About the client
A private investment management firm dealing with international investments across a range of portfolios.
Our client was interested in buying a large asset from a European Government – a portfolio of mortgages valued at around €10 billion.
Their team of business analysts had worked for six months using Microsoft Excel to attempt to understand the value of the asset to inform a bidding process. In particular, our client was interested in understanding how proactive management could be used to increase the value of the asset.
With one week to go, the bid team were still not able to provide a concrete understanding of the asset and what it was worth. This left them ill-equipped to make a decision of this magnitude.
Mango was engaged to take on this challenge with just six days to find a solution. The task: model the potential value of the asset and simulate monthly cash flows over the next 50 years to enable the client to understand how to bid.
Thankfully, our data scientists had experience of cutting edge tools and methods that just weren’t available to our client’s team. A team of six data scientists and two business analysts spent six days pricing and simulating the cash flows using a range of possible analytic approaches using R and Python. The required simulation would take approximately 4.3 years of compute time, so AWS cloud computing was used to parallelise the simulation, reducing the overall compute time to the time frame available.
We completed the task in just six days, enabling us to provide evidence and advise the client that a bid would not be in their best interest. This saved them approximately €10 billion and the ongoing running costs associated with administrating an asset that we proved would not return the expected value.
Our client recognised the impact our analysis had as well as the critical role strategic data analytics played in their decision-making. As a result, our team were requested to package up the tool we developed for the project and provide it to the client so they could run similar analyses for future potential investments.