Financial Inclusion through Scalable Cloud-Native Transaction Systems

Authors

  • Dasaradhi Eddula

Keywords:

Financial Inclusion, Cloud-Native Architecture, Mobile Payments, Digital Financial Services, Emerging Markets, Scalable Infrastructure, Event-Driven Systems, Microfinance Technology

Abstract

An estimated 1.4 billion adults worldwide remain unbanked as of 2024, concentrated in Sub-Saharan Africa, South Asia, and Southeast Asia, where geographic barriers, high transaction costs, and legacy infrastructure have historically excluded large populations from formal financial services. Cloud-native transaction systems, architected for elasticity, low-cost operation, and global reach, represent a technically viable and economically compelling pathway to extend financial services to these populations at scale. This article examines how microservices architectures, event-driven platforms, containerized deployment, and intelligent orchestration collectively enable fintech platforms to serve high-volume, low-value transactions at the unit economics required to reach underserved markets. Drawing on verified empirical evidence from recent literature and deployment experience, the article analyzes the architectural, infrastructure, and performance engineering properties that determine whether digital financial platforms can achieve the scale and cost efficiency that financial inclusion demands. It further examines the sustainability dimensions of cloud-native inclusion platforms, both environmental, in terms of energy efficiency, and social, in terms of equitable access, gender inclusion, and resilience during crises. The article concludes that cloud-native design principles are not merely technically superior to legacy alternatives for inclusion platforms but represent a structural alignment between the economic requirements of serving underserved populations and the engineering properties of well-designed distributed systems.

DOI: https://doi.org/10.17762/ijisae.v14i1s.8318

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References

World Bank, "The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19," World Bank Group, Washington, D.C., 2022. [Online]. Available: https://documents.worldbank.org/en/publication/documents-reports/documentdetail/099818107072234182

Safaricom, "M-Pesa Annual Report and Impact Data 2023," Safaricom PLC, Nairobi, Kenya, 2023. [Online]. Available: https://www.safaricom.co.ke/annualreport_2023/m-pesa.html

Y. Qiao et al., "EdgeOptimizer: A programmable containerized scheduler of time-critical tasks in Kubernetes-based edge-cloud clusters," Future Generation Computer Systems, vol. 156, pp. 221–230, 2024. [Online]. Available: https://doi.org/10.1016/j.future.2024.03.007

J. Jiang et al., "DRKC: Deep reinforcement learning enhanced microservice scheduling on Kubernetes clusters in cloud-edge environment," IEEE Transactions on Cloud Computing, vol. 13, no. 4, pp. 1472–1486, 2025. [Online]. Available: https://ieeexplore.ieee.org/document/11214429

L. Zhu et al., "Two-stage learning approach for semantic-aware task scheduling in container-based clouds," IEEE Transactions on Cloud Computing, vol. 13, no. 1, pp. 148–165, 2025. [Online]. Available: https://docs.google.com/document/d/1iCTHwAx32AR37fOqK_Bu8WMJNf4taAVZbp8eSyJdNRs/edit?tab=t.0

K. Staykova and J. Damsgaard, "A 2020 perspective on the race to dominate the mobile payments platform: Entry and expansion strategies," Electronic Commerce Research and Applications, vol. 41, p. 100954, 2020. [Online]. Available: https://www.sciencedirect.com/science/article/abs/pii/S1567422320300314?via%3Dihub

C. Shan et al., "KubeAdaptor: A docking framework for workflow containerization on Kubernetes," Future Generation Computer Systems, vol. 148, pp. 584–599, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/abs/pii/S0167739X2300242X?via%3Dihub

N. McDonnell et al., "Dynamic virtual machine consolidation using a multi-agent system to optimise energy efficiency in cloud computing," Future Generation Computer Systems, vol. 108, pp. 288–301, 2020. [Online]. Available: https://www.sciencedirect.com/science/article/abs/pii/S0167739X19314591?via%3Dihub

R. Gollapudi, "Autonomous Multi-Zone Replication for Zero-Loss Settlement Systems," International Journal of Computational and Experimental Science and Engineering, vol. 12, no. 1, pp. 423–438, 2026. [Online]. Available: https://ijcesen.com/index.php/ijcesen/article/view/4817/1767

J. M. Parra-Ullauri et al., "kubeFlower: A privacy-preserving framework for Kubernetes-based federated learning in cloud-edge environments," Future Generation Computer Systems, vol. 156, pp. 246–261, 2024. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0167739X24001134?via%3Dihub

Santa Maria Shithil and Muhammad Abdullah Adnan, "A prediction based replica selection strategy for reducing tail latency in geo-distributed systems," IEEE Transactions on Cloud Computing, vol. 11, no. 3, pp. 2954–2965, 2023. [Online]. Available: https://ieeexplore.ieee.org/document/10042477

Ahmad Taghinezhad-Niar and Javid Taheri, "Reliability, rental cost, and energy-aware multi-workflow scheduling on multi-cloud systems," IEEE Transactions on Cloud Computing, vol. 12, no. 1, pp. 312–328, 2022. [Online]. Available: https://ieeexplore.ieee.org/document/9956841

M. Szalay et al., "Real-time FaaS: Towards a latency bounded serverless cloud," IEEE Transactions on Cloud Computing, vol. 11, no. 2, pp. 1636–1650, 2022. [Online]. Available: https://ieeexplore.ieee.org/document/9714028

R. Gollapudi, "Telemetry-Driven Predictive Failure Models for High-Scale Financial Databases," Journal of Computational Analysis and Applications, vol. 34, no. 12, pp. 1035–1049, 2025. [Online]. Available: https://eudoxuspress.com/index.php/pub/article/view/4835

S. Cai and X. Li, "Explainable fraud detection of financial statement data driven by two-layer knowledge graph," Expert Systems with Applications, vol. 246, p. 123126, 2024. [Online]. Available: https://www.sciencedirect.com/science/article/abs/pii/S0957417423036308?via%3Dihub

T. Ergen and S. S. Kozat, "Unsupervised anomaly detection with LSTM neural networks," IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 8, pp. 3127–3141, 2020. [Online]. Available: https://ieeexplore.ieee.org/document/8836638

Cho-Hsun Lu, "The moderating role of e-lifestyle on disclosure intention in mobile banking: A privacy calculus perspective," Electronic Commerce Research and Applications, vol. 64, p. 101374, 2024. [Online]. Available: https://www.sciencedirect.com/science/article/abs/pii/S156742232400019X?via%3Dihub

A. Abusitta et al., "Survey on explainable AI: Techniques, challenges and open issues," Expert Systems with Applications, vol. 255, p. 124710, 2024. [Online]. Available: https://www.sciencedirect.com/science/article/abs/pii/S095741742401577X?via%3Dihub

Y. Chen et al., "Stochastic workload scheduling for uncoordinated datacenter clouds with multiple QoS constraints," IEEE Transactions on Cloud Computing, vol. 8, no. 4, pp. 1284–1295, 2020. [Online]. Available: https://ieeexplore.ieee.org/document/7501820

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Published

29.05.2026

How to Cite

Dasaradhi Eddula. (2026). Financial Inclusion through Scalable Cloud-Native Transaction Systems. International Journal of Intelligent Systems and Applications in Engineering, 14(1s), 1114–1112. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/8318

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Section

Research Article