Detecting New and Evolving Fraud Patterns in Digital Commerce

2018-08-24T16:13:54+00:00August 14th, 2018|Technical Posts|

As attacks grow in scale and velocity, businesses are forced to evolve their fraud detection methods from manual detection involving blacklists and rule engines to machine learning algorithms that can detect known and emerging types of fraud. This article highlights why existing fraud detection methods have limitations and more importantly a few reasons why unsupervised machine learning is gaining traction.

Guest Post: AML Data Quality – The Challenge of Fitting a Square Peg into a Round Hole

2018-04-17T17:35:04+00:00April 17th, 2017|Technical Posts|

As mentioned in my previous articles, traditional rule-based transaction monitoring systems (TMS) have architectural limitations which make them prone to false positives and false negatives: Naive rules create a plague of false positives that are [...]

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