Credit risk model management
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.
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
Enquire about:
- Agenda and registration process
- Group booking rates
- Customisation of this programme
- Season tickets options
Accreditation
This course is CPD (Continued Professional Development) accredited. One credit is awarded for every hour of learning at the event.