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Zach McKeown | Jun 29, 2023

Steering FP&A Performance with ML-Enabled Analytics

In today’s digital era, Financial Planning & Analysis (FP&A) teams are inundated with vast amounts of data.  This data holds invaluable insights that, if harnessed effectively, drive significant improvements in organizational performance.  In that sense, machine learning (ML)-enabled analytics is an emerging powerful tool that helps organizations make sense of data, identify patterns and make informed decisions to steer performance in the right direction.  This blog post explores the key benefits of ML-enabled analytics and how it’s revolutionizing organizational performance management.

Specifically, we explore the transformative potential of ML-enabled analytics and how FP&A teams can harness its power to drive their organizations’ financial success.

The Power of ML-Enabled Analytics

Incorporating ML-enabled analytics into the FP&A toolkit is no longer a luxury but a necessity in today’s data-driven world.  By leveraging ML algorithms, FP&A teams can enhance financial forecasting, improve operational efficiency, optimize pricing strategies and mitigate financial risks.  The ability to leverage the resulting data-driven insights empowers CFOs to make informed decisions, drive financial performance and deliver sustainable growth.

Those benefits emphasize how machine learning and advanced analytics have emerged as powerful tools for FP&A teams, offering deeper insights into financial data and enabling predictive and prescriptive analytics.  By leveraging ML algorithms, FP&A teams can analyze vast amounts of data to uncover patterns, detect anomalies and generate accurate forecasts. ML-enabled analytics ultimately helps FP&A teams in the following five ways.

1. Enhancing Financial Forecasting and Planning

One of the primary responsibilities of FP&A is to develop robust financial forecasts and plans.  Traditional forecasting methods employed by FP&A often rely on historical data and assumptions, leading to inaccuracies and limited predictive capabilities.  ML-enabled analytics revolutionize this process by incorporating multiple variables and complex data relationships, empowering FP&A to make accurate predictions and projections (see Figure 1).

Figure 1:  Sensible ML Enhanced Financial Forecasting and Planning Overview

By leveraging ML algorithms, FP&A can analyze historical financial data alongside external factors such as market trends, customer behavior and economic indicators.  These algorithms can identify hidden patterns, uncover non-linear relationships and generate more accurate forecasts.  As a result, FP&A can make data-driven decisions, optimize resource allocation and mitigate financial risks more effectively.

2. Employing Scenario Modeling and Sensitivity Analysis

ML-enabled analytics can generate scenario models and perform sensitivity analysis, allowing FP&A to evaluate how various business decisions and external factors can impact financial performance.  Using such evaluations, FP&A teams can make strategic choices and develop contingency plans to mitigate risks and capitalize on opportunities.

Advances in AI and ML have especially enhanced scenario planning by allowing Finance to make more accurate and reliable forecasts.  With AI and ML, FP&A teams can analyze vast amounts of data and identify complex patterns and relationships between different factors.  Such analysis can enable organizations to develop more sophisticated and accurate forecasts that reflect current market conditions and emerging trends.

By incorporating AI and ML forecasting into scenario planning, businesses can therefore create more realistic and useful scenarios, helping organizations make better-informed decisions and stay ahead of the curve (see Figure 2).

Figure 2:  Scenario Planning Process

3. Improving Operational Efficiency

ML-enabled analytics can significantly enhance operational efficiency via automating repetitive tasks, minimizing errors and identifying areas for improvement. More specifically, FP&A can leverage ML algorithms to streamline financial processes such as budgeting, variance analysis and financial reporting.

For example, ML algorithms can analyze large volumes of financial data to identify anomalies, detect fraud and flag potential risks in real time.  By automating these processes, FP&A can save valuable time, enhance accuracy and focus on value-added activities (e.g., strategic planning and analysis).

4. Optimizing Pricing and Revenue Management

Pricing and revenue management are critical aspects of financial performance, especially for businesses operating in highly competitive markets.  ML-enabled analytics can help FP&A optimize pricing strategies and revenue generation.

By analyzing market dynamics, customer behavior, competitor pricing and historical sales data, ML algorithms can identify optimal pricing levels, demand patterns and customer segments.  FP&A can then leverage these insights to develop dynamic pricing models, implement personalized pricing strategies and maximize revenue – all while ensuring competitiveness.

5. Mitigating Financial Risks

In an uncertain business landscape, FP&A must proactively identify and mitigate financial risks.  ML-enabled analytics provide powerful risk management tools, empowering FP&A teams to identify potential risks, predict outcomes and take preventive measures (see Figure 3).

Figure 3:  Sensible ML Workspace to Mitigate Risks to Performance

By analyzing historical and real-time data, ML algorithms can identify early warning signals for financial risks, such as liquidity issues, credit defaults and market volatility.  FP&A can then leverage these insights to develop risk mitigation strategies, establish contingency plans and make informed decisions to protect the organization’s financial health.

Sensible ML Makes Forecasting Easy

Sensible ML makes forecasting easy by breaking down the barriers that have traditionally held back Finance and Operations teams and others from embracing ML within core planning processes.  While ML has powerful potential to help scale work like never before, organizations face several challenges when using traditional machine learning. Figure 4 depicts some of the biggest traditional ML challenges.

Figure 4:  Sensible ML Solves for Traditional ML Challenges

Sensible Use Cases Foster Success

Sensible ML enables organizations to more quickly and accurately foster success with the following use cases (see Figure 5):

  • To support strategic planning processes, Sensible ML makes it easier to create the 3- or 5-year (or longer) forecasts often needed to provide greater clarity of vision and a roadmap to support the intent of the company, identify strategic choices, ensure clear direction and drive competitive growth.
  • To support Annual Operating Planning (AOP) or forecasting processes, Sensible ML can help lines of business better translate “top-down” financial goals into granular, “bottom-up” monthly plans across product categories, sales channels and customers – which can result in hundreds of forecasts.
  • To support daily or weekly Demand Planning and/or Sales & Operations Planning (S&OP), Sensible ML can help demand planners, business analysts and/or Finance business partners create granular-, product- and location-level forecasts aimed at guiding tactical staffing, procurement, logistics and inventory management decisions.
  • For revenue expenses or workforce planning where monthly forecasts per target (e.g., 60 data points per forecast target) are required for top-down planning, Sensible ML can create predictive/statistical forecasts.
  • For more granular, bottom-up forecasting by customer, product by location and/or S&OP where organizations can share hundreds of data points per target, Sensible ML can create weekly or daily forecasts that account for specific intuition from the business analysis on impacts such as holidays, weather, pricing changes, competitive impacts or any time-based intuition.
Figure 5:  Sensible ML Use Case Matrix

Conclusion

As the role of FP&A continues to evolve, embracing ML-enabled analytics becomes crucial for steering performance and driving organizational success.  FP&A can leverage the power of ML algorithms to extract valuable insights from vast amounts of financial data, enhance forecasting accuracy, proactively identify risks, optimize costs and make informed decisions.  In those ways, the integration of ML into Finance functions enables FP&A to become a strategic partner to business leaders, providing the organization with the tools to navigate complex challenges, drive growth and create long-term value for organizations.

Learn More

To learn more about how FP&A teams are moving beyond the AI hype, stay tuned for additional posts from our Sensible ML blog series or download our white paper here.

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