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Presentation: "Modern Fraud Prevention using Deep Learning"

Track: The State of Data / Time: Tuesday 14:30 - 15:20 / Location: Grandball

In any language and all cultures, financial gain is the aim of most people. Unfortunately, many people (and companies!) resort to techniques that lie somewhere between immoral and criminal. Currently, many companies rely on a combination of external scoring services and internal manual fraud prevention procedures. This presentation will introduce ideas that are able to automate the detection of fraud and automate the analysis of new unknown fraud patterns.

Fraud detection is very specific to a particular domain. For example, credit card fraud is completely different to benefit fraud. This means that solutions must be bespoke and must fulfill the company's specific requirements. Mortgage fraud will be used as an example throughout this talk, although the techniques are applicable to any domain. It will discuss using software, databases and cluster computing frameworks to automate detection. It will also introduce the field of Machine Learning, with a particular focus on Deep Learning. Modern deep learning algorithms allow machines to analyze huge amounts of data to automate the discovery of new fraud vectors.

If you are thinking about implementing a fraud detection system for your business, and are interested in the cutting-edge of fraud detection technology, then this is the talk for you. Alternatively, feel free to visit me at the Trifork stand to talk about your specific domain. If you don't know what Machine Learning is and would like a buzzword-free introduction and example, this is also the talk for you.

Keywords: Fraud, Detection, Machine Learning, Deep learning, Trifork, NoSQL, Spark, Python, Scala, Java

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Phil Winder, Engineer at Trifork Leeds

Phil Winder

Biography: Phil Winder

Phil Winder is a multi-disciplinary consultant architect for Trifork Leeds Ltd, part of an international Software consultancy. His role is a combination of research, strategy, consultancy and sales. Describing himself simply as an “Engineer”, he has 10 years experience in a wide range of Engineering disciplines. Before joining Trifork, he worked for a fibre-optic acoustics company. Here he ran development projects to enable new verticals by writing machine learning algorithms, to perform complex classification tasks. Phil has Ph.D. and Masters degrees from the University of Hull, U.K. These were in Electronics, with a focus on embedded signal processing.

Twitter: @DrPhilWinder