Machine Learning provides tremendous insight regarding market trends & business drivers. These factors include market propensity, consumer demand, economic factors, weather, & transportation costs. Many companies take these variables into consideration but provide limited or time-consuming analysis.  This process limits corporate agility. 

In the video, Asif Khan, Global FP&A Lead at PayU, shares 5 steps of implementing ML for fore

Takeshi Murakami, Group Controller at Microsoft, shares an interesting case study on leveraging AI/ML in decision-making. Microsoft Finance enhanced forecast accuracy by using ML instead of the traditional bottom-up process.

FP&A teams are using AI to drive step changes in business performance, pushing their influence beyond their traditional areas of analyses.

The pressure of globalization and agile decision-making requires companies to improve their business modeling.  They must integrate big data in real-time, synthesize that data to identify causal relationships and value-drivers, and ultimately use the findings to make high-impact business decisions.

The democratization of technologies is underway. Tools like machine learning (ML), which were confined to universities, hedge funds or investment banks just until a decade ago, are now finding their way into industry-wide applications. The finance function is set to reap the benefits of this democratization wave.