
F. T. Johora, R. Hasan, S. F. Farabi, J. Akter, and M. A. A. Mahmud, “Ai-powered fraud detection in banking: Safeguarding financial transactions,” The American Journal of Management and Economics Innovations, vol. 6, no. 6, pp. 8–22, 2024.
L. Hernandez Aros, L. Bustamante Molano, and F. Gutierrez-Portela, “Financial fraud detection through the application of machine learning techniques: a literature review,” Commun, vol. 11, p. 1130, 2024.
A. Cherif, A. Badhib, H. Ammar, and S. Alshehri, “Credit card fraud detection in the era of disruptive technologies: A systematic review,” Journal of King Saud University - Computer and Information Sciences, vol. 35, no. 1,
pp. 145–174, 2023.
A. Hanae, E. M. Saida, and G. Youssef, “Synergy of machine learning and blockchain strategies for transactional fraud detection in fintech systems,” in 11th International Conference on Future Internet of Things and Cloud,
pp. 292–297, 2024.
N. Rane, S. Choudhary, and J. Rane, “Blockchain and artificial intelligence (ai) integration for revolutionizing security and transparency in finance,” SSRN Electronic Journal, 2023.
N. O. A. Farayola, “Revolutionizing banking security: Integrating artificial intelligence, blockchain, and business intelligence for enhanced cybersecurity,” Finance & Accounting Research Journal, vol. 6, no. 4, pp. 501–514, 2024.
T. Renduchintala, H. Alfauri, Z. Yang, R. Pietro, and R. Jain, “A survey of blockchain applications in the fintech sector,” J. Open Innov. Technol. Mark. Complex, vol. 8, p. 185, 2022.
V. D. P. Sambrow and K. Iqbal, “Integrating artificial intelligence in banking fraud prevention: A focus on deep learning and data analytics,” Eigenpub Review of Science and Technology, vol. 6, no. 1, pp. 17–33, 2022.

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Accepted 2025-04-29
Published 2025-05-06