Photo of Jayna Bundy, Microsoft and Meg Coates.
Microsoft Treasury and Microsoft’s Cortana intelligence team partnered together to build a machine learning forecasting solution for accounts receivable. This addresses a key exposure for the company and exceeded expectations by improving forecasting accuracy and operational efficiency for the team.
Director, Scott Schuler, Group Manager
Sr. Treasury Manager
Founded in 1975, Microsoft is the worldwide leader in software, services and solutions that help people and businesses realise their full potential.
Cortana, how much cash do we need today?
Microsoft has substantial exposure to foreign exchange (FX) risk, owing to its operations in over 150 countries.
It is the job of its treasury team to continuously evaluate and manage this risk. In the past, the team used forecasts generated by a series of Excel spreadsheets, using inputs from disparate groups across the company that needed to be aggregated and rationalised in a very manual and tedious process.
These exposures were then hedged by the team using FX forwards and options, effectively reducing the risk of the forecasted exposure to zero.
However, the uncertainty in these forecasts often led to excessive variance between actual and forecasted FX exposures, resulting in increased P&L volatility in other income (FAS 52).
Past challenges include:
Inconsistent definitions, processes and reports.
80% of analysts’ time spent collecting and compiling data.
Less than 500 subsidiaries that need to be included in consolidation of exposures.
Accuracy – poor forecasting leads to P/L volatility.
Microsoft Treasury and Microsoft’s Cortana Intelligence Suite team partnered together to build a machine learning forecasting solution for accounts receivable. This addresses a key exposure for the company and exceeded expectations by improving forecasting accuracy and operational efficiency for the team.