What is financial forecasting?

It helps businesses make informed decisions regarding hiring, investments, operations, budgets and other aspects of financial planning.

In practice, financial forecasting is typically based on a combination of historical data, market trends and expert opinions. Financial analysts use these insights to create pro forma (projected) financial statements that predict future sales, profitability, cash expenditures and the overall financial position of a business.

Financial forecasting is a fundamental tool for strategic planning. It helps businesses face new challenges, seize opportunities, manage risk and improve decision-making. It also provides critical financial data for business stakeholders, such as lenders, investors and business partners.

Businesses typically use financial forecasts to achieve the following objectives:

• Make informed decisions: Financial projections help businesses decide how to allocate resources in areas like payroll, staffing, inventory and production.

• Set goals: Accurate forecasts for future financial performance help businesses set realistic goals, such as target growth rates based on the availability of working capital.

• Plan budgets: Finance teams rely on their company’s financial forecasts for revenues, expenses and cash flows to set spending limits across departments and projects.

• Manage risk: Businesses use financial forecasting to plan for different scenarios that might pose financial risks, such as a market downturn, a rise in operating costs or supply chain disruptions.

• Attract investors: By projecting future revenues and profits, financial forecasting helps determine the valuation of a business. Investors often use this information as the basis for investing in a company.

• Monitor business performance: Businesses often compare their actual financial metrics against forecasts to track progress or identify problems and adjust their strategies.

There are four key elements for creating an integrated financial forecast:

1. Sales forecasting
2. Income forecasting
3. Cash flow forecasting
4. Balance sheet forecasting

Each of these elements acts as a building block that connects with the others to form a projection of how a business might perform in the future.

Sales forecasting is the foundation for all other components of financial forecasting. It is the process of predicting what a company is likely to sell over a future time frame, typically measured in weeks, months or quarters.

It estimates sales revenue from deals already in progress or expected to enter the sales pipeline.

When sales projections are in place, income forecasting can begin. This process measures all expected revenues along with expenses like operating costs, cost of goods sold, taxes and interest payments.

This information is then used to create a pro forma income statement, which shows a company’s projected future net income or profits.

The estimates of future sales and net income are then used to predict how and when cash will enter and leave a business.

Two common examples of cash flow are when a customer makes a payment (an inflow) and when a business pays it employees (an outflow). A pro forma cash flow statement projects these receipts and payments across a business for a specific period of time.

Predictions from the sales, income and cash flow forecasts are combined to create a pro forma balance sheet. This document is a high-level overview of a company’s future assets, liabilities and equity.

For example, it might include product inventory (an asset), money owed to vendors (a liability) and common stock (equity). It offers a detailed view of a company’s projected financial position.

The two primary types of financial forecasting are quantitative and qualitative. Businesses commonly use a combination of both forecasting methods.

Quantitative forecasts use historical data to predict what is likely to happen in the future. For example, sales figures from the last quarter might be used to estimate revenue for the next quarter. They rely heavily on statistics and mathematical models to project how trends and patterns will affect future business performance.

The following are common quantitative techniques:

• Straight line: This simple technique assumes that a company’s historical growth rate will continue on the same path, like a straight line. For example, if a business had a 4% growth rate the last 2 years, the straight-line technique would predict 4% growth again the next year. It’s considered effective for stable markets, but less reliable for markets with frequent changes or volatility.

• Moving average: Unlike a straight-line trend, the moving average technique smooths out fluctuations in business data over time. It does this process by averaging the results of past periods and by using that average to project future results. For example, it might estimate next month’s stock price by using the average stock price calculated from the previous five months.

• Linear regression: This technique uses regression analysis to understand the relationship between an independent variable and a dependent variable. For example, it might show how advertising expenditure (independent variable) relates to monthly sales (dependent variable). This approach can help a business predict future sales revenue based on expected ad spending.

Unlike the quantitative method, which relies on statistics, qualitative forecasting uses human judgment and opinions as the basis for its predictions. It is helpful in situations where no historical data is available, such as the launch of a new product or the creation of a startup company.

Common qualitative techniques include the techniques outlined here:

• Market research: Consumer surveys and focus groups along with competitive research are common tactics businesses use to conduct market research. The data they collect can help them anticipate future market conditions, such as consumer behavior, demand, pricing preferences and competitors’ strategies.

• Expert opinion: This technique relies on the knowledge of experts both inside and outside a business, such as company executives, sales teams, financial analysts and industry analysts. Opinions are typically collected through surveys, interviews or committees and then combined to build forecasts.

• Delphi method: The Delphi method is a system designed to increase the forecast accuracy of expert opinions. It uses multiple rounds of gathering, analyzing and refining opinions from experts to gradually build a consensus. Because it requires repeated feedback, it is often more expensive and time-consuming than other qualitative methods.

Financial forecasting predicts future financial outcomes. Financial modeling helps businesses guide strategy based on those predictions.

In other words, forecasts provide a baseline for mathematical models (often in Excel) that analyze and predict the result of different scenarios. For example, if a financial forecast predicts next month’s revenue, a financial model can project how a price increase would affect that figure.

Financial forecasting and budgeting are closely related but different processes. Budgets typically provide a static roadmap of how a business allocates expected revenues and resources during a fiscal year.

The financial forecasting process is more dynamic, predicting future outcomes across both short-term and long-term time frames. Financial forecasts for revenue, cash flow and expenses are essential for building realistic and reliable budgets.

Many businesses are now using AI-powered financial forecasting tools to analyze past performance and predict future outcomes.

According to an IBM Institute of Business Value survey of 300 CFOs, 58% say that they are now using traditional artificial intelligence (AI) for forecasting and modeling. Meanwhile, 42% report that they plan to use generative AI for forecasting and modeling in the future.

The benefits of using AI for financial forecasting include these key benefits:

• Real-time forecasting: AI forecasting models can analyze internal and external data sources for real-time predictions based on the latest market changes.

• Automated reporting: AI can automate the reporting of financial forecasts with natural language prompts, reducing reporting time from days to minutes.

• Accurate predictions: AI can uncover patterns, trends and outliers in vast amounts of data that would be difficult or time-consuming to discover with traditional methods.

• Risk detection: AI can provide early warnings of data patterns that might pose financial risks, such as fraudulent transactions or supply chain disruptions.