Fast followers with a practical approach to machine learning in particular, stand to gain the most from the advent of artificial intelligence in the Office of Finance. The essential foundations are high data quality, de-centralised storage of financial and operational data and the right people with advanced analytical skills.
This paper provides use cases from the simple to more advanced to facilitate growth and minimize pain points. The use of machine learning collection to improve data quality is pre-requisite to access its other advantages, and it is also important to bring financial and operational data together. The use cases cover forecasting of sales volume, revenue, pipeline and cost, and also delve into contributor analytics and driver-based simulations.
What you’ll learn:
- How to bring financial and operational data together for successful machine learning
- What to specify when implementing machine learning
- The positive outcomes for data storytelling of an integrated and automated forecast