AI-Accelerated Mainframe Modernization: Intelligent Migration Frameworks for Financial Infrastructure Transformation

Abstract

Mainframe-resident systems still process the substantial majority of core transaction volume in global financial infrastructure, yet the workforce able to maintain them is shrinking as COBOL expertise retires out of the labor market (U.S. Government Accountability Office, 2019). This paper presents a six-phase, AI-assisted migration framework combining automated code analysis, machine-learning-driven dependency mapping, and intelligent cutover validation, built for systems whose failure modes are systemic rather than local. We illustrate the framework's necessity through a systemic-dependency case, an eight-digit Bank Identification Number (BIN) expansion in payment infrastructure, and discuss the organizational and national-infrastructure-policy implications of modernizing legacy systems that sit at the center of critical financial plumbing.