Predictive AI for Payment Network Failures: Proactive Risk, Resilient Systems

About the Session

Global payment systems like SWIFT gpi, UPI, and FedNow process over $12 trillion daily, making systemic failure a critical risk. Even minor anomalies can cascade into outages, costing institutions millions and damaging reputations. This session unveils an AI-driven predictive modeling framework that transforms failure response into proactive resilience. By mining defect logs, incident tickets, telemetry, and user signals, the system uses ensemble learning, time-series forecasting, anomaly detection, and NLP to flag potential issues before they escalate. Real-world deployments show measurable impact: a central bank cut critical incidents by 60%, and a global network boosted mean time between failures by 45%. A greenfield platform used global insights to prevent fraud bottlenecks pre-launch. We’ll explore the AI architecture, risk matrix evolution, and integration into agile workflows including sprint planning and QA resourcing. Attendees will gain actionable tools to shift from reactive troubleshooting to intelligent prediction vital in an economy where uptime is national infrastructure.

Speaker

Muni Chandra Sriperumbudur

IT and Project Management Professional