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Original research AI-DRIVEN CODING, MACHINE LEARNING FRAUD DETECTION, AND BLOCKCHAIN-ENABLED API INTEGRATION FOR SECURE PAYMENT GATEWAY PERFORMANCEPages 211-224
Abstract
Secure digital payment gateways increasingly depend on AI-assisted development, machine-learning fraud detection, blockchain controls, and reliable system integration. This study examines how these capabilities and related operational mechanisms influence secure payment gateway performance. A cross-sectional survey of 500 technical respondents in Myanmar was analyzed using SPSS and AMOS through reliability analysis, confirmatory factor analysis, structural equation modeling, bootstrapped mediation, and moderation analysis. The measurement model showed acceptable fit (CMIN/DF = 1.063, CFI = 0.993, RMSEA = 0.011), and the structural model explained 64.6% of performance variance. AI-driven coding, machine-learning fraud detection, API integration reliability, security assurance quality, and blockchain trust mechanisms had significant positive direct effects, with security assurance quality showing the strongest effect. Eleven indirect effects and three moderation effects were supported. The findings highlight the importance of combining technological resources with assurance, integration, compliance, and trust capabilities to support reliable and secure digital payment services.
Keywords:
AI-driven coding, machine learning fraud detection, blockchain, API integration reliability, secure payment gateways, cybersecurity resilience.
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Thaingar Aung Soe,
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