Fraud Detection Using Supervised, Unsupervised, and Social Network Analytics
Durchgeführt von Véronique Van Vlasselaer, Ph.D. Researcher, KU Leuven.
A typical organization loses an estimated 5 of its yearly revenue to fraud. This course will show how learning fraud patterns from historical data can be used to fight fraud. To be discussed is 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, 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.Lernen Sie, wie Sie / Learn how to
Zielgruppe / Who should attendFraud analysts, data miners, and data scientists; consultants working in fraud detection; validators auditing fraud models; and PhD researchers in financial services companies, banks, insurance companies, government institutions, healthcare institutions, and consulting firms
Before attending this course, you should have a basic knowledge of statistics, including descriptive statistics, confidence intervals, and hypothesis testing.
In diesem Kurs wird mit folgenden Software Modulen gearbeitet: SAS Enterprise Miner Software
Base SAS and SAS Enterprise Miner will also be used in this course.
|Kurstitel||Kursdauer||Gültigkeitszeitraum||Sprache||Kursgebühr||In den Warenkorb|
|Fraud Detection Using Descriptive, Predictive, and Social Network Analytics (released Feb 2018)||21.0 Stunden||180 Tage||Englisch||1,350 EUR|