AI in Financial Forecasting: Applications & Benefits for CFOs

Financial forecasting allows your organization to stay ahead of the competition. While this process has historically been labor-intensive, this is changing with AI. AI-powered solutions are allowing finance teams to go from a pile of data to a finished forecast more quickly, while creating more comprehensive forecasts, often with multiple potential scenarios.

But not all AI tools are created equal, and there are some hurdles to cross before implementing them. 

Here’s what finance leaders can get from implementing AI in their financial forecasting. 

What is AI for financial forecasting?

“AI” is a broad term, covering a range of tools and technologies. In the context of financial forecasting, AI tools typically enhance your finance team’s ability to collect and clean data, analyze it for trends, and use these trends in their forecasts. These tools can often analyze data independently, call up specific data points on request, and chat interfaces to turn natural language requests into reports and dashboards.

This is achieved through a broad variety of AI technologies, including:

• Machine learning: This technology allows AI models to learn from large sets of data without needing instructions, continually improving on specific tasks. In financial forecasting, machine learning could allow an AI tool to better understand your organization’s expenses after being trained on years of budgets.
• Natural language processing: This allows AI tools to better understand human language by being trained on examples. They can then be used to analyze written language, generate voice-overs, and even detect the meaning of certain texts.
• Predictive modeling: By being fed historical data, AI tools can create predictive models (like forecasts) that take existing trends into account. This can dramatically accelerate your own forecasting.
• Generative AI: Fed data like images, written text, and more, this technology gives an AI tool the ability to generate its own content. Usually, this is done by responding to user prompts.
• Conservational AI: Conversation tools like ChatGPT rely on other technologies, like machine learning, while giving users an interface that allows users to enter natural language prompts to get a response based on the tool’s data.
• Large language models: This technology answers prompts by making highly accurate guesses about what the prompts require, based on the database it was trained on. 

AI-powered forecasting vs traditional methods

There’s one key similarity between AI-powered forecasting and more traditional methods. AI tools, just like the people who use them, can learn from your data over time, becoming more efficient. This puts them a step above traditional forecasting tools that don’t rely on AI.

Deploying AI in forecasting allows finance teams to use more data without necessarily needing to dig through it themselves. When built into existing forecasting tools or FP&A software like Prophix One, AI gives you superior data analysis and trend detection while integrating seamlessly with the features you already use. That leads to better forecasts, dashboards, and more.  

Additionally, when you train AI tools on your own data, you get better outcomes than when you rely on general AI tools using general data. Your data will be safer, too.

Applications of AI in financial forecasting

AI can deliver outsized value in your forecasting workflows, but only when deployed intentionally. Simply spinning up ChatGPT and asking it questions about your forecasts can help you save some time on repetitive tasks, but it’s not quite the same as using dedicated tools. Here are just a few ways AI tools can make a difference in your forecasts. 

Automation

Forecasting is full of time-intensive manual tasks, like collecting and cleaning data from multiple sources, as well as scrolling through dozens of financial reports to track down that one elusive expense. AI tools like Prophix One can automatically centralize financial data as well as serve up specific data points. 

Scenario planning

AI can analyze your existing financial data and produce multiple scenarios in a fraction of the time your finance team can. This saves crucial time you can then use to analyze these scenarios or launch new initiatives from them. 

Revenue and cash flow projections

Manually estimating revenue and cash flow projects requires going through tons of data. AI can automatically do this for you, producing projections you can then use in other workflows without having to create them yourself. 

Expense management

Tracking, categorizing, approving, and reporting on expenses creates a significant workload if handled manually. That’s why many finance tools already give finance teams ways to automate and streamline this process. AI raises this to another level, allowing your tools to learn about your organization’s expense trends over time, getting better at automatically categorizing and approving expenses. 

Variance analysis and driver-based forecasting

Accurately detecting the factors leading to variance and their weight requires hours of data analysis. Properly basing your forecasts around them can be time-consuming, as well. AI tools can crunch through more data, more quickly, meaning you can identify variance more efficiently. 

AI-powered insights

AI insights refer to conclusions, opinions, and trends that AI tools generate based on the data you give them. These can be essential in brainstorming factors that might affect your forecasts, correctly identifying trends, and turning complex reports into simpler insights. 

Benefits of AI in financial forecasting 

AI tools come with major benefits for just about any workflow, and this is also true in financial forecasting. Here’s what you have to look forward to when implementing AI tools: 

• Increased accuracy: When combined with human oversight, AI tools allow finance teams to analyze data more efficiently and prepare more accurate reports.
• Improved risk management: Fully calculating the potential risk of an initiative or financial strategy can be difficult. AI helps build a more holistic picture of these risks.
• Enhanced productivity: By automating routine tasks and processing data, AI tools can free up more time for your finance teams, allowing them to get more done.
• Real-time insights: Asking a human finance team to provide real-time insights for every stakeholder isn’t scalable. But with AI, it can be.
• Cost efficiency: While doubling your finance team might be financially feasible, adding an AI tool to your stack can be more affordable while still allowing for a massive performance boost.
• More data sources and more comprehensive forecasts: AI tools can crawl through more data sources than your finance team in less time, giving them a more holistic view of your organization’s financials, leading to the creation of more robust forecasts.

These benefits create a massive impact in all sorts of financial processes, but you’ll see this chain in reaction in financial forecasting above all. That’s because finance teams that learn to augment their work with AI can better anticipate risks, optimize their organization’s resource allocation, and respond more quickly to market changes. That leads to better financial planning and a more effective overall strategy. 
 

How to implement AI forecasting tools

While AI forecasting tools can lead to noticeable improvements in your forecasting workflows, they need to be implemented the right way. Here are some essential aspects of implementing AI tools you should keep in mind. 

Define clear objectives

Before implementing any tool, you need specific, measurable goals. This is no different with AI. Are you primarily concerned with improving the accuracy of your forecasts? Will your main metric be the time saved by finance teams? Or do you want to identify variables and business drivers more effectively? 

Select the right AI tools

Not all AI tools are created equal. Some are too general for your needs, while others aren’t quite feature-rich enough. A dedicated FP&A tool like Prophix One, with built-in AI features, is usually an ideal choice. 

Integrating AI with existing systems

When you deploy an AI tool, you should ensure it works effectively with your existing tool stack. Otherwise, you’ll spend more time and budget on sourcing and setting up software integration platforms than you’ll gain from using AI in the first place.  

Balance AI-driven insights with human expertise

AI isn’t a replacement for your finance team. It can give them access to more insights, more quickly, but it will never know your organization as well as the people who work there. Human team members should always be involved in your forecasting processes. 

Ensuring data quality in AI forecasting 

The effectiveness of an AI tool’s output depends on the quality of the data you feed it. Unlike humans, AI can’t differentiate between good data and bad data, adjusting its approach accordingly. AI needs accurate data—and human oversight—in order to work effectively. Here are some data quality measures you can put in place to give your AI tools the best data possible. 

• Robust data management protocols: Standardizing the way you collect, process, and clean data across data sources and departments can prevent issues that would require lengthy audits in the future.
• Regular data audits and validations: Reviewing existing data can reveal data management processes that require improvement, while validation ensures that more of your data is free of faults.
• Strategies to address data gaps or inconsistencies: Having pre-defined processes for identifying and solving data health issues means your data will get healthier and more robust over time, rather than devolving.
• Strong data security measures and access controls: You don’t necessarily want to restrict access to your data sources, but the more individuals have access to them, the more likely they are to introduce errors.
• Ongoing staff training and data literacy initiatives: Improving data literacy across your organization can prevent data errors and improve data management protocols. 

Step into the future of finance: Get started with AI forecasting

The right AI tool can completely transform the way your finance teams operate. They can process more data in a fraction of the time it would usually take them, build more comprehensive forecasts, and contribute to a more data-driven business strategy. Even better, it empowers them to make data accessible to more stakeholders, leading to better decisions throughout the organization. 

Frequently asked questions: AI in financial forecasting

How does AI-driven forecasting differ from traditional FP&A tools?

AI-driven forecasting allows finance teams to offload data analysis and other time-intensive tasks to AI tools, allowing them to prepare forecasts more efficiently. Even better, AI-powered tools learn from your data over time, making them more efficient as you use them. 

Can AI improve forecasting and reduce risk?

AI-powered tools can help prevent the errors associated with manual data entry and produce deeper analyses of potential financial risks. 

What financial data is needed to get started with AI forecasting?

Primarily, you need to ensure that any AI tool you deploy can integrate seamlessly with existing data sources, whether that’s spreadsheets or dedicated finance tools. Then, as long as you already have a solid base of data (e.g., financial statements, budgets, P&L reports), you can use AI for forecasting. 

Is AI in financial forecasting secure and compliant for enterprise use?

If you load your company’s financial data into general AI tools like ChatGPT, that data will be used for future training by that model, which anyone can access. When you use enterprise AI-powered tools like Prophix One, your data is kept private and secure. 

Can Prophix One support AI-driven scenario modeling and driver-based forecasting?

Absolutely! Prophix One uses AI insights in scenario modeling and driver-based forecasting, feeding accessible dashboards for finance leaders.