Model risk management and quantification
View AgendaKey reasons to attend
- Integrate artificial intelligence (AI) machine learning into model risk models
- Learn about topics such as end-to-end processes through in-depth case studies
- Focus on common pitfalls of model risk management (MRM)
Customised Solutions
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Working with the portfolio of expert tutors and Risk.net’s editorial team, we can develop and deliver a customised learning to make the most impact for your team, from initial assessment to final review.
About the course
Participants will gain an in-depth understanding of the application and necessary tools for successful model risk management and quantification. Sessions explore best practice techniques that are fundamental to structuring model risk profiles.
Supported by interactive case study examples, participants will explore the key principles necessary for effective and efficient MRM. Sessions will focus on novel areas such as the quantification of model risk, how to measure model risk in AI and machine learning models and how to gain real value from quantification results.
Flexible pricing options:
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Early-bird rate: book in advance and save $200
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3-for-2 group rate: book three delegates for the price of two and save more than $2,000
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Season tickets: book a team of 10 or more and save up to 50%
Learning objectives
- Successfully transform risk qualifications into a competitive advantage
- Evaluate the impact of recent developments in MRM frameworks
- Differentiate between controls and quantification
- Overcome challenges when approaching model risk quantification
- Identify what an ideal infrastructure for managing model risk looks like
- Align results from model risk quantification into real business impact
Who should attend
Relevant departments may include but are not limited to:
- MRM
- Model risk
- Pricing models
- Credit models
- Risk management
- AI
- Data science
- Technology
- Regulation
- Front office
Agenda
March 13–16, 2023
Timezones: Emea/Apac
Sessions:
- The importance of managing model risk
- Model risk regulatory landscape
- MRM frameworks
- Case study one: end-to-end processes for MRM
- Ideal infrastructure for managing model risk
- Case study two: examples of model risk quantification
- Model risk in artificial intelligence and machine learning models
- Case study three: presentation and use of model risk metrics
Tutors
Dr Nasir Ahmad
Managing partner
Basinghall Analytics
Dr Horst Kausch
Partner and head of research
Basinghall Analytics
Accreditation
This course is CPD (Continued Professional Development) accredited. One credit is awarded for every hour of learning at the event.
Pre-reading materials
The Risk.net resources below have been selected to enhance your learning experience:
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Machine learning and AI in model risk management: a quant perspective - Read article
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Covid chaos spurs on search for model risk aggregation - Read article
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Model risk management: building trust and governance - Read article
To access some of the above articles you need to have a current subscription to Risk.net. If you don’t have one now, please subscribe to a free trial
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