7/25/2023 0 Comments Black box it![]() To make the data applicable for the algorithm of the selected model, the first step is to extract relevant information out of the data – a process we call “feature engineering”. A specific algorithm analyses the existing data and provides a function (also called a classifier) that can be used in order to make an informed decision on previously unknown transactions. This means that labelled data is used for training a model. So how does this magical “black box” really work? At Risk Ident, we use mainly “supervised machine learning” for our fraud prevention software stack. Well-integrated machine learning provides the most accurate predictions, works in real-time and – most importantly – can easily adapt to new fraud patterns.Ī concern about machine learning from some fraud managers and specialists is that they find it unintuitive and non-transparent, often calling it a “black box”, cloaking everything that happens inside. This is because it has some huge advantages compared to rule-based systems, which are very common in today’s fraud prevention industry. In fact, machine learning is a very important part of modern software across industries, and it also provides great assistance when it comes to fraud prevention. This may sound quite bold, but if you think about self-driving cars, speech recognition or advanced web search, does it really seem like an exaggerated claim? Roberto Valerio, Risk Ident: Machine learning is sometimes referred to as a revolutionary approach in technology
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