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ISSN Approved Journal || eISSN: 2582-8185 || CODEN: IJSRO2 || Impact Factor 8.2 || Google Scholar and CrossRef Indexed

Peer Reviewed and Referred Journal || Free Certificate of Publication

Research and review articles are invited for publication in March 2026 (Volume 18, Issue 3) Submit manuscript

Developing Secure, AI-Enabled Multi-Cloud Payment Gateways with Built-In Regulatory Compliance Automation

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  • Developing Secure, AI-Enabled Multi-Cloud Payment Gateways with Built-In Regulatory Compliance Automation

Kolawole Oloke *

Head VAS, Interswitch Group, Nigeria.

Research Article
 
International Journal of Science and Research Archive, 2021, 04(01), 502-516.
Article DOI: 10.30574/ijsra.2021.4.1.0214
DOI url: https://doi.org/10.30574/ijsra.2021.4.1.0214

Received on 13 November 2021; revised on 22 December 2021; accepted on 28 December 2021

The accelerating digitization of global financial transactions has intensified the demand for payment infrastructures that are secure, resilient, and capable of adapting to fast-evolving regulatory and fraud landscapes. Traditional payment gateways largely centralized and rule-based struggle to meet the operational complexity created by rising transaction volumes, diverse payment channels, cross-border settlement requirements, and increasingly sophisticated cyber threats. At a broader level, the emergence of multi-cloud architectures and AI-driven automation offers a transformative opportunity to redesign payment gateways for scalability, reliability, and regulatory alignment. Multi-cloud environments enhance availability and geographic redundancy, while distributed security frameworks and encrypted routing fabrics reduce single points of failure and mitigate systemic vulnerabilities. Narrowing the focus, this paper proposes a comprehensive framework for building secure, AI-enabled multi-cloud payment gateways that integrate real-time fraud detection, adaptive authentication, and automated regulatory compliance across jurisdictions. The architecture leverages microservice-based orchestration, event-driven payment pipelines, zero-trust security models, and cloud-native machine learning engines to analyze transaction behavior, detect anomalies, and enforce compliance checks within milliseconds. AI-enhanced compliance automation including document intelligence, sanctions screening, entity resolution, and AML pattern recognition enables dynamic risk scoring and reduces dependence on manual review processes prone to delay and inconsistency. Additionally, the framework integrates programmable policy engines capable of mapping payment workflows to regulatory constraints such as PCI-DSS, PSD2, GDPR, OCC, and emerging data-localization laws. Immutable audit trails, explainable AI components, and continuous monitoring further strengthen accountability and governance. Together, these components form an end-to-end blueprint for the next generation of intelligent payment gateways that are globally interoperable, predictively secure, regulator-ready, and optimized for high-velocity digital commerce.

Multi-cloud payment gateways; AI-driven compliance; Real-time fraud detection; Zero-trust security; Payment orchestration; Regulatory automation

https://ijsra.net/sites/default/files/fulltext_pdf/IJSRA-2021-0214.pdf

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Kolawole Oloke. Developing Secure, AI-Enabled Multi-Cloud Payment Gateways with Built-In Regulatory Compliance Automation. International Journal of Science and Research Archive, 2021, 04(01), 502-516. Article DOI: https://doi.org/10.30574/ijsra.2021.4.1.0214

Copyright © Author(s). All rights reserved. This article is published under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, sharing, adaptation, distribution, and reproduction in any medium or format, as long as appropriate credit is given to the original author(s) and source, a link to the license is provided, and any changes made are indicated.


All statements, opinions, and data contained in this publication are solely those of the individual author(s) and contributor(s). The journal, editors, reviewers, and publisher disclaim any responsibility or liability for the content, including accuracy, completeness, or any consequences arising from its use.

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