Fraud

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When Machine Learning and Anomaly Detection Cannot Detect Fraud: Be careful what you invest into

As part of being professional consultant building client-specific fraud detection solutions I often witness product pitches by different vendors in a security / fraud detection space.

The recent wave of successful high profile cyberattacks and disastrous data leaks added new level of activity into search for the perfect fraud detection and early alerting solution.

With attackers changing their activity vectors, attack patterns and techniques on a daily basis this makes many legacy fraud detection tools to lose their efficiency and get outdated very quickly.

In the never ending quest to protect enterprise against fraud losses the ideas of Automated Anomaly Detection are picking up steam.

The way it generally works – anomaly detection system would establish baseline for certain predefined dimensions and system would then monitor (often in real time) for deviations from established baseline. Once sufficiently abnormal condition is detected – the alert is issued. Such system could operate pretty automatically and learn from historical and present data constantly updating it’s baselines as well as trigger thresholds. […]

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