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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.

Please refer to full course details at https://alphazetta.ai/fraud-and-anomaly-detection/

Earlybird pricing is available up to 2 weeks prior.

This is an AlphaZetta public course – group discounts are available and also apply during the earlybird period (up to 2 weeks prior): 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.

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