MICROSERVICES FOR FINANCIAL RISK INTELLIGENCE: SECURE DATA ARCHITECTURE FOR ML-DRIVEN FINTECH

Abstract

The rapid evolution of financial technology (FinTech) has accelerated the integration of microservices, financial
risk intelligence, secure data architecture, machine learning (ML), and enterprise solutions to achieve both
innovation and resilience. This study examines how modular architectures, UX-driven generative AI (GenAI),
and enterprise configure–price–quote (CPQ) systems collectively influence financial performance, customer
experience, and organizational governance. Using a mixed-method design that combined survey data from 250
industry professionals, expert interviews, and secondary enterprise reports, the research evaluated technological,
financial, organizational, and user-experience variables. Results indicate that microservices enhance scalability
and interoperability, which in turn strengthen ML-driven risk intelligence, while secure data architecture
significantly supports UX-driven GenAI outcomes. CPQ systems were found to be critical in translating
technological capabilities into measurable revenue growth and reduced leakage. Structural equation modeling
confirmed the mediating roles of ML-driven risk intelligence and GenAI in improving revenue management
outcomes. Cluster analysis revealed three organizational archetypes innovators, pragmatists, and traditionalists
with innovators achieving superior results through integrated adoption of all systems. Overall, the findings
underscore that the greatest performance gains arise when these components are implemented holistically,
offering a roadmap for FinTech firms to balance innovation with compliance, efficiency, and customer trust.