The Efficacy of Low-Code Orchestration in Enterprise Payment Systems: A Comparative Study on Operational Scalability, Security, and Time-to-Market

Authors

  • Kandasamy Sellappan Independent Researcher, USA

Keywords:

Low-Code Orchestration; Enterprise Payment Systems; Operational Scalability; Time-To-Market; Payment Modernization; Regulatory Compliance

Abstract

Background: Enterprise payment systems operate as mission-critical financial infrastructure, demanding real-time processing, regulatory compliance, and continuous availability. Legacy custom code development models are no longer capable of supporting the pace of digital innovation in financial services․ Aim: To evaluate LCO for enterprise payment systems with respect to operational scalability, security and compliance, and time to market. Methods: A comparative analytical framework was constructed drawing on peer-reviewed literature, architectural pattern analysis, and anonymized case evidence derived from large-scale payment modernization programs. Three representative enterprise deployment scenarios were examined against equivalent traditional engineering approaches. Results: We found that LCO platforms increase throughput elasticity‚ reduce the time to deploy software changes by 40 to 60%‚ and improve the compliance posture through the reuse of access control and audit frameworks‚ in particular when hybrid architectural patterns reduce security concerns.  Conclusion: Low-code orchestration constitutes a strategically viable modernization pathway for financial institutions, most effective when positioned as an orchestration layer complementing—rather than replacing—core payment processing engines. A domain-specific evaluation framework is proposed for guiding adoption decisions in regulated financial environments.

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Published

2026-05-12

How to Cite

Sellappan, K. (2026). The Efficacy of Low-Code Orchestration in Enterprise Payment Systems: A Comparative Study on Operational Scalability, Security, and Time-to-Market. International Journal of Artificial Intelligence and Machine Learning, 6(2s), 55–68. Retrieved from https://mail.svedbergopen.com/index.php/ijaiml/article/view/184

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