Krzysztof Nalborski

In my previous post, I described how fraud analytics for open banking work. Following an introduction to behavioral analytics, this post focuses on how the data refined through behavioral profiles are combined in an unsupervised machine learning model to produce a score indicative of fraud or non-fraud. Rationale for Utilising Unsupervised Models Regarding fraud ana…

Digital banking channels are increasingly popular, and behavioral profiling of customers is vital in preventing new types of fraud. The open banking revolution makes understanding each customer’s behavior even more important in preventing fraud by considering all the aspects of transactions. Transactions in the world of open banking contain data not previously seen in…

The recent fascination with artificial intelligence and machine learning has made some of us (naturally intelligent) humans confused about the role that these technologies play in the broader field of fraud analytics. In this blog post, I explain their usage and particularly how they will operate in the open banking revolution. [caption id="attachment_37041" align=…