AI Model Risk & Governance in Financial Services

Program Details

This program provides a rigorous introduction to the governance, monitoring, and validation of AI/ML models deployed in financial services. With financial regulators worldwide tightening expectations around AI usage, model transparency, explainability, and controls have become essential.

Participants explore not just what AI systems do, but how they must be governed to satisfy supervisory standards such as the EU AI Act, OCC/Fed Model Risk Guidance, MAS FEAT, NIST AI RMF, and global best practices.

The program integrates conceptual learning with hands-on exposure to validation workflows, documentation templates, explainability tools, and real regulatory case studies.

Curriculum: A Bird’s-Eye View

AI/ML Model Lifecycle: development, testing, deployment, monitoring
Governance frameworks: second-line oversight, policy design, risk controls
XAI & Responsible AI: explainability metrics, fairness, bias, drift, stability
Validation of ML systems: conceptual soundness, outcomes analysis, benchmarking
Regulations: EU AI Act, MAS, OCC/Fed SR 11-7, PRA/ECB expectations, NIST
Documentation: model governance documents, inventories, decision logs, audit trails
Monitoring controls: performance drift detection, data drift, robustness checks

Who Should Attend?

04

Risk managers overseeing algorithmic models and automated systems

01

Model validation and model risk teams expanding into AI

02

Data scientists who need regulatory and governance context

03

Technology leaders overseeing AI deployment within regulated financial environments.

What Distinguishes Certificate in AI Model Risk & Governance of Riskinfo.AI

Designed by practitioners from leading banks, fintechs, and Big 4 consulting firms
Translates complex regulatory requirements into actionable governance processes
Includes downloadable templates for inventories, validation checklists, and governance policies
Focuses on skills required in high-demand AI assurance and Responsible AI roles