7 steps to create value with Financial Forecasting - a practical guide

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Do you want to implement Artificial Intelligence (AI) in your financial forecasting? This is a huge leap forward in the accuracy of your forecast, but getting there can seem daunting. We’ve made it easy for you and have broken down the process into 7 steps. Below you will find a practical guide to implementing AI in financial forecasting.

1. Determine the worth of AI for the entire organization

To make a good start you need to start with a broad view. Take time to define what the worth is of implementing AI in financial forecasting for the entire organization. What can colleagues be able to do/see after you implement it and how will work improve? This is an important step to take to ensure that everyone is aligned on why you are investing time and resources.

There are many good reasons to implement AI in finance, both from an internal and an external perspective. Within the company an AI-based forecast reduces stress and uncertainty within the forecasting process. The forecast’s credibility improves drastically which leads to fewer discussions and more data-based decisions. Externally this improved credibility also satisfies investors (firms and individuals) as it is a trustworthy tool.

2. Understand your deltas

To implement AI in a way that leads to the desired results you need to understand your deltas. This means taking time to evaluate where, how, and why the difference between your forecast and reality exists. Research these deltas and define which ones will have the most impact if they are improved. It often helps to arrange the deltas from large to small.

3. Define what you want to know

Once you have defined the deltas that need improvement, you can think about what you really want to know. Think about the level of depth your data analysis needs to create a smaller delta. The deepest level would be the level of the individual customer behavior. But keep in mind that if you have 1000 customers, that would mean that you have 1000 lines to evaluate. So try to think in segments or drivers that can combine valuable insights. This creates a data framework.

4. Start the build of your forecasting tool

Combine all steps you have taken thus far. Use the worth that AI has for your company (step1) to define a goal for which you then choose a delta to improve (step 2) within the data framework (step 3) you have defined. This leads to a few design choices. You can now define what data you are missing and what analysis you need to make to get the right results. Also think about the right tool (excel, python, or other) in which you’ll want to work.

To make all of this combine you’ll need the help of a few people. The data analyst needs to help build and design the actual framework and can help find the right data. The financial expert can interpret the data in context and validate it. It is important to also invite someone from sales, product management, or marketing. This is because these colleagues can tell you whether certain campaigns or actions might have influenced the data.

5. Way of working

During your build of the tool, you are going to probably also think about the way you want to use it. Most companies make their forecasts monthly, quarterly or yearly. The great benefit of an AI-driven forecast is that it is constantly up to date. This means that you need to think about the way you want to use this ‘live’ data. Will you go from a monthly forecast to a rolling forecast? And what timeframe do you want to keep track of? Choose the right way of working that fits your company and improves your forecasting.

6. Test and iterate

Now that you have the setup of the tool and you have decided how you want to work with it, you can test it. Choose a small segment of data and improve with AI using the design you made in steps 1 to 5. Keep testing it throughout different iterations until your delta declines. Keep in mind: the smaller your delta the more accurate your forecast is.

7. Scale up

Have your tests been successful? Is your delta declining and is your way of working giving you more insight into your forecast? Then you are ready to scale up. Expand the tests you did to other segments and slowly build up a forecast that is completely AI-driven.

The other thing you need to scale up is your team. Make sure that all people working with the forecast have a good understanding of how it is built and how AI improves it. Bringing your team to this level ensures that everyone understands the way of working and that the forecast keeps improving.


Do you want to know more about the benefits of using AI in your financial forecast? Or are you curious about the benefits specifically for your company? Get in touch with us to see how we can help you and your company best!

Want to get your finance team AI-ready in just 3 days?

As you have probably realized by now, people are essential in getting your AI-based forecast up and running. Your people need to know what they are doing and feel comfortable working with AI. But often AI courses don’t take into account financial tools. That is why we have designed ‘Financial Forecasting using AI’. It is a course that gets your team working with real AI solutions in finance. The best thing is that it only takes 3 days.

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