How AI turns financial reporting upside down
Anders Liu-Lindberg
Leading advisor to senior Finance and FP&A leaders on creating impact through business partnering | Interim | VP Finance | Business Finance
In the latest blog in our series of posts on AI in Finance, we look at some practical examples where companies have used AI solutions to change and grow the way they compile, create, and communicate their financial forecasts.
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Finance is ripe for revolution but choosing the right course needs to be done with speed
What if your finance function felt that the effectiveness of the tasks they carried out (say, measuring and managing company performance, or planning and executing strategy) hadn’t seen an improvement in over a decade?
How would you feel about the lack of progress being made in the core tasks carried out by Finance?
Well, that’s what researchers at the Institute for Business Value 2022 C-suite CFO study found. For CFOs and Finance more generally, there is a new expectation being placed on them. They need to be both a conserver of stability and a change agent tasked with leading transformation. So how does one deal with an inherent paradox?
Finance is about delivering value to the business. To do this in the modern era, CFOs need to implement creative thinking: Finance needs new approaches, new tools, new technology, and new skills when it comes to data collection, extraction, visualization, and presentation.
AI will change how work gets done to drive business outcomes
One of the positive side effects of AI adoption for the automation of manual tasks to help streamline financial processes is that it will create better business partnerships. How, I hear you ask but it's not rocket science.
If you’re better informed, you’re going to make better decisions. Better decision-making leads to improved outcomes which in turn creates closer and more fruitful relationships.
The irony of the saying is that “rocket science” is actually simple but not quite as simple as the logic of employing a machine to do something that machines excel at so that humans can do the things that they excel at.
How much is too much and when is enough never enough
Do you have any idea just how much data must be aggregated and assessed by Finance teams? More than can be reasonably done by humans would be my guess.
Armies of people sift through market data on company performance, competitor information, pricing, tracking customer sentiment in real-time, regional performance, operations, and more. That’s just in the average corporate finance function but how can we be expected to get it right all of the time when even the 400+ economists at the Federal Reserve can’t create accurate early reports and forecasts on available data?
Data revisions by top economists are sometimes so big that they “upend our shared understanding of what’s going on” according to James Mackintosh at the Wall Street Journal. For instance, all of the news has been about how the UK is the “sick man of Europe” whereas the truth is that the UK economy is growing faster than Germany. How could this be?
We hold fast to the idea that the numbers are accurate when in fact they are not. If we think about Finance, the confidence with which we report figures is - to the best of our knowledge accurate - but what if it wasn’t, and what if there was an amazing new technology that could redefine what we call “accuracy”?
3 major use cases for AI in financial forecasting and analytics
Nukissiorfiit is Greenland’s green, government-owned energy company. They don’t use any fossil fuels whatsoever. With the goal of giving more accurate financial forecasting and having more effective data, the company set about using machine learning (a subset of AI) and data analytics solutions.
The issue was that it was a very large team of 70 people spending over 1000 hours a year to generate just one, single, forecast annually. 70 people for one forecast! AI can constantly and continuously compare current data with historical data and trends. This allows FP&A teams to consider, say, macroeconomic data, company KPIs and benchmarks, and real-time operational data.
The result was a reduction from 1000 hours and 70 people to a core team of just 9 people, spending only 200 hours creating an accurate monthly forecast, instead of only one.
Bakers are usually up before the sun to create tasty breakfast treats. Well, what if a sudden “black swan” event caused demand to double overnight? That’s exactly what happened to Finnish baker Vaasan.
Predictive analytics, another subset of AI, allowed the company to manage the huge pressure on their supply chain by using relevant data and AI models to make forecasts on when they could operate with reduced capacity to give staff a rest and their suppliers some reprieve, they could also predict energy consumption, costs, and build longer-term plans for their products.
The results were higher customer satisfaction than before, despite a 100% increase in production, and higher profits overall. Not bad.
What if you made millions upon millions of your product every single day but making accurate financial predictions eluded you? You’d never know how fast or hard to drive your factories or where to allocate your resources.
One pharmaceutical company had this exact dilemma and used an AI solution to create an integrated, dynamic, and streamlined financial planning solution.
Instead of basing their forecasts on historical data to estimate future metrics (which is the traditional method), which led them to be slow to react to competition, and lose out on ROI, they used machine learning with a live scenario planning engine, real-time tracking of customers and competitor actions, regulatory impacts, and market dynamics.
This improved forecasting accuracy to 97% and optimized resource allocation through on-demand planning. And, an increase of USD $115 million in profit over two years. Astounding.
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The results of AI integration speak for themselves
According to the same IBV study, AI adopters say they see a between 20-50% increase in overall per quarter forecasting accuracy.
And, as accuracy improves, the associated risk and variance rates go down, so Finance can make smarter, more informed decisions for the company related to staffing levels, production targets, capital expenditures, innovation, ESG, and other areas.
But let’s say simply that your total sales forecasting accuracy went up and your total inventory cost accuracy also went up due to AI tools. It’d be worth it, right?
What do you think about the implementation and use of AI in forecasting? Let us know in the comments below.
This was the eight article in our new series "Demystifying AI in Finance & Accounting". Remember to subscribe to catch the upcoming articles and read the previous ones below.
Don't forget to catch our previous series "Welcome to Finance Function 5.0" below.
Check out our previous series "Rebranding the CFO" below.
Continue reading below for more articles about trends in finance and accounting.
Anders Liu-Lindberg is the co-founder and a partner at Business Partnering Institute and the owner of the largest group dedicated to Finance Business Partnering on LinkedIn with more than 11,000 members. I have ten years of experience as a business partner at the global transport and logistics company Maersk. I am the co-author of the book “Create Value as a Finance Business Partner” and a long-time Finance Blogger on LinkedIn with 300,000+ followers. I am also an advisory board member at Born Capital where I help identify and grow the next big thing in #CFOTech. Finally, I'm a member of the board of directors at PACE - Profitability Analytics Center of Excellence where I support the development of new analytics frameworks that can improve profitability in companies around the world.
QuickBooks Certified Pro-Advisor, XERO Advisor, MSS, M. Com (Accounting), CMA (Final),
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1yCould you recommend any tool?
Helping you with hassle-free Bookkeeping| Director at Beyond Books | Content Creation on Bookkeeping & Finance
1yAbsolutely, Anders!💯 The challenges faced by finance professionals are indeed significant, and the lack of progress in core tasks can be frustrating. However, with AI, finance professionals can focus on implementing creative thinking, exploring new approaches, and leveraging advanced tools and technology for data management and analysis. This innovative approach not only conserves stability but also drives transformative change, making the paradox a realm of possibilities for the finance function.
AI helps in finance reports. It's like a smart assistant, making reports accurate and finding mistakes quickly. AI is a big help in financial reporting. Informative Anders!
Your post is very well-written and informative. I especially appreciate the way you have highlighted the benefits of AI in financial forecasting and analytics, such as reducing time, improving agility, increasing accuracy, upgrading the planning cycle to offer high quality to customers, and beating the competition. I also agree with your point that AI can help Finance teams make smarter, more informed decisions for the company. By improving forecasting accuracy and reducing risk and variance rates, AI can enable Finance to better plan for staffing levels, production targets, capital expenditures, innovation, ESG, and other areas. I believe that AI is a powerful tool that can help Finance teams to improve their performance and value to the business. I encourage Finance teams to explore the possibilities of AI and to consider implementing AI forecasting solutions in their organizations.