Think you know enough about Credit Risk Management?
Follow our advanced online course and start to understand the iceberg.
Acquire cutting-edge knowledge and skills in a flexible and inspiring way
This unique online course from TU Delft in collaboration with Deloitte is for ambitious risk professionals, consultants and managers eager to master the most important models of credit risk management, and to understand and discuss the always-changing regulatory framework.
- 40-60 hours
- Professional Education Certificate
- Diverse learning material
- 24/7 online access
- Interaction with participants & instructor
This online course comprises four modules that offer an effective blend of theory and practice to make it challenging and valuable for your work. With the knowledge and experience gained, you will be able to advance your current work tasks and support your future professional development in the field.
Dr. Pasquale Cirillo delivers the academic part of the course. He is a risk expert from the Department of Applied Mathematics at the Delft University of Technology (TU Delft), coordinator of the financial engineering specialization and experienced statistical consultant for major companies and institutions. Credit risk practitioners from Deloitte share knowledge on credit risk in practice to ensure the course is relevant for the industry.
A Live Chapter Meeting will take place to complement the online course and attendance to this session is optional.
The Live Chapter Meeting takes place at a Deloitte office. It is an interactive session on current credit risk topics where you can meet your fellow participants and the instructors, and discuss your questions. The Live Chapter Meeting does not impact the grading. The Live Chapter Meeting will take place around May 2019 and exact locations are to be announced.
Take a deep dive into the subject of Credit Risk
You may think you know a lot about the management of risk but could there be more to it than meets the eye?
This unique online course takes a deep dive into the subject of
credit risk. It helps ambitious risk professionals, consultants and
managers stay abreast of the latest developments in this field. You will gain in-depth knowledge and hands-on experience of:
- Latest approaches to PD modeling - including GLM and Machine Learning techniques
- LGD modeling using survival models
- Possible evolution of the regulatory framework (such as TRIM, CRR, CRD IV, etc.)
- Recent technological advances in this field
- IFRS9, Basel II-III and the future Basel IV
Course contents
The course covers topics such as:
- The latest regulatory requirements, e.g. IFRS9, Basel II-III
and the future Basel IV. - Strengths and weaknesses of important credit risk models.
- Model risk and error quantification.
- A solid understanding of the mathematics behind credit
risk modelling.
Mehr zu Risk Advisory
For professionals eager to broaden their understanding of credit risk
Who should participate?
Anyone who is ambitious enough to seek a higher level of competence when dealing with credit risk. Participants may include:
- Credit risk professionals and managers who would like to understand what lies behind the formulas and models they use on a daily basis.
- Risk professionals who would like to increase their understanding of credit risk.
Prerequisites:
- Knowledge of basic risk management.
- Statistics and probability at university level (upper bachelor level). For those needing revisions, links to external resources will be provided.
- Professional business experience is a plus.
- During the course, codes and examples will be developed using the R language (freely downloadable), but participants are free to use their preferred language.
Learning objectives
- Gain knowledge about the latest regulatory developments, such as IFRS9, Basel II-III and the future Basel IV.
- Develop a more solid understanding of the mathematics behind credit risk modeling, which will help you to better understand the foundation of the formulas and models you regularly use.
- Analyze the strengths and weaknesses of important credit risk models.
- Work with model risk and error quantification.
- Investigate the implications of dropping assumptions like Gaussianity.
- Explore open questions like small sample corrections and dependence modeling.
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