Fraud Analytics

In this course, participants learn how to use analytics in the fight against fraud. We start by setting the stage and review the importance of fraud, its definition, some examples, challenges and approaches. For the data preprocessing part, we refer to our Machine Learning Essentials course. Feature engineering is discussed next. This is a very important step to boost the performance of your analytical fraud models. We then give an overview of various methods to deal with imbalanced data sets, a very typical problem in fraud detection since usually only a few transactions are fraudulent. We briefly elaborate on supervised learning for fraud detection (see our Machine Learning Essentials course for more details). Unsupervised learning for fraud detection or anomaly detection is covered next. The course concludes by extensively discussing social networks for fraud detection.

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. Throughout the course, the instructors also extensively report upon their research and industry experience.

The course features more than 3 hours of video lectures, more than 50 multiple choice questions, and references to background literature. A certificate signed by the instructors is provided upon successful completion.