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Demystifying AI for Finance in 5 Q&As

May 13, 2020 |

As more companies are exploring the use of artificial intelligence (AI), machine learning (ML) and predictive analytics to transform their business, understanding the strategic impact they have on driving finance-led business planning forward is not only timely, it’s a key differentiator for business leaders today.

Vena’s chief solutions architect Rishi Grover and renowned AI expert and author Ramy Nassar are no strangers to how AI is making a huge impact on finance and operations teams. As keynote speakers for Vena Nation, they’ll be demystifying AI for FP&A. We sat down with Rishi ahead of Vena Nation to discover why the timing is now for finance to adopt AI and machine learning (ML), what tools you may already have to do it, how to develop a data-driven AI strategy to successfully plan for today and tomorrow and what else we can expect to learn from their must-see Vena Nation keynote.

AI and machine learning have been top-of-mind in finance, technology and business for years. What makes AI more relevant now?

A lot of what makes AI relevant and ready for finance today has to do with the evolution of the finance function itself. Now the leaders of integrated planning and the hub of company performance data, finance has found itself managing a LOT more data than ever before— financial and operational, internal and external—now centralized where it used to be disparate or siloed.

The vast majority of companies have fully adopted cloud technologies to tap into the horsepower required to analyze that data. And many enterprise software vendors already have machine learning (ML) algorithms embedded into their products so customers can take advantage of them.

The volume and diversity of data available to finance combined with the processing power and built-in features of cloud software are precisely what AI and ML need to deliver huge value to the modern finance function. 

You’ve said AI can help CFOs address strategic planning challenges and make better, more competitive decisions. How so? Can you give us an example?

Since strategic planning combines both financial and operational data, ML in particular helps companies dive beneath the surface of their data for better analysis, insights and—more importantly—decision making. ML algorithms can scan through massive data sets to identify granular trends, data anomalies, correlations and other patterns that would take inordinate amounts of human effort to uncover. 

For example, companies can already gain insight into products that sell better than others in certain geographies, but ML algorithms can reveal that an increase in one product’s sales is a direct result of a previous set of products purchased. You’ve probably already seen this in the “you might be interested in” section of any e-commerce transaction. 

A classic example of AI is translating images into descriptions for indexing and search purposes. How could this translate into a use case for finance?

Here’s a great example. Microsoft recently released a feature that allows users to take pictures of a financial statement or a data table from a PDF file with their phone and to then translate them into an Excel table for analysis. You’ve probably seen similar tech at work with expense reporting apps. 

What’s the relationship between natural language queries and AI? How does this translate into benefits for the finance function?

Natural language querying (NLQ) is an application of AI technologies that allows people to type or verbalize a question the same way they would to another person—in natural language. In finance, a great example is a CFO overwhelmed by analyst questions on an earnings call. 

Using AI technologies on these calls can provide analysts with suggested charts or graphs that contain answers to their specific questions—or at least a starting point to get to the answer. Technologies like Power BI have embedded this directly into their applications. 

Whether on an analyst call or FP&A team meeting, the benefit of NLQ is that it can produce tables or charts to answer a data-driven question in a fraction of the time it does normally, in the language that’s natural to ask a colleague, and likely what first comes to mind. Typing a question like, “what product line had the highest sales in Territory B last quarter?” might take minutes or even hours to answer manually. With NLQ and AI, you can get a near-immediate answer in real time.

You’ve spoken about Power BI and Excel Ideas as AI tools. What’s the connection? What’s the use case in financial or business planning?

Microsoft is investing heavily in making AI an intuitive, built-in functionality of Excel and Power BI. They’re just two, widely adopted examples of intuitive, readily available AI tools for today’s finance teams and perhaps better still, they’re both free.

Beyond their analytics and reporting functionality, Power BI and Excel both have built-in features that use underlying ML algorithms to uncover new insights in your data sets that you likely wouldn’t discover on your own. You may have some appreciation for Power BI already, but built-in ML features like decomposition trees and adjusting key influencers make it that much more powerful for predictive analytics and other forward-looking needs. 

Excel Ideas is a (perhaps underappreciated) feature of the world’s most popular spreadsheet. Ideas scans large sets of data to identify patterns, trends and anomalies in your data, allowing you also to slice and dice your data or to drill down into the cause and effect of each one. 

Especially for strategic finance teams, Excel Ideas and Power BI allow finance teams to create more meaningful charts, graphs and dashboards, and in turn inform better decision making at the highest levels of their company. 

Hear more from Rishi and Ramy in their Vena Nation presentation—Demystifying AI for FP&A—available on demand. If you haven’t already, register now for your free All Access pass to Vena nation today. 

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