Fraud And Anomaly Detection
Fraud And Anomaly Detection
SYDNEY: 27-28 November
Date & Time
Tue., 27/11/2018, 9:30 am –
Wed., 28/11/2018, 5:00 pm
65 York Street
Sydney, NSW 2000
About the Course
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.
Having studied stats at Uni I was surprised how far the field has progressed in the last few years, particularly in the area of big data. The great thing about Eugene’s course is I left with a sense that I was up to date with the latest big data modelling concepts but more importantly could also deploy them with some confidence using R. Eugene also made it clear he was available to answer questions after the course, so you are not left hanging. I would absolutely recommend this!
—Damon Rasheed, CEO, Rate Detective
For someone who does not come from an IT background R is a terrifying program. Before doing the Introduction to R course I had previously done other courses in R but always found myself in over my head because they assumed a high level of program experience (even course that required no prior programming knowledge). This course is not like that at all. It starts at ground zero and teaches you everything you need to know to be able to use R confidently in your everyday workplace. It is a must attend for anyone who wants use R!
Data science can be a challenging topic but Eugene’s “Introduction to Machine Learning” course turns complex statistical models into plain English. The course contents and presentation were accessible and I enjoyed the mixture of hands-on rattle() exercises, the challenge of building multiple models with real life data, and the salient theory whiteboard discussions created many “aha” moments.
It was a great introductory course and it gave me with a better grasp of Machine Learning in general, a great framework for thinking about it and practical hands-on skills that I can put to immediate use. I wish I had done this course sooner.
—Charl Swart, Director of Business Operations, Unisys Credit Services
Questions and Further Information
Meals and refreshments
Catered morning tea and lunch are provided on both days of the course. Please notify us at least a week ahead if you have any special dietary requirements.
Course material may vary from what is advertised due to the demands and learning pace of attendees. Additional material may be presented along with or in place of what is advertised.
Frequently asked question(s) (FAQ(s))
Do I need to bring my own computer?
There’s no need to bring your own laptop or PC. Our courses take place in modern, professional training facilities that have all the computing equipment you’ll need.