MODERNIZING LEGACY FINANCIAL PLATFORMS FOR AI ADOPTION: AN ARCHITECTURAL PERSPECTIVE

Abstract

Financial institutions worldwide grapple with aging technology infrastructures that inhibit their ability to leverage artificial intelligence for competitive advantage. This research examines the architectural challenges and strategic approaches for modernizing legacy financial platforms to enable AI adoption while managing operational risks. Through analysis of migration patterns, refactoring strategies, and risk mitigation frameworks, we identify key architectural principles that facilitate successful transformation. Our study reveals that successful modernization requires a balanced approach combining incremental migration, data architecture restructuring, and parallel system operation during transition periods. The research demonstrates that organizations achieving AI readiness prioritize data accessibility, API-driven architectures, and modular system design over complete platform replacement. We present a comprehensive framework for assessing legacy system AI readiness and designing migration paths that minimize disruption while maximizing technological capability. This work contributes practical guidance for financial technology leaders navigating the complex intersection of legacy systems, regulatory constraints, and emerging AI opportunities.