Many companies currently still rely on hard-coded, inflexible ways of detecting anomalies in their financial transactions, hence leading to lots of false positives or risking fraudulent transactions to be executed. How can anomalies be found in financial transactions when we don’t know what indicators we are looking for in advance? One possible solution to this is building a machine learning model that attempts to automate the approach. The model measures how easy individual data points can be separated from the rest of the data (i.e. using an isolation forest). In this webinar we will explore how to build such a Machine Learning model, how to use its outputs, and how to create a complementary explanation model to interpret and validate these outputs.
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