This course presents statistical, computational and machine-learning techniques for 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.
Discounts
Face to face public courses: early bird pricing is available until 2 weeks prior. Group discounts: 5% for 2–4 people, 10% for 5–6 people, 15% for 7–8 people, and 20% for 9 or more people. Discounts are calculated during checkout.
Online public courses: available at a 25% off the face-to-face courses as a special introductory price. to groups or to individuals who want to follow a curriculum program and attend multiple courses:
- 2-4 courses/attendees 10% off
- 5+ courses/attendees 20% off
Hurry as bookings will close 1 week before each course. Group discounts are calculated during checkout on individual courses. Individuals can book multiple courses at a discount – please enquire.
Additional Information – Fraud and Anomaly Detection
| Audience | 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. |
| Pre-requisites | Students should have completed or have equivalent knowledge to the course Fundamentals of AI, Machine Learning, Data Science and Predictive Analytics and Intro to R (+ Data Visualisation) |
| Objective | Gain insight into statistical, computational and machine-learning techniques for predictive detection of fraud and security breaches. |
| Format | Class |
| Duration | 2 days |
| Course Author | Dr Eugene Dubossarsky |
| Trainer | Courses are taught by Dr Eugene Dubossarsky and/or his hand-picked team of highly skilled instructors. |
| Delivery Method | Online, in-person at AlphaZetta Academy locations or on-premise for corporate groups |
Private and Corporate Training
In addition to our public seminars, workshops and courses, AlphaZetta Academy can provide this training for your organisation in a private setting at your location or ours, or online. Please enquire to discuss your needs.
Testimonials
Eugene’s fraud and anomaly detection course is extremely valuable for anyone wishing to learn more about fraud detection using analytical techniques. Eugene’s ability to cater and tailor the course for all levels of experience is fantastic and much appreciated.
The Data Analytics for Fraud and Anomaly Detection in Forensics and Security course is brilliant. By the end of the course you will walk away with tools and statistical modelling techniques you can implement in your everyday business. Best of all because Eugene is able to explain complex statistical models in plain English you will have an understanding of how to implement these models successfully.
Scheduled Public Courses
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Private and Corporate Training
In addition to our public seminars, workshops and courses, AlphaZetta Academy can provide this training for your organisation in a private setting at your location or ours, or online. Please enquire to discuss your needs.
