Credit risk model management
View AgendaKey reasons to attend
- Explore the impact of Basel 3.1 and International Financial Reporting Standard 9 (IFRS 9) on credit risk modelling
- Learn how to develop a robust model validation framework
- Discover stress-testing techniques for credit risk portfolios across diverse economic scenarios
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About the course
This course provides insights into the effective management of credit risk models, focusing on the latest Basel 3.1 and IFRS 9 requirements. Participants will deepen their understanding of key estimation techniques, learn best practices in stress-testing across portfolio types and explore strategies for adapting models to economic shifts.
Through discussions on AI applications in credit risk modelling and guidance on model validation, attendees will learn to enhance model accuracy and transparency. The course also covers essential governance practices, including risk appetite, policy development and adherence to evolving regulatory standards.
Subject matter experts will address the unique challenges posed by both high- and low- default portfolios, equipping participants with the skills to optimise risk frameworks and build resilience in today’s dynamic economic landscape.
Avoid the price increase - book by December 31, 2024
Save up to $1,000*. Use promo code ‘LOCK24’ at checkout or contact us at learning@risk.net for more details.
Pricing options:
- Early-bird rate: save up to $800 per person by booking in advance (refer to the booking section for the deadline)
- 3-for-2 rate: save over $2,000 by booking a group of three attendees (applicable to this course)
- Subscriber reward: save 30% off the standard rate if you are a Risk.net subscriber (use code SUB30)
- Season tickets: save over $1,000 per person by booking 10 or more tickets (available on selection of courses)
*T&Cs apply
Learning objectives
Examine the evolving landscape of model risk management
Leverage artificial intelligence (AI) and machine learning to improve model accuracy
Discuss estimation techniques for high- and low-default portfolios
Explore strategies for handling missing scoring data and ratings assessments
Investigate the challenges associated with low-default portfolios under stress
Discover best practices for developing a credit risk appetite
Who should attend
Employees whose job responsibilities may include but are not limited to:
- Credit risk
- Risk modelling
- Risk management
- Model risk management
- Machine learning
- stress testing
Agenda
February 11–13, 2025
Live online. Timezones: Emea/Americas
Sessions:
- Introduction to credit risk model management and regulatory landscape
- Credit risk modelling developments
- Credit risk modelling post-IFRS 9
- Stress-testing credit risk portfolios
- Application of AI and machine learning in credit risk modelling
- Credit risk model validation
Pre-reading materials
The Risk.net resources below have been selected to enhance your learning experience:
- Analyzing credit risk model problems through natural language processing-based clustering and machine learning: insights from validation reports
- Emerging lessons from the current credit risk cycle
- Can CRE credit risk models cope with hybrid working?
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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