This course presents statistical, computational and machine-learning techniques for predictive detection of fraud and security breaches. These methods are shown in the context of use cases for their application, and include the extraction of business rules and a framework for the interoperation of human, rule-based, predictive and outlier-detection methods.
Methods presented include predictive tools that do not rely on explicit fraud labels, as well as a range of outlier-detection techniques including unsupervised learning methods, notably the powerful random-forest algorithm, which can be used for all supervised and unsupervised applications, as well as cluster analysis, visualisation and fraud detection based on Benford’s law. The course will also cover the analysis and visualisation of social-network data.
A basic knowledge of R and predictive analytics is advantageous.
Who should attend?
This course is suitable for all practitioners in fraud detection, law enforcement, security, compliance, insurance, audit and the finance function seeking an introduction and hands-on experience with data analysis techniques.
It is also perfect for IT and data analytics practitioners seeking to add fraud detection capability to their existing analytics skill set.
The course will be instructed by Presciient managing director Dr Eugene Dubossarsky. Eugene is a founder and Fellow of the Institute of Analytics Professionals of Australia (IAPA); Director, University of New South Wales School of Mathematics and Statistics Industry Advisory Board; and a recognised industry leader in Business Analytics. Eugene is an experienced, professional data miner of 20 years’ experience programming in R and its parent language, S.
Attendees are recommended to have completed Presciient’s Introduction to R two-day course, or equivalent.