Purpose of Programme
This one-day hands-on workshop takes first-line developers and business teams, together with banking risk, audit and technology professionals, under the hood of the AI systems they build, deploy or govern. Where the Deep Dive into AI Governance and Risk Management for Banks builds intuition, this course builds a working, hands-on understanding of how these systems work and fail: across machine learning, generative AI and agentic AI, participants work with live models and demonstrations to see how these systems are built, how they fail and how they are tested, and run fairness, explainability and evaluation techniques for themselves. It serves both the second and third lines, who must challenge and validate AI, and first-line developers and business teams, who want a working, hands-on familiarity with the AI risk management techniques and tools they are increasingly expected to apply themselves, and the judgement to know what to demand of them. Anchored in the MAS AI Risk Management Guidelines (AIRG) lifecycle controls, the course closes with a lab in which participants build their own GenAI and agentic chatbot and then try to break it. This is a governance course, not an engineering course: in a day it does not make participants ML engineers or model validators; it makes them able to interrogate and challenge those who are.
What You'll Learn
On completion, participants will be able to:
• Interrogate an AI model or vendor on its technical mechanism, not just its process;
• Distinguish the mechanisms and failure modes of ML, deep learning, generative and agentic AI;
• Evaluate the evidence behind an AI system (evaluation design, testing, red-teaming, guardrails);
• Assess explainability and fairness claims against faithfulness and the impossibility of satisfying all definitions at once;
• Understand the tooling landscape and what a tool report does not measure;
• Translate observed failures into the tests and evidence to demand of a builder or vendor.
• Interrogate an AI model or vendor on its technical mechanism, not just its process;
• Distinguish the mechanisms and failure modes of ML, deep learning, generative and agentic AI;
• Evaluate the evidence behind an AI system (evaluation design, testing, red-teaming, guardrails);
• Assess explainability and fairness claims against faithfulness and the impossibility of satisfying all definitions at once;
• Understand the tooling landscape and what a tool report does not measure;
• Translate observed failures into the tests and evidence to demand of a builder or vendor.
COURSE FEE
$850.00
(Before Subsidy)
SCHEME
FTS
Skills & Competencies Requirements
| SKILLS CATEGORY | SKILLS AND COMPETENCIES | PROFICIENCY LEVEL |
|---|---|---|
| Future-Enabled Skills | Generative Artificial Intelligence |
ADVISORY
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