Daniel Sousa-Lennox: How Microsoft is using AI to win at forecasts and help their FP&A team
Oct 17, 2023
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Daniel Sousa-Lennox, a data scientist at Microsoft, discusses how the company uses AI for accurate forecasts. They developed FINN, an AI finance tool that has achieved 99% accuracy in revenue forecasting for an Azure product. Sousa-Lennox emphasizes that while AI isn't magic, it has transformed financial forecasting at Microsoft. FINN provides access to 25+ models, reducing forecasting time significantly. The podcast also covers building flexible budget models, improving forecasts with machine learning, the role of AI in data analysis and forecasting, and the curiosity about meeting historical figures like Leonardo da Vinci.
Using AI finance tools like FINN, Microsoft's finance team has achieved 99% accuracy in forecasting revenue for Azure products.
Finance professionals need to embrace technology like generative AI to streamline manual tasks and focus on analysis and insights.
Deep dives
Automating Forecasting with Machine Learning: A Game Changer for FP&A
One of the major challenges faced by finance professionals in the FP&A field is the time-consuming nature of budgeting and forecasting processes. Daniel Sousa-Lennox, a software engineer at Microsoft, shared his experience in using machine learning to automate these processes. By leveraging tools like Finn, a standardized modeling framework, finance professionals can produce machine learning forecasts without the need for coding or technical expertise. These tools handle data pre-processing, feature engineering, model selection, and future forecasting. This automation not only saves time but also increases accuracy, allowing finance teams to focus more on analysis and insights rather than manual tasks. The key takeaway is that finance professionals need to embrace the creative aspects of their role and leverage technology like generative AI to improve productivity and decision-making.
Challenges and Benefits of Implementing Machine Learning in FP&A
While the implementation of machine learning in FP&A offers numerous benefits, there are also challenges that need to be addressed. One of the main challenges is ensuring the quality of data used for forecasting. Finance professionals need to understand the drivers of their business and incorporate relevant data to improve accuracy. Additionally, there is a need for clarity regarding the limitations of machine learning and the importance of iterative processes. Machine learning is not a magical solution, but rather a tool that requires continuous refinement and analysis. On the positive side, the adoption of machine learning in FP&A streamlines tedious tasks, such as data exploration and forecasting, freeing up time for analysts to focus on value-added activities like providing insights and recommendations. By embracing generative AI and other technological advancements, FP&A professionals can enhance their forecasting capabilities and contribute more strategically to their organizations.
The Role of Expertise and Analysis in FP&A
Daniel Sousa-Lennox emphasizes the importance of incorporating domain knowledge and expertise in the FP&A field. Technology, including machine learning, should serve as a supplementary tool to enhance the analysis conducted by finance professionals. While automated tools like Finn can perform complex tasks, they rely on the expertise of the user to provide accurate and meaningful insights. To excel in FP&A, professionals should fight the urge to merely update spreadsheets and focus on deeper analysis and interpretation of data. This involves anticipating requests, providing insights, and offering recommendations to the leadership team. By leveraging their domain knowledge, finance professionals can make a greater impact in their organizations, stand out in their roles, and drive better decision-making.
Embracing Change and Productivity in FP&A
The rise of technologies like generative AI and machine learning is transforming FP&A by increasing productivity and enabling finance professionals to tackle more strategic tasks. However, embracing change can be challenging, as people may fear the unknown or worry about the impact on their roles. It is essential for FP&A professionals to adopt a growth mindset and view these technologies as productivity tools rather than threats to their jobs. The integration of generative AI and other automation tools allows finance professionals to prioritize value-added activities, such as analysis, insights, and recommendations. By leveraging technology, finance professionals can unlock their full potential and enhance their impact within their organizations.
How is Microsoft using AI to reach up to 99% accuracy in some forecasts? In this episode of FP&A Today, Daniel Sousa-Lennox, a data scientist at Microsoft with deep experience of financial analysis, reveals all.
The Microsoft’s finance team – about 5000 people – saw its first foray into machine learning in forecasting. The team developed FINN (or Microsoft Finance Time Series Forecasting Framework) says Sousa-Lennox who sits within the finance organization, providing software engineering and data science expertise. Using machine learning and creating AI finance tools (such as FINN which has been open-sourced) Sousa-Lennox says the Microsoft finance team has replaced burdensome forecasting and Excel heavy lifting. "The tool has helped, for instance, this one team in terms of accuracy to get the revenue that they were trying to forecast for an Azure product (the cloud computing platform run by Microsoft) to 99% accuracy". But Sousa-Lennox adds that it is important to remember that AI is not "magic" and "wont lead to 99% in every single forecast" and the quality of data is paramount.
Nevertheless the results are transforming financial forecasting at the $2trillion company.
FINN alone provides access to 25+ models supporting Microsoft’s daily, weekly, monthly, quarterly, and yearly forecasts, transforming the approach of FP&A.
Sousa-Lennox says: “It takes on average for a forecast used run using our tool, it takes about an hour to an hour and a half, which is significantly less than a week [previously]. It is also an hour and a half where you don’t have to be actively doing anything.”
In this episode Daniel Sousa-Lennox shares:
Similarities and differences between FP&A in US and Panama (and banking vs Microsoft)
Getting an internship to full-time career at Microsoft
How he trained in Data science and technology
How to evangelize finance teams into adopting AI
How Microsoft is transforming its forecasting and FP&A
Despite its success why AI is not a “magic bullet” for finance
The two most powerful lessons in presenting AI to finance teams
The biggest advice for anyone starting in FP&A
Last thing finance thing I Googled ChatGPT’d
My favorite Excel function
Links
Follow Daniel Sousa-Lennox: https://www.linkedin.com/in/dsousalennox/
FINN (Open-sourced by Microsoft): https://microsoft.github.io/finnts/
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