About this online course

Overview

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Understand the iceberg: what lurks beneath the surface?

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:

  • IFRS9, Basel II-III and the future Basel IV;
  • latest approaches to PD modeling – including GLM and Machine Learning techniques;
  • LGD modeling using survival models;
  • possible evolutions of the regulatory framework (such as TRIM, CRR, CRD IV, etc.);
  • recent technological advances in this field.

The course comprises four modules that offer an effective blend of theory and practice to make it challenging and valuable so you can use and apply this knowledge in practice from day one. With the knowledge and experience gained, you will be able to perform your current work tasks more effectively and enhance your future professional development in the field.

Join us for an underwater journey to the base of the iceberg. See the full picture and acquire cutting-edge knowledge and skills in a flexible and inspiring way!
Course Highlights

Learn at the time and place that suits you! This course offers access to course material 24/7. This includes video lectures, accompanying learning modules created by Deloitte, readings, exercises and assignments. We also offer you the opportunity to interact with other credit risk professionals worldwide and attend (optional) local Live Chapter Meetings for live discussions and question opportunities with experts and fellow participants.

Learning Objectives
  • Gain knowledge about the latest regulatory developments, such as IFRS9, Basel II, Basel 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.
Sample video lectures


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Study at the time and place that suits you!
If you wish to take this course at your own pace, you can do so if you register before July 31, 2019. The course content will be available from the moment you enroll until September 30, 2019. If you start the course in February-March you will additionally benefit from active moderation in the course forum and the possibility to join our (optional) Live Chapter Meetings. With an estimated effort of 4 - 6 hours per week the course can be finished in 10 weeks.

"A great experience for everyone dealing with credit risk management and who wants to advance their knowledge in that field." - Model developer (Dutch Tier-1 bank)

"For me, this was a great experience and I believe I learned a lot, I truly enjoyed the course. The lectures were so nice and also additional materials were useful." - Model validator (Dutch Tier-2 bank)

Details

The estimated effort required to finish this course is 40 to 60 hours, but you may take additional time to complete all the assignments at your own pace. The course content will be available until September 30, 2019. The course will provide the following:

  • 4 modules covering more than 20 topic areas with an estimated workload of 4-6 hours per week
  • Quizzes, exams and final assignment assessments to reinforce key learning concepts
  • A business view on credit risk and the opportunity to put theory into practice in lectures provided by Deloitte
  • Discussion forums to discuss thought provoking questions about credit risk
  • Downloadable course material
  • Course material remains accessible after ending the course

Is this course for you?

This course is for professionals eager to master the most important models of credit risk management and to keep up with the ever-changing regulatory framework.

The course is most relevant to:

  • all risk professionals – model developers, model validators, managers, consultants and developers.
  • this course caters specifically for the financial services industry, shadow banking entities (leasing, factoring, etc.) and telecoms, however the course could also be of value for most other industries.

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.

Instructors

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.


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Qualifications

By completing this course you will earn a professional education certificate from TU Delft. With this certificate you are eligible to receive 4.0 Continuing Education Units (CEUs).


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Admission

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.

Contact

If you have any questions about this course or the TU Delft online learning environment, please visit our Help & Support page.

Enroll now

  • Starts: Anytime (Self-Paced)
  • Fee: € 1500
  • Enrollment open until: Jul 31, 2019
  • Length: Self-Paced
  • Effort: 40 - 60 hours

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