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The Intersection of AI and FP&A: A Current Overview

March 12, 2024

Artificial Intelligence (AI) is an important topic, as its use across various industries continues to evolve. While it isn’t exactly new technology, what has significantly changed is its exponential growth and adoption. The introduction of ChatGPT and DALL-E has made AI more accessible, exciting, and available to the public.

The finance sector is not historically adventure-seeking, but contrary to what you might think—this sector has actually accelerated AI development in the last few years. The determination of many finance companies to automate menial tasks, detect anomalies, and prevent fraudulent transactions powered the research, development, and eventual widespread adoption of AI.

Although finance, in general, continues to rely on human intervention and judgment, the penetration of AI in finance is not at all surprising. Most finance roles involve collecting data, combining data sources, and analyzing data. The operational and strategic decisions recommended by finance to senior management come from data. Taken in the context of Financial Planning & Analysis (FP&A), AI has started disrupting and upgrading the way FP&A professionals work. But before we go too deep into the state of AI in FP&A, let us first look at the differences between Machine Learning (ML), AI, and the concept of Predictive Analytics.

Machine Learning describes a subset of artificial intelligence; the term came into existence in the 1970s. ML is distinguished by a machine or program that is fed and trained on existing data, enabling it to then find patterns, make predictions, or perform tasks when it encounters data it has never seen before. Examples of machine learning applications today include recommendations of products or services, fraud detection, and self-driving cars.

Artificial Intelligence is a field of computer science that aims to create systems capable of performing tasks that would normally require human intelligence. Generative AI is an AI technology that broadly describes ML systems capable of generating text, images, code, or other types of content, often in response to a prompt entered by a user. Generative AI automates the creation of new content that did not previously exist.

Predictive Analytics is a branch of advanced analytics that uses historical data, statistical modeling, data mining techniques, and machine learning to make predictions about future outcomes. It is an advanced form of analytics that helps to answer questions surrounding what might happen next. Machine learning is a key component of predictive analytics. Predictive Analytics often uses ML algorithms to analyze datasets and identify trends or patterns that are useful in making predictions. It requires historical data to train models and apply those to new and unseen data to come up with forecasts or predictions.

AI Today in FP&A

After working with several finance departments over the years, what I’ve learned is that finance professionals are eager to learn and apply best practices and to improve the way they do things—their constant pursuit of increased efficiency reflects this commitment to productivity. At the same time, I have not met many FP&A professionals who have specifically asked for AI or ML in their search for FP&A solutions. However, if I can answer their questions on how to do things better using AI or ML, they are keen to learn and listen. These days you can use machine learning for developing a driver-based P&L forecast based on historical data, which is a better, faster, and more accurate way of forecasting. You can also use chatbots and predictive analytics to eliminate repetitive and manual work done by highly trained FP&A professionals.

There are still concerns on the use of AI in FP&A. Those concerns focus on security, data, integrity, traceability, and compliance. Many finance professionals think that AI-related data cannot be trusted yet because it isn’t secured. It’s also very difficult to explain and trace should questions arise on how the predictions came about. Finally, there isn’t yet any clear guidance on compliance requirements governing the use of AI.

What is clear is that FP&A will continue to rely on human expertise and judgment. This aligns with the Epicor strategy of delivering AI that augments work to inform human expertise, freeing up resources to focus on strategic issues that help build the business. We see a time coming when AI might significantly reduce the amount of time needed to perform certain repetitive tasks; many companies are already benefiting from this efficiency today.  Within FP&A, Epicor AI will upgrade the way FP&A professionals collaborate, tell their story, and make recommendations.

See how Epicor leverages machine learning in financial forecasting, helping drive the Insight Advantage for your business.