Unlocking AI for financial risk management
View AgendaKey reasons to attend
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Learn the requirements of explainable AI models from a regulatory perspective
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Understand the implication of AI on MRM and its processes
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Identify best practices for mitigating fraud and AML using AI
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About the course
Join us for a 4-day interactive course, led by subject matter experts, to get a deeper understanding of topics such as AI model explainability, interpretability and bias.
With the fast-paced nature of AI, attending this course allows attendees to develop a stronger understanding of how AI is changing the face of traditional banking supported by practical uses of AI such as managing financial crime and impact on operational risk.
This course will guide attendees in understanding best practices for implementation of AI into their financial institution, providing insight into the tools needed to better identify the risks associated with AI and to help mitigate setbacks from immature use of AI.
Learning objectives
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Learn to comply with regulations regarding AI
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Differentiate between explainability and interpretability
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Manage risks associated with bias and fairness
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Identify key ethics indicators
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Utilize model validation tools
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Assess the quality and maturity of AI systems
Who should attend
Relevant departments may include but are not limited to:
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AI/ML
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Risk management
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Model validation
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Data science
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Operational risk
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Digital innovation
Pre-reading materials
The Risk.net resources below have been selected to enhance your learning experience:
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Vulnerabilities arise in financial services as AI and machine learning use balloons - Read article | Risk.net
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Why machine learning quants need ‘golden’ datasets - Read article| Risk.net
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Banks fear Fed crackdown on AI models - Read article | Risk.net
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