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<\/a> <\/p>\nTable of Contents<\/strong><\/p>\n <\/a> <\/p>\n According to Gartner<\/a>, 58% of finance functions are using AI in 2024, up 21% since 2023. More than a quarter of companies (28%) use AI for finance analytics, including forecasting. That number is rising fast. They\u2019re using AI for everything from sales and demand forecasting<\/a> to risk assessment to budget forecasting.<\/p>\n Here\u2019s why companies are clamoring to add AI-powered financial forecasting<\/a> to their toolbox.<\/p>\n AI models process data faster than humans \u2014 far faster. This speed saves time and costs from manual forecasts. Companies have reported lower operational costs<\/a> and greater operational efficiency<\/a> after implementing AI for finance.<\/p>\n \u201cOur finance team spends 40% less time with AI forecasting compared to manual work,\u201d reports Chunyang Shen<\/a>, co-founder of Jarsy, Inc.<\/a> \u201cThis saves time and leaves us with more time and effort to make key business decisions instead of doing computations.\u201d<\/p>\n You can use AI to find anomalies and human errors in large datasets like expense reporting and invoices. One study found<\/a> that machine learning models reduce forecasting errors by approximately 30% over traditional statistical approaches.<\/p>\n With better data analysis, AI can create more accurate forecasts. Infosys reports<\/a> that 80% of financial planning and accounting teams are now projecting more often and more accurately with AI tools.<\/p>\n Better, faster forecasts mean companies can make smarter decisions in real-time. AI can alert companies when forecasts change or key performance benchmarks are breached. That means that instead of waiting for monthly or quarterly forecasts, you can take decisive action now to reach your benchmarks.<\/p>\n And how do AI tools impact financial performance? Nearly 60% of companies<\/a> using AI for corporate finance reported growing revenue, with 10% reporting growth of over 10%. Additionally, 31% of the same companies found that AI implementation cut costs, with 7% cutting costs by over 10%.<\/p>\n AI is good at speed, scalability, and pattern identification. But it\u2019s not without limitations. Inaccurate data inputs or not enough baseline data can result in faulty results. Then, there can always be outlier events.<\/p>\n \u201cThe future patterns are very useful and the algorithms can work with real-time data, but AI does not exclude all unexpected factors,\u201d warns Shen. \u201cHuman management is still required for monitoring these factors or market fluctuations.\u201d<\/p>\n <\/a> <\/p>\n All of this is exciting, but before diving in, I want to take a minute to understand how AI in financial forecasting works and how it differs from traditional forecasting.<\/p>\n \u201cHistorically, financial forecasting and analysis were predominantly qualitative, relying on small sample data and human expertise,\u201d writes researcher Olubusola Odeyemi<\/a>. \u201cThe methods employed were largely based on fundamental and technical analyses which involved scrutinizing financial statements and market trends to make predictions about future market behaviors.<\/p>\n \u201cThe advent of AI and machine learning has ushered in a new era, characterized by the processing of vast amounts of data and the application of sophisticated algorithms to uncover deeper insights and patterns,\u201d she explains.<\/p>\n Welcome to the new paradigm \u2014 out with manual processes, and in with predictive intelligence.<\/p>\n So does AI financial forecasting work?<\/strong> In a nutshell, AI models use machine learning<\/a> to analyze inputs from internal and external data sources to create future predictions.<\/p>\n Financial forecasting depends on inputs from historic and external data to produce outputs. AI models process, prioritize, and analyze financial data to help companies predict revenue, cash flow, expenses, and more. Here are the steps.<\/p>\n An AI model collects input from large amounts of data. This starts with your own historical financial data from costs to transaction histories to financial performance. You can also use retrieval-augmented generation (RAG)<\/a> to connect your current sales or accounting software to AI to pull new data in real-time. The models then clean and process the data for analysis.<\/p>\n Some models also consider external data like stock prices, economic indicators, and social media sentiment.<\/p>\n Next, the model uses feature engineering to identify the most important data points, like price trends or seasonality, to make the best predictions.<\/p>\n Based on the goal, AI financial forecasts may use different models. A time series model predicts trends over time like season sales, while deep learning models like LSTM<\/a> can predict stock prices from historical data.<\/p>\n The model learns from historical data, tests, and fine-tunes its model.<\/p>\n Now, we get to the output \u2014 the forecast. Once the model is ready, humans can prompt it to make specific predictions, set it to run at regular intervals, or send alerts if a prediction changes.<\/p>\n I think it\u2019s helpful to see this in action. Here\u2019s an example of how a forecast could look:<\/p>\n\n
Why Use AI for Financial Forecasting?<\/h2>\n
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<\/p>\n1. Better Efficiency<\/h3>\n
2. Fewer Errors<\/h3>\n
3. More Accurate Forecasts<\/h3>\n
4. More Timely, Data-Backed Decisions<\/h3>\n
Limitations of AI in Financial Forecasting<\/h3>\n
How to Use AI for Financial Forecasting<\/h2>\n
1. Data Collection<\/h3>\n
2. Identifying Key Patterns<\/h3>\n
3. Choosing a Model<\/h3>\n
4. Testing and Training<\/h3>\n
5. Forecasting<\/h3>\n