Advanced Credit Risk Modeling for Basel/IFRS 9 using R/Python/SAS

In this course, students learn how to do advanced credit risk modeling. We start by reviewing the Basel and IFRS 9 regulation. We then discuss how to leverage alternative data sources for credit risk modeling and do feature engineering. This is followed by an overview of variable selection and profit driven performance evaluation. We discuss some advanced modeling methods such as ensemble methods, neural networks, and Bayesian networks. We then cover low default portfolios and validation. The course concludes by reviewing stress testing. Modeling methods, performance measurement and benchmarks are discussed into great detail. The course provides a sound mix of both theoretical and technical insights, as well as practical implementation details. These are illustrated by several real-life case studies and examples. The course also features code examples in R, Python and SAS. Throughout the course, the instructors also extenisvely report upon their research and industry experience.

The course features more than 6 hours of video lectures, multiple multiple choice questions, and various references to background literature. A certificate signed by the instructors is provided upon successful completion.

See this sample lecture video on YouTube to get a free teaser of the course contents.