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.

See what former trainees are saying about this course.

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.

Course Booking Terms and Conditions

Additional Information

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 In-person at AlphaZetta Academy locations or on-premise for corporate groups

Our online courses run as live online meetings using Zoom for the video meeting part and Microsoft virtual computers for the practical components. The benefit of having a live trainer for online training is you can ask questions, obtain mentoring from the trainer and interact with classmates.

Course participants will require the following technologies and online accounts. Please check that your setup satisfies these requirements:

  • Course participants will require the following technologies and online accounts:
  • Reliable computer (Windows, Mac or Linux)
  • Webcam (to help facilitate the mentoring aspect of our training)
  • Reliable internet access
  • A quiet space
  • Zoom video conferencing software and Zoom account (register and pre-install the software at zoom.us)
  • Microsoft account in order to access the virtual lab PCs (Existing or new account. There’s nothing to be installed, you just need an account to sign-in with.)

Meals and refreshments

Face-to-face courses: 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.

Feedback

Use academy@alphazetta.ai to email us any questions about the course, including requests for more detail, or for specific content you would like to see covered, or queries regarding prerequisites and suitability.
If you would like to attend but for any reason cannot, please also let us know.

Variation

Course material may vary from advertised due to demands and learning pace of attendees. Additional material may be presented, along with or in place of advertised.

Cancellations and refunds

You can get a full refund if you cancel 14 days or more before the course starts. No refunds will be issued for cancellations made less than 14 days before the course starts.

Frequently asked questions (FAQ)

Do I need to bring my own computer?
This is dependent on the venue. Please check the course event page.

Why do I need to provide a shipping address?
For online courses, we need an address to send you the course notes that you need for the course.

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.

Enquire Now

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.

Alix Duncan

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.

Enquire Now

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