I share the view that modern financial intermediation has proven that it has the potential to effectively spread risk and it has undoubtedly promoted economic efficiency and made capital available to productive sectors that would have otherwise not had access to any.
So far so good, but if one wants to embed the data science tools, such as the outlier detection tool, into existing workflows there is the challenge of correctly implementing and, subsequently, maintaining it. How to implement and maintain the tool? The traditional approach is to deliver a data science solution to an IT department as a Proof of Concept to implement in production.