As part of Mo.net 7.6, we have extended the external interface to compiled Mo.net tasks so that it now accepts a Python list. This update allows a new array of inputs to be passed from Python to the compiled Mo.net task DLL without having to create an intermediate inputs file.
One thing that I’m frequently asked is how client models can be better integrated with database or data warehouse environments. This article explores what else might be possible with Mo.net…
Are actuaries and other “power users” of Excel using their ingenuity to solve solutions with a relatively limited set of functions… or are they ignorant of other functions that may be better suited to the particular use case?
Following on from the Mo.net Loves Python series. this article outlines how to change the input data used by the Mo.net projection when being called from Python.
In the last article I demonstrated how to run a Python script with arguments from a Mo.net group projection task and return the results from the script back to Mo.net. In this third and final part of the Mo.net Loves Python series, I will reverse the scenario and use a Python script to call a Mo.net projection task.
Part two of our Mo.net Loves Python series. We look at how to run an existing Python-based Black Scholes model from a Mo.net group projection task, passing in arguments to define the vector size to use, and retrieving the completion message & run time from the Python script.
The first article in our Mo.net Loves Python series. We discuss how Mo.net and Python can be integrated in a range of different ways to meet a multitude of potential use cases.
In the last few months, a number of existing clients and start-up insurers have approached us with a view to developing game-changing bulk annuity offerings. Based on our recent discussions, there appear to be three primary challenges…