Fraud Detection Using Descriptive, Predictive, and Social Network Analytics
Business Knowledge Series course
Presented by Bart Baesens, Ph.D. or Christophe Mues, Ph.D., Professors at the School of Management, or Cristian Bravo, Ph.D., Assistant Professor, Business Analytics, University of Southampton (UK); or Wouter Verbeke, Ph.D., Assistant Professor, Business Informatics, University of Brussels (Belgium); or Stefan Lessmann, Ph.D., Professor, School of Business and Economics, Humboldt University (Germany)
A typical organization loses an estimated 5 of its yearly revenue to fraud. This course shows how learning fraud patterns from historical data can be used to fight fraud. The course discusses the use of supervised learning (using a labeled data set), unsupervised learning (using an unlabeled 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, and counterfeiting. The course provides a mix of both theoretical and technical insights, as well as practical implementation details. During the course, the instructor reports extensively on his recent research insights about the topic. Various real-life case studies and examples are presented for further clarification.
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Who should attend
Interested in this course? Join our interest list and receive course updates and scheduling information as it becomes available.
Before attending this course, you should have a basic knowledge of statistics, including descriptive statistics, confidence intervals, and hypothesis testing.
This course addresses SAS Enterprise Miner software.Base SAS and SAS Social Network Analytics are also used in this course.
|Title||Duration||Access Period||Language||Fee||Add to Cart|
|Fraud Detection Using Descriptive, Predictive, and Social Network Analytics||21.0 hours||365 days||English||1020 USD / 2.16 EPTO|