Authors : Gunjan Kumar

Volume/Issue : Volume 10 - 2025, Issue 4 - April

Google Scholar : https://tinyurl.com/my7ezyus

Scribd : https://tinyurl.com/3nndez6j

DOI : https://doi.org/10.38124/ijisrt/25apr2359

Google Scholar

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

Note : Google Scholar may take 15 to 20 days to display the article.

Abstract : The rise of Fintech brought about new dimensions of scaling, tweaks to the regulatory landscape, as well as the advent of AI and machine learning in financial technology. Alongside the growth in technology and the human obsession with fast operating speeds, the building of large-scale platforms that can undertake a range of regulatory compliance checks and high transactions per second is high stake. This paper takes a closer look at the design principles and infrastructure backend strategies necessary for the development of a platform for Fintech capable of these huge throughputs as well as maneuverability at the hands of regulatory complexity and dynamically changing markets. It will go from the analysis of the microservices useful for AI and ML applications in detecting fraud through risk modeling to engaging customers; data engineering pipelines and cloud-native propositions indirectly used for AI architecture would then be studied. Here, the paper comes under an abstracted cloud, black of concrete architectures that some authors are still shy of mentioning.

Keywords : Fackend Fintech-AI in Finance-ML Architecture and ML Architecture- ML Machine Learning for Scalability Resilience-Microservices-Cloud-Native Platforms- Fraud Detection-Directed Engineering-Predictive Analytics.

References :

1. Fathima, S. A. (2025). AI-Driven Insights for Risk Management in Banking: Leveraging Cloud-Native Technologies for Scalability. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 1(01), 34-44.
2. Thompson, A. (2022). AI-Driven Insights for Risk Management in Banking: Leveraging Cloud-Native Technologies for Scalability. International Journal of AI, BigData, Computational and Management Studies, 3(4), 1-10.
3. Ionescu, S. A., Diaconita, V., & Radu, A. O. (2025). Engineering Sustainable Data Architectures for Modern Financial Institutions. Electronics, 14(8), 1650.
4. George, A. S. (2023). Securing the future of finance: how AI, Blockchain, and machine learning safeguard emerging Neobank technology against evolving cyber threats. Partners Universal Innovative Research Publication, 1(1), 54-66.
5. George, A. S. (2024). Finance 4.0: The Transformation of Financial Services in the Digital Age. Partners Universal Innovative Research Publication, 2(3), 104-125.
6. Vadisetty, R. (2024, November). Efficient large-scale data based on cloud framework using critical influences on financial landscape. In 2024 International Conference on Intelligent Computing and Emerging Communication Technologies (ICEC) (pp. 1-6). IEEE.
7. Dunsin, D. ZERO-DOWNTIME FINTECH PLATFORMS USING SELF-REPAIRING MICROSERVICES ARCHITECTURE.
8. Yadav, O. P., Teotia, R., & Baliyan, R. (2025). Fintech and Data Science: Shaping the Future of the Digital Economy. In Synergy of AI and Fintech in the Digital Gig Economy (pp. 332-349). CRC Press.
9. Shivashankar, K., Hajj, G. S. A., & Martini, A. (2025). Scalability and Maintainability Challenges and Solutions in Machine Learning: Systematic Literature Review. arXiv preprint arXiv:2504.11079.
10. Yadav, O. P., Teotia, R., & Baliyan, R. (2025). Fintech and Data Science: Shaping the Future of the Digital Economy. In Synergy of AI and Fintech in the Digital Gig Economy (pp. 332-349). CRC Press.
11. Oladinni, A., & Adewale, G. T. INNOVATIVE API FRAMEWORKS AND DATA-DRIVEN MODELLING FOR ENHANCED FINTECH LENDING APPLICATIONS.
12. Xu, J. (2022). The technical foundations of FinTech: ABCDI and more. The Future and FinTech, ABCDI and beyond.
13. Dodda, A., Lakkarasu, P., Singireddy, J., Challa, K., & Pamisetty, V. (2022). Optimizing Digital Finance and Regulatory Systems Through Intelligent Automation, Secure Data Architectures, and Advanced Analytical Technologies.
14. Singireddy, S., Adusupalli, B., Pamisetty, A., Mashetty, S., & Kaulwar, P. K. (2024). Redefining Financial Risk Strategies: The Integration of Smart Automation, Secure Access Systems, and Predictive Intelligence in Insurance, Lending, and Asset Management. Journal of Artificial Intelligence and Big Data Disciplines, 1(1), 109-124.
15. Azzutti, A. (2024). Artificial Intelligence and Machine Learning in Finance: Key Concepts, Applications, and Regulatory Considerations. In The Emerald Handbook of Fintech: Reshaping Finance (pp. 315-339). Emerald Publishing Limited.
16. Dunsin, D. SELF-HEALING FINANCIAL PLATFORMS USING MACHINE LEARNING AND PREDICTIVE MONITORING.
17. Oko-Odion, C., & Angela, O. (2025). Risk management frameworks for financial institutions in a rapidly changing economic landscape. Int J Sci Res Arch, 14(1), 1182-1204.
18. Subramanyam, S. V. (2021). Cloud computing and business process re-engineering in financial systems: The future of digital transformation. International Journal of Information Technology and Management Information Systems (IJITMIS), 12(1), 126-143.
19. Raju, N., Quazi, F., & Kondle, P. (2024). AI and Machine Learning in Digital Modernization Transforming Industries for the Future. Available at SSRN 5053870.
20. Allioui, H., & Mourdi, Y. (2023). Exploring the full potentials of IoT for better financial growth and stability: A comprehensive survey. Sensors, 23(19), 8015.
21. Zaichkowsky, T. M. (2020). Systems architecture perspective on digital transformation for financial services (Doctoral dissertation, Massachusetts Institute of Technology).
22. Narsina, D. (2020). The Integration of Cybersecurity, IoT, and Fintech: Establishing a Secure Future for Digital Banking. NEXG AI Review of America, 1(1), 119-134.
23. Abdullah, A., Omolola, H., Taiwo, S., & Aderibigbe, O. ADVANCED AI SOLUTIONS FOR SECURITIES TRADING: BUILDING SCALABLE AND OPTIMIZED SYSTEMS FOR GLOBAL FINANCIAL MARKETS.
24. Poon, P. L., Wibowo, S., & Tang, S. F. (2024). A FinTech Clustering Framework: Technology, Model, and Stakeholder Perspectives. arXiv preprint arXiv:2412.05285.
25. Sah, J., Padma, S., Yanamandra, R., & Irfan, M. (2024). Risk Management of Future of Defi Using Artificial Intelligence as a Tool. In AI-Driven Decentralized Finance and the Future of Finance (pp. 252-272). IGI Global.
26. Vučinić, M., & Luburić, R. (2024). Artificial intelligence, fintech and challenges to central banks. Journal of Central Banking Theory and Practice, 13(3), 5-42.
27. Famoti, O., Shittu, R. A., Omowole, B. M., Akokodaripon, D., Ezechi, O. N., Ewim, C. P. M., ... & Abutu, D. E. Agile Software Engineering Framework for Real-Time Personalization in Financial Applications.
28. Jayasundara, H., & Wickramarachchi, A. P. R. (2024). Critical factors affecting digital resilience in the Sri Lankan banking sector: a systematic review. Journal of Science of the University of Kelaniya, 17(2).
29. Irfan, M., Muhammad, K., Naifar, N., & Khan, M. A. (Eds.). (2024). Applications of Block Chain technology and Artificial Intelligence: Lead-ins in Banking, Finance, and Capital Market. Springer Nature.
30. Chauhan, R. P. S., Sonker, S. K., Kaur, M., Sharma, C., Singh, R., & Singh, R. (2024, March). Optimizing IoT Threat Mitigation with Artificial Intelligence in Banking: A Multi-Objective Approach. In 2024 2nd International Conference on Disruptive Technologies (ICDT) (pp. 296-301). IEEE.