Fraud Detection Using Descriptive, Predictive, and Social Network Analytics
Business Knowledge Series course
Presented by Bart Baesens, Ph.D. Professor at KU Leuven (Belgium), and lecturer at the University of Southampton (UK); or Tim Verdonck is Professor of Statistics and Data Science at the Department of Mathematics of University of Antwerp (Belgium)
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.Learn how to
Who should attendFraud analysts, data miners, and data scientists; consultants working in fraud detection; validators auditing fraud models; and researchers in financial services companies, banks, insurance companies, government institutions, health-care institutions, and consulting firms
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 (released Feb 2018)||21.0 hours||180 days from order date||English||6 120 SEK|