EXECUTIVE TRAINING IN CREDIT RISK MODELING
‘STATE OF THE ART IN CREDIT RISK MODELING’ WITH BART BAESENS
Credit risk modeling is undoubtedly one of the most crucial issues in the field of financial risk management. With the recent financial turmoil and regulatory changes introduced by the Basel accords, credit risk analytics has been receiving even greater attention by the financial and banking industry. In this web series, Bart Baesens will decompose credit risk into its three components: PD (probability of default), LGD (loss given default) and EAD (exposure at default). We will then introduce a multi-level architecture to model the three components and illustrate how this can also be used for back-testing, benchmarking, and stress testing. Throughout the discussion, we will highlight our research, industry experience, and recommendations on the current environment and challenges mentioned. The webinar concludes with an overview and complimentary invitation to our new Self-Paced E-learning course on Credit Risk Modeling led by Bart Baesens.
What you get
- Executive training from 3 time author and lecturer on Credit Risk Modeling
- Deep dive analysis of the top three Credit Risk components
- Introduction to a multi-level architecture model for Credit Risk Modeling
- Best practices in back-testing, benchmarking, and stress testing methodologies
- Invitation to Bart Baesens’ Credit Risk Modeling course
ABOUT BART BAESENS
Bart Baesens is a professor at KU Leuven (Belgium), and a lecturer at the University of Southampton (United Kingdom). He has done extensive research on credit risk modeling, big data & analytics, and fraud analytics. His findings have been published in well-known international journals (e.g. Machine Learning, Management Science, IEEE Transactions on Neural Networks, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Evolutionary Computation, Journal of Machine Learning Research, …) and presented at international top conferences. He is author of the books Credit Risk Management: Basic Concepts, Analytics in a Big Data World and Fraud Analytics using Descriptive, Predictive and Social Network Techniques and teaches E-learning courses on Credit Risk Modeling and Advanced Analytics in a Big Data World. His research is summarized at www.dataminingapps.com. He also regularly tutors, advises and provides consulting support to international firms with respect to their big data, analytics and credit risk management strategy.