JOIN US for a live webinar: State of the Art in Credit Risk Modeling by Prof. Bart Baesens
|Date:||Thursday, 22nd October 2015||Time:||15h00 CEST|
|Topic:||State of the Art in Credit Risk Modeling|
|Speaker:||Prof. Bart Baesens – Professor, KU Leuven University (Belgium) and Lecturer, University of Southampton (UK)|
|Link to Join:||https://meetings.webex.com/collabs/meetings/join?uuid=MAYEUHT6EQ50GI4DVYS1D8JHAS-FAZL|
Creditinfo Academy Webinar Overview
Credit risk modeling is undoubtedly among the most crucial issues in the field of financial risk management. With the recent financial turmoil and the regulatory changes introduced by the Basel accords, credit risk analytics has been receiving even greater attention by the financial and banking industry.
In this presentation, we 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 backtesting, benchmarking and stress testing.
Throughout the talk, we will embed both our research and industry experience and give key recommendations and guidelines to the challenges mentioned.
We will conclude by giving an overview of our new Self-Paced E-learning course on Credit Risk Modeling,
Professor 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.