Model Monitoring in Banking - How to Track Performance, Detect Drift, and Manage Risk
3 December 2025·10 min
Watch on YouTubeAbout this video
Model validation ensures a model is sound before use but the real test begins once it enters the live environment. In this session, we explore Model Monitoring and Performance Management, the stage where model behaviour meets real world complexity and where early warning signs of risk must be detected before they escalate.
Monitoring is essential because markets shift, customer behaviour evolves, data sources change, and assumptions age. Even the strongest models deteriorate over time. This lesson breaks down why model drift happens, how banks detect it, and what controls every institution must implement to ensure ongoing model health.
📌 What You Will Learn in This Video
• Why monitoring is a core pillar of model risk management
Understand how models degrade in production and why live performance management is critical for safety, regulatory confidence, and decision quality.
• Data drift vs concept drift explained
Learn the difference between changes in input data and changes in real world relationships and how each impacts model accuracy.
• Key monitoring metrics and tools
Explore PSI, CSI, accuracy metrics, stability testing, dashboards, automated alerts, and advanced monitoring techniques for AI and machine learning models.
• How to design effective alert thresholds and escalation workflows
See how institutions define in control, warning, and breach levels and how these link to issue management, remediation, and redevelopment.
• Model issue management and root cause analysis
Learn how teams identify why a model is failing and decide whether recalibration, redevelopment, or strategic retirement is required.
• Monitoring expectations under global regulation
Understand PRA SS123, SR 117, ECB TRIM, MAS FEAT, and how monitoring is now a regulatory requirement, not optional guidance.
• Advanced monitoring for AI driven models
Explore explainability drift, bias checks, retraining controls, and why machine learning models need additional oversight.
• How monitoring drives strategic model lifecycle decisions
Discover how performance insights help firms identify obsolete models, prioritise redevelopment, and maintain a healthy model portfolio.
🎯 Who This Video Is For
Model developers
Model validators
Risk managers and governance teams
Data scientists and ML engineers
Internal audit professionals
Senior leaders overseeing risk and analytics
Anyone working with models in banking or financial services
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🔍 Keywords
model monitoring, model performance, model drift, data drift, concept drift, PSI, CSI, model accuracy, AI model governance, machine learning monitoring, PRA SS123, SR 117, ECB TRIM, model risk management, financial models, model lifecycle, bank risk management, the industry portal, model risk course
model monitoringmodel performance managementmodel driftdata driftconcept driftmodel risk managementbanking modelsPRA SS1/23SR 11/7monitoring metricsPSICSI
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