E-learning courses on Advanced Analytics, Credit Risk Modeling, and Fraud Analytics

Together with SAS, Professor Bart Baesens has developed the following E-learning courses:

E-learning course: Advanced Analytics in a Big Data World

The E-learning course starts by refreshing the basic concepts of the analytics process model: data preprocessing, analytics and post processing. We then discuss decision trees and ensemble methods (bagging, boosting, random forests), neural networks, support vector machines (SVMs), Bayesian networks, survival analysis, social networks, monitoring and backtesting analytical models. Throughout the course, we extensively refer to our industry and research experience. Various business examples (e.g. credit scoring, churn prediction, fraud detection, customer segmentation, etc.) and small case studies are also included for further clarification. The E-learning course consists of more than 20 hours of movies, each 5 minutes on average. Quizzes are included to facilitate the understanding of the material. Upon registration, you will get an access code which gives you unlimited access to all course material (movies, quizzes, scripts, ...) during 1 year. The E-learning course focusses on the concepts and modeling methodologies and not on the SAS software. To access the course material, you only need a laptop, iPad, iPhone with a web browser. No SAS software is needed. See https://support.sas.com/edu/schedules.html?ctry=us&id=2169 for more details.

E-learning course: Credit Risk Modeling

The E-learning course covers both the basic as well some more advanced ways of modeling, validating and stress testing Probability of Default (PD), Loss Given Default (LGD ) and Exposure At Default (EAD) models. Throughout the course, we extensively refer to our industry and research experience. Various business examples and small case studies in both retail and corporate credit are also included for further clarification. The E-learning course consists of more than 20 hours of movies, each 5 minutes on average. Quizzes are included to facilitate the understanding of the material. Upon registration, you will get an access code which gives you unlimited access to all course material (movies, quizzes, scripts, ...) during 1 year. The course focusses on the concepts and modeling methodologies and not on the SAS software. To access the course material, you only need a laptop, iPad, iPhone with a web browser. No SAS software is needed. See https://support.sas.com/edu/schedules.html?ctry=us&id=2455 for more details.

E-learning course: Fraud Analytics using Descriptive, Predictive and Social Network Analytics

This new E-learning course will show how learning fraud patterns from historical data can be used to fight fraud. To be discussed is the use of descriptive analytics (using an unlabeled data set), predictive analytics (using a labeled data set) and social network learning (using a networked data set). The techniques can be applied across a wide variety of fraud applications, such as insurance fraud, credit card fraud, anti-money laundering, healthcare fraud, telecommunications fraud, click fraud, tax evasion, counterfeit, etc. The course will provide a mix of both theoretical and technical insights, as well as practical implementation details. The instructor will also extensively report on his recent research insights about the topic. Various real-life case studies and examples will be used for further clarification.

The E-learning course consists of more than 20 hours of movies, each approximately 5 minutes on average. Quizzes are included to facilitate the understanding of the material. Upon registration, you will get an access code which gives you unlimited access to all course material (movies, quizzes, scripts, ...) during 1 year. The E-learning course focusses on the concepts and modeling methodologies and not on the SAS software! To access the course material, you only need a laptop, iPad, iPhone with a web browser. No SAS software is needed. See https://support.sas.com/edu/schedules.html?ctry=us&id=1912 more details.

Bio: Bart Baesens

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 big data& analytics, fraud detection, marketing analytics and credit risk management. 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, Fraud Analytics using Descriptive, Predictive and Social Network Techniques, and Credit Risk Analytics: Measurement Techniques, Applications, and Examples in SAS. He teaches E-learning courses on Advanced Analytics in a Big Data World and Credit Risk Modeling. 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 fraud detection strategy.