How AI & ML Can Power Advanced Analytics for Corporate Finance


Advanced analytics powered by AI and ML is set to play a significant role in making strategic, data-driven decision-making for large organizations, especially in the new business reality. In this article, Scott Stern, Director of Product Marketing at corporate performance management (CPM) solutions provider OneStream Software explains:

  • Why finance teams need to shift focus from building advanced models to engaging with forecasting models
  • Key differences between predictive analytics and machine learning (ML)
  • How finance leaders can finally increase adoption and ROI on investments into advanced analytics

With the first wave of COVID-19Opens a new window behind us, organizations are now surveying the damage. For those who have survived, they’re wondering what’s next. Will there be a second wave? If so, will it be as devastating as the first, or are we now entering the “new normal” phase of the pandemic?

If your organization survived COVID-19, the answer doesn’t matter. You survived.

To now thrive, you’ll need to get back to business. Let’s not confuse this with implying sales or product demand will return to pre-COVID levels anytime soon. Getting back to business means your organization still needs to plan, analyze, and invest in key initiatives if you want to thrive.

That’s exactly why finance leaders at midsize to large organizations must keep their sights set on finding value from artificial intelligenceOpens a new window (AI) technologies such as machine learningOpens a new window (ML) and predictive analytics. This time, if we want to see adoption in finance go mainstream, we have to think differently.

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Here’s what that looks like.

Finding Your Why

To evaluate the value of advanced analytics, finance teams should ask themselves a simple question: “Do we want to spend time building models or interacting with forecast models?”

Advanced analytics in finance is similar to other corporate performance management (CPM) processes such as planning, financial consolidation and reporting. If finance teams spend all of their time moving data, reconciling data and building reports, they’ll have no time to leverage this data to guide key decisions. By leveraging technology to handle the hard work, finance teams can shift a larger portion of their time to value-driving activities, such as forecasting cash or evaluating key capital investment decisions. In doing so, finance teams are stepping forward, as they have throughout the COVID-19 crisis, to unleash their true potential as strategic business partners.

Creating value from advanced analytics in finance requires a similar approach.

Within finance processes, advanced analytics can play a powerful role in driving collaboration and effective decision-making. Consider the CPM framework (see Figure 1). While there are several versions of this framework, most consistently show the role strategic finance plays in steering organizational performance as a continuous cycle of mapping key strategies into actionable business plans.

Figure1: Corporate Performance Management Framework
Source: OneStream

Despite all the promise of AI, adoption in finance is about 15% according to the Beyond the Hype Market StudyOpens a new window from OneStream Software (see Figure 2). Most finance teams don’t have the skillsets or tools to quickly develop statistically significant and insightful models, integrate them directly into planning and analysis processes – and do it at scale.

Figure 2: OneStream Software’s Beyond the Hype Predictive Analytics & Machine Learning Market Survey
Source: OneStream

Here are a few examples to illustrate the point:

  • When your finance team meets with the board or executive team, do you have any perspective on how your products or services are impacted by external factors?
  • When your finance team sets targets for consolidated company or divisional plans, are your targets based on gut feel, or do they consider historical performance and external factors (e.g., GDP, consumer preferences, or oil prices)?
  • Do your forecast models accommodate the potential impact from competitor actions such as pricing changes or new product additions?
  • During annual planning or forecasting processes, can you work in real-time with sales, operations, and other functions to understand the “why” behind bottom-up forecasting to “test the numbers”?

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Enter Advanced Analytics

Like the increased adoption of cloud-based solutions by finance teams, the adoption of predictive analytics and ML is a matter of when – not if.

With the sheer volume of data available and advancements in CPM software, finance teams now – finally – have the capabilities to interact with advanced analytics and do it at scale. How? Instead of taking on the burden of building models, finance teams can rely on purpose-built software to help them supplement their planning processes with statistically significant, predictive forecasts to compare against manager-driven forecasts that may be biased by the fog of uncertainty.

What’s the best way to get started?

Finance teams should consider advanced analytics as part of a broader framework that includes predictive analytics and ML.

Predictive Analytics: Low Cost & High Value

Predictive analytics provides the power to predict future performance based on applying predictive algorithms to historical data. This is not new, but by automating model creation and deployment directly within CPM processes, predictive modeling is easy to execute for any planner.

This “ease of use” makes predictive models incredibly powerful for finance teams, especially when they can directly leverage predictive models or combine a baseline predictive forecast with specific business initiatives. However, no predictive algorithm is going to predict with 100% accuracy. Human intuition and business acumen still play a role, after all.

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Unlimited Forecasting with Machine Learning (ML)For finance teams who need to go beyond traditional predictive analytics, ML is the answer. ML is where people and technology come together. For example, consider a retailer’s demand forecast for product A in region B within store C. To forecast at this granular level, there are so many other factors at play. How about the weather in region B, for instance? And what if store C built a new parking garage? Or what if a major competitor opens a location across the street or materially changes pricing? Factors like these are all potential features an ML model might consider if your finance team has made the necessary investments in data scientists.

Compared to predictive analytics, ML comes at a higher cost and is harder to scale, but if your organization is committed to the investment, there’s a higher reward in terms of forecast accuracy.

Ultimately, it’s not a question of whether predictive analytics or machine learning is better or worse than the other. The bigger and more important point for finance teams is to know what you’re trying to achieve with advanced analytics and then select the right technique to do so.

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Bringing It All Together

What does this all mean for finance teams? Does it mean that all of today’s decisions will be automated away? No. Should finance teams expect 100% forecast accuracy all of the time? No. Does it mean that all legacy planning processes should stop? Absolutely not.

Here’s a simple truth – advanced analytics offers finance teams a new way to ask “why?” And there’s nothing bad about having an unbiased forecast scenario to help drive dialogue with business partners.

Like with any other new technique or technology, it’s critical to sift through the hype and try to understand “what this means for me.” No solution will offer the perfect forecast or answer every question that CFOs or CEOs ask. However, if we take the one small step forward to evaluate advanced analytics through the lens of being an enabler for finance rather than being yet another burden, we’ll take a giant leap forward into this brave new COVID-19 world. And we just might set up our organizations to thrive too.

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