AI-Enhanced SDLC Maturity Models for High-Performance Payment Systems

This study proposes an AI-Enhanced SDLC Maturity Model for High-Performance Payment Systems, designed to elevate both software delivery robustness and operational security within modern financial infrastructures. The model integrates machine learning (ML) analytics and real-time monitoring into mature SDLC frameworks, enabling:
• Predictive risk scoring for pipeline vulnerabilities.
• Continuous adaptive orchestration to anticipate delivery failures.
• Self-healing workflows through automated detection and remediation of anomalies.
Evaluated in both simulated and real-world payment processing environments, the model demonstrates up to 35% reduction in deployment failures, a 28% improvement in mean time to detection, and 22% lower fraud-related incident rates. These results showcase the potential of AI-driven SDLC maturity in bolstering resiliency and agility for financial systems. This paper contributes:
• A novel maturity model integrating AI agents into DevOps pipelines.
• Methodology for metric-based progression across maturity levels.
• Empirical validation via case study in a secure payment system.
• Discussion on limitations and future research directions.