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.
Photo of Jayna Bundy, Microsoft and Meg Coates.
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.
The project was taken on with the dual purpose of solving a tangible internal business problem, and testing the Cortana Intelligence Suite platform in an area of key interest to Microsoft’s customers.
“With the exponential growth in the volume and availability of relevant data, today’s finance organisations face significant challenges in their forecasting processes. It is an honor to receive an Adam Smith Award for the work we do to empower the teams at Microsoft to make critical business decisions.”
The teams worked jointly on obtaining and analysing the historical data, defining the data processing pipeline and cleaning the data for modelling. The machine learning team then applied their techniques to create an automated forecasting solution using the coding language R.
Since the solution was created in Azure Machine Learning it is accessible by web where any user with access can run the forecasting or consume its data without any need for any knowledge of coding in R or a separate installation.
Best practice and innovation
The treasury team has successfully leveraged machine learning solutions to reduce FX volatility, which has helped to increase shareholder value. “Azure Machine Learning has significantly helped improve our hedging forecasts, saving real dollars during a period of volatile currency markets,” says Oystein Harsvik, Director at Microsoft.
This solution has helped improve treasury efficiency, as FX forecasts are now auto-generated using Cortana Intelligence Suite. Accuracy has also improved, as it removes potential human bias from accounts receivable projections and improving overall accuracy. Together these have resulted in cost savings through improved forecasting a reduced FX impact.
Key learning points
Cash flow is the life-blood of all businesses, keeping a healthy circulatory system that ensures cash is available when and where needed is vital. Accordingly, professional cash management is dependent on accurate information collected via a standardised process with instant and easy access to historic and current data. Microsoft’s solution utilises first and third-party apps to collect, store and visualise data from a variety of sources. By combining and linking manually forecasted data with actual bank transactions in one cloud based database, different groups within the organisation are empowered to pick and choose relevant data from a single source for a tailored report and further individualised analysis.