SafeBet is a research project in Ulster University focussing on creating algorithms that rapidly detect online peer-to-peer gambling fraud through DeepTech that analyses and aggregates individual in-game patterns, trends and behaviours for multi-player games.
Online gambling techniques for peer-to-peer fraud detection focus mainly on detection of fraudulent accounts and activities. Traditional methods to combat these accounts include rule-based fraud detection techniques using IP/device/location tracking or fingerprint-based verifications which fraudsters can learn to overcome. Some companies also analyse account activity at the withdrawal stage to determine whether they should pay out.
Whilst these methods have had a great deal of success in the past, they fall short in more ways than one considering the evolving calibre of online fraudsters. Our novel deep-tech AI engine analyses in-game behaviour in real-time, looking for relationships, patterns, dependencies and hidden structures to reveal the tell-tale signs of fraud. SafeBet has the capability to learn and adapt to new behaviours over time, thus reducing the chances of a fraudster being able to overcome the technology. This allows companies to automatically detect fraudulent and suspicious behaviour before the withdrawal stage, thus reducing the company losses.
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