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Demystifying the Role of AI in Finance

June 11, 2020 Evan Webster  
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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?

RG: 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?

RG: 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?

RG: 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?

RG: 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?

RG: 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.

3 Reasons Why the Time is Now for Finance to Adopt AI

If you’ve been in finance for more than a few years, odds are you’ve heard all about the potential value of artificial intelligence (AI), machine learning (ML) and predictive analytics. You’ve heard that when the conditions are right and the technology is ready, AI promises to uncover hidden insights on your business today and help you plan for a successful tomorrow.

As Rishi and Ramy explain, AI and related technologies are ripe for finance adoption for three key reasons:

1. The evolving role of the strategic finance function

It’s hard to read a finance or business publication today without finding a reference to the evolving role of the finance function in modern, agile organizations. If you’re reading this, you already know how strategic finance teams aren’t bookkeepers of yesteryear. They lead your company’s strategic planning efforts, plan for virtually any future scenario and tell the story behind your core financial numbers to plan with confidence for today and tomorrow. 

2. An explosion in company-wide data for finance to analyze

Screenshot of Rishi Grover and Ramy Nassar presenting during Vena Nation 2020.

Today’s strategic finance teams are also tasked with analyzing an ever-increasing volume of data. To tell the story behind your numbers and to get a holistic picture of your business, they need to look not just at financials, but at the larger set of non-financial data from virtually every department in your business. And that larger data set—if it’s not already—is quickly becoming too big for humans to analyze on their own. 

If anything, this explosion of data is a key reason why AI isn’t just ripe for finance adoption, but essential to uncover hidden insights that no individual or team can find on their own.

“AI and machine learning give us the power to uncover signals or patterns buried inside of massive data sets. And we can use those signals to make better, more strategic, more well-timed, forward-looking decisions that are grounded in data insight.” — Ramy Nassar

3. A wealth of readily available tools and resources

For evidence of AI and related tools readily available to today’s strategic office of finance, you need look no further than the world’s favorite business software—Excel. In addition to Google, IBM and others, in recent years, Microsoft has made major and growing investments building AI and ML technologies into Excel and into its data visualization companion, Power BI. 

Excel Ideas, for example, identifies hidden correlations and anomalies in your data to identify what truly drives growth and other changes in your performance metrics. What’s more, Excel Ideas learns from your feedback to provide increasingly valuable insights every time you use it.

“[AI and machine learning] have been around for a very long time. There are lots of companies like Microsoft and others that are creating applications and technologies that are leveraging these underlying models. We don’t need to recreate those.” — Rishi Grover

Join Rishi and Ramy to dive deeper into these trends as they unpack best practices to develop a successful AI strategy—from increasing your organization’s curiosity quotient (CQ) to understanding your data strengths and biases. Learn how AI can help maximize business performance and uncover the (often surprising) factors that drive your business the most. And go one step further to make accurate predictions about the future of your market and business performance. 

“CQ stands for curiosity quotient…and there’s a group of researchers and there are organizations all over the world starting to look at the impact of CQ on the ability to make better strategic decisions.” — Ramy Nassar 

Learn how and why you can make AI work for your finance team today, with key takeaways from Rishi and Ramy including:

  • Diving into the key trends that make AI more valuable to finance today than ever.
  • Knowing the difference between AI and related technologies including ML and deep learning (DL).
  • Understanding how AI can help you distinguish between traditional lagging indicators (FYIs) and data-driven leading indicators (KPIs).
  • Identifying your true business drivers to maximize business performance and other practical AI use cases for the strategic office of finance.
  • Learning about the readily available tools you can use to deploy AI and other emerging technologies today.

Thought Leader Bios

Headshot of Vena CFO Rishi Grover.Rishi Grover: Chief Solutions Architect, Vena Solutions

Rishi focuses on the continuous innovation of Vena’s product offering, delivery and vision, working closely with clients and fellow industry experts. Starting his financial software career in sales engineering, Rishi has since developed a passion for translating AI and other emerging technologies into customer value.

 

Headshot of Ramy Nassar, author of the AI Product Design Handbook.Ramy Nassar: Digital Expert & AI Author


Ramy is an engineer, designer and maker. He leads digital studio 1000 Days Out to help clients, including Apple and Facebook, align emerging technology with their organizational goals and end-user desires. He is a frequent speaker on emerging technologies and the author of the AI Product Design Handbook.

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