Model risk management workshop
View AgendaKey reasons to attend
- Access impactful ways to maximise data and analytical abilities
- Implement strategies to optimise and transform models
- Evaluate the key elements and design principles of model validation
Customised Solutions
Does your team require a tailored learning solution on this or any other topic?
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
This in-person event offers the opportunity for participants to enhance their understanding of MRM by exploring the key characteristics and emerging technologies in the industry.
Participants will learn how to develop a healthy MRM framework by studying model risk appetite and optimal organisational structures. Sessions will explore key requirements for effective MRM, such as the governance, policies and controls required.
Through active learning and Q&A sessions alongside an expert tutor, participants will acquire the necessary tools to improve the robustness of models in order to withstand the current volatile markets.
Learning objectives
- Identify relevant model validation techniques and approaches
- Implement artificial intelligence (AI) applications to model risk management (MRM) frameworks
- Address the impact of environmental, social and governance (ESG) factors in financial risk modelling
- Align current trends and regulatory requirements between AI, machine learning and MRM
- Apply appropriate policies and procedures into MRM frameworks
- Evaluate the results of model quantification
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
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
Pre-reading materials
The Risk.net resources below have been selected to enhance your learning experience:
- A new automated model validation tool for financial institutions - Read article
- Model risk management is evolving: regulation, volatility, machine learning and AI - Read article
- Unlocking the power of model ops for risk management gains - 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