AI & Machine Learning: Transforming FP&A

AI and machine learning are reshaping finance—slashing the time it takes to plan, report, and forecast, and allowing finance teams to spend more time on sharing insights rather than trading spreadsheets.

advances in financial machine learning

AI is revolutionising the world of work—and the finance function is no exception. Consider Bergzeit, a fast-growing online sports retailer based in Germany.

Bergzeit once heavily relied on a spreadsheet-based budgeting and planning process. During its yearly planning, the finance team spent countless hours shuffling spreadsheets and consolidating their data into a single version. By the time they were done, their information was often dated and sometimes inaccurate.

After shifting to an AI-enhanced, cloud-based budgeting and forecasting tool, the teams now enjoy “a full view of our finances in one place, supporting the insights we need to make better decisions,” says Thorsten Fritz, team lead of controlling. 

Bergzeit’s finance team not only forecasts multiple times a year, but they do so faster and more accurately.

With AI and machine learning (ML), finance teams across the globe are becoming more efficient and effective, optimising their processes and methods. AI in FP&A automates repetitive work, standardises process flows, and gets questions answered more quickly.

One particularly exciting upside is that finance teams are now spending more of their valuable time on high-value tasks and strategic decision-making rather than manual, repetitive processes.

Finance leaders are increasingly recognizing AI’s transformative power. Among financial services executives, 86% say AI and ML are required to keep their business competitive, and two-thirds say using these technologies has increased productivity and operational efficiencies.  

Technology promises to have a more transformative effect on the finance department in the coming 10 years than in the previous 50.

That includes all aspects of the finance office, including accounting, planning and analytics, budgeting, and closing. By the late 2020s, PwC estimates that AI could contribute as much as $15 trillion to the economy.

To help finance teams leverage the profound advances in financial machine learning, this article will cover applications of AI in FP&A, the role of AI-driven software in financial forecasting, and what it means for future financial analysts.


The Power of AI in Financial Planning and Analysis

Finance faces a nagging problem: outdated information. Because finance traditionally makes decisions based on data analyses from the previous week, month, or quarter, leaders have sometimes lacked real-time data, making it tough to react to changing conditions.  

AI for FP&A has vast and powerful applications, with benefits ranging from real-time monitoring to greater compliance. Other benefits AI brings to the finance function include

  • Processing high-volume transactions. A mountain of data is no problem for AI. The technology can quickly analyse and learn from large data sets. 

  • Pattern spotting. AI can recognise patterns in data and monitor cash flows, flagging anomalies when it finds any data outside the norm. And with feedback, AI gets smarter, so its accuracy keeps improving.

  • Summarization. Generative AI, in particular, can be used for asset summary analysis and sentiment analysis. An AI text analyser tool, for instance, can process long and detailed financial articles and produce snippets summarising the top takeaways. 

  • Freeing time for human judgement. By using AI to streamline labour-intensive, manual tasks such as travel and expense management, finance teams can devote more time to decision-making and strategic support. For example, rather than spending long hours gathering and reconciling information throughout the close period, AI can pull information and manage exceptions.

With financial machine learning doing what machines do best, people can do what they do best.

“AI is not going to replace CFOs. But CFOs who use AI will replace those who don’t.”


Erik Brynjolfsson, Professor am Stanford Digital Economy Lab und Mitbegründer von Workhelix, Inc.

AI’s Mastery in Forecasting

These days, FP&A teams must always be planning. While finance teams aren’t expected to fully predict the future, today’s business leaders expect finance to prepare the organisation for multiple potential futures.

As the leading tool for predictive analysis, AI has changed how finance will forecast. Rather than operating from a fixed plan created at the start of the year, finance is shifting to an ongoing, forward-looking process of modern, continuous planning.

Using AI, financial forecasting enables planning for multiple shifting scenarios. For example, if an M&A opportunity pops up—even if there’s no sign of one in the present—organisations can create plans to seize these opportunities.   

Planning with AI tools allows FP&A to visualise and explore these what-if scenarios, instead of making financial commitments based on estimations or assumptions. And with greater automation, planning cycles become shorter so that planning, analysis, and execution can happen continually and concurrently.  

When organisations have a better idea of likely future scenarios, and can devise improved plans to react to them, they can boost profits, growth, and employee engagement.


Bridging Planning and Analysis with Machine Learning

Financial analysis focuses on present conditions, forecasting future predictions. The bridge between the two? Planning. AI in FP&A can help build that bridge—connecting analysis of the company’s current state with its future aspirations.  

Here’s how: AI can help finance teams consider any possibilities that could affect company performance—and help decide how to adjust. For instance, an exposure analysis tool can generate a list of assets susceptible to certain market exposures. The tool could reveal how a massive event such as Brexit might affect different asset classes—so finance can understand the portfolio’s risk exposure to an event of that magnitude.

In the case of a Brexit-like event, FP&A could craft practical, actional roadmaps to react to outcomes such as supply chain snarls, rising inflation, and labour shortages. These scenario models would enable Leaders need to make informed decisions about how best to not only weather disruptive events but also potentially capitalise on them.  


Streamlined Financial Reporting with AI

Today’s organisations aren’t hurting for data—just the opposite. And while data can be powerful, information overload can delay decision-making and result in missed opportunities.  

If finance teams don’t have the time or capacity to assess data—much less to create timely, accurate, and easy-to-digest reports based on it—it’s not very useful. And it may be why less than half of accounting and reporting teams feel they effectively meet stakeholder needs.

AI isn’t fazed by mountains of data.

Using natural language generation (NLG), AI can quickly process data from numerous sources, offer useful insights, and collate and visualise the information in digestible forms for more concise and effective reports. With AI, finance can gain insights through asset summary generation, text summary creation, report translation, and sentiment analysis.

AI also automates data collection from multiple sources. For example, AI tools can provide accurate, insightful summaries of news articles so FP&A teams can compare financial projections from various sources.

Because AI improves the quality of data and removes some of the human burden in reporting, business leaders don’t have to wait for the finance team to reconcile data and assemble reports before they can take action. They can access self-service dashboards, reports, and actionable information themselves.

With greater depth and breadth of data, finance can surface deeper, more meaningful insights, putting the right data into decision-makers' hands. 


Automation: The Future of FP&A with AI

The future of FP&A is one where automation drives greater efficiency and cost savings. For example, Robotic process automation (RPA) bots can automatically perform rule-based, recurring tasks without human intervention. Say, quickly sorting customer complaints into common categories or pulling information from timesheets to speed up payroll processing.

According to Gartner, a single bot can displace as much as 30 times the work of a single full-time employee. Gartner reports that RP&A technology typically costs one-third of an offshore employee and just one-fifth of an onshore employee. These metrics help explain why 80% of finance leaders either have implemented or are planning to implement RPA.

RPA excels at structured and predictable finance tasks that involve pulling information from one system and inputting it into another. Rather than managing multiple spreadsheets and untold numbers of cells within spreadsheets, finance teams can spend time on more engaging work such as financial forecasting.


Embrace the Future of FP&A: Harness the Power of AI Today

Finance leaders have high hopes for the future of AI in FP&A: 71% anticipate substantial adoption by the end of the decade. But they’ll have a good bit of ground to make up. Almost three-quarters of the finance function have no experience using AI and ML.

To achieve the adoption that leaders expect for tomorrow, finance must begin harnessing the power of AI today. Savvy leaders understand this reality—and are hiring for it. When vetting new hires, 57% of CFOs are prioritising people who can leverage AI and ML technologies.

The sooner finance teams embrace AI for FP&A, the sooner they’ll start reaping its rewards—better and faster decision-making, more accurate forecasting, and boosted ROI.  

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