What is financial modeling?

Financial modeling is widely used in corporate finance, investment banking, private equity, equity research and consulting. A company might use it to evaluate to introduce a new product or expand into a new market. An investor might use one to estimate what a company is worth before making an acquisition or investment. Lenders use it to assess whether a borrower can meet debt obligations. Models also play a central role in mergers and acquisitions (M&A).

Models are often used to run scenarios and sensitivity analyses so that leaders can see how changes in key variables affect results. The goal is to create a tool that helps in decision-making by forecasting how a business or investment might perform under different scenarios. This approach gives companies, investors and lenders a way to anticipate real-world risks and evaluate potential returns.

Financial analysts build financial models within their corporate finance or financial planning and analysis (FP&A) departments. The financial modeling process begins with accurate historical data. This information helps explain how the business operates, what its main drivers are and how different parts of the company connect.

From there, assumptions are made about future internal factors like sales, customer growth, cost structure and investment plans, as well as external factors like economic conditions, interest rates and regulations. These inputs feed into the projected statements and schedules.

Spreadsheets such as Microsoft Excel are used to link the historical financial statements with the assumptions about the future. The result is a set of projections that estimate future revenues, costs, profits, cash flow and other key metrics, depending on the function of the model and the goals of the modeler.

The most common type of financial model is a three-statement model, which links the income statement, balance sheet and cash flow statement. More advanced models build on this foundation to include valuation, scenario analysis or forecasting under different assumptions.

Strong financial modeling relies on more than technical spreadsheet skills. It requires solid knowledge of accounting, finance and the business itself, as well as sound analysis and good judgement. A good model is accurate, flexible and easier to follow. It shows results but also provides insight into what drives those results.

Many finance professionals improve these skills by taking a financial modeling course that combines theory with practice. With the advent of artificial intelligence (AI) in financial modeling, business schools and training programs are placing greater emphasis on data science, machine learning and generative AI. These efforts aim to prepare future analysts to work alongside advanced systems.³

Financial modeling provides organizations with a structured way to understand their financial analysis and future outlook. Companies operate in environments full of uncertainty, and business decisions often involve money and risk. A financial model organizes data into a framework that shows how different factors interact, which allows leaders to see not just numbers but the relationships and dynamics that drive performance. Without this clarity, decision-making can become fragmented or based on incomplete information.

It also connects strategy with execution. A business plan or strategic vision must eventually be translated into revenues, expenses, cash flows and capital needs to determine whether the underlying business model is financially viable. Financial models bridge this gap by showing whether a plan is feasible and how it affects resources.

They also help estimate the kind of returns a plan can generate. In this way, models support accurate valuation and investment analysis, helping organizations assess whether opportunities are worth pursuing and if pricing is fair.

In day-to-day operations, financial modeling helps organizations manage budgets, allocate resources and maintain liquidity. By predicting revenues, costs and capital needs, models strengthen financial planning and forecasting while also guiding efficient capital allocation. For example, a company often uses models to see how delays in customer payments would affect cash flow or whether taking on debt is sustainable under different interest rate conditions.

Startups often rely on modeling to test and validate their plans before seeking investors. These insights improve risk management by preparing businesses for challenges such as cash shortfalls, market downturns or rising costs, helping safeguard financial health and continuity.

Financial modeling supports communication and accountability. Models offer a common language for executives, investors, lenders and other stakeholders. They make it possible to test and adjust plans transparently, improving communication, aligning expectation and helping to ensure trust.

Financial models support better decision-making by offering a quantitative foundation for evaluating choices. Scenario analysis also strengthens long-term strategic planning. Organizations can evaluate the financial impact of new products, expansion, cost-cutting or major transactions through hands-on scenario planning before execution. Because updates can be made quickly after a solid structure is in place, financial modeling saves time.

Altogether, financial modeling ensures that decisions are data-driven as well as realistic, reliable and efficient.

Financial modeling comes in different forms, each designed for a specific technical purpose or structure. Depending on context, models are used for corporate planning, valuation or project finance initiatives such as infrastructure investments. Often, a standard template is adapted to the company’s needs. Here are descriptions of some common models, including how they are constructed and the roles they play.

This model is the basic building block for most financial models. It links together the income statement, balance sheet and cash flow statement into one framework, capturing assets, liabilities, revenues and expenses in a connected way. Assumptions about revenue, costs and investments flow through all three statements. If something changes, such as sales growth or interest expense, you can clearly trace the effect on profits, cash and the balance sheet.

The DCF model builds on the three-statement model. It projects future free cash flows, which are the funds left after covering operating expenses, taxes and reinvestment. These cash flows represent the money available to investors and are central to valuation.

The projected cash flows are then discounted back to their present value, known as net present value (NPV). This approach involves applying a rate that reflects risk—typically the weighted average cost of capital (WACC)—to estimate the business’s current worth.

These models are built for internal planning, usually over a short to medium term of one to several years. They focus on estimating revenue, costs, capital expenditures and working capital needs. The goal is to help management plan what resources are needed and prepare for likely financial performance, making them a central tool in planning, budgeting and forecasting processes.

Rather than projecting cash flows, the CCA model (sometimes called the multiples model) approach values a business by comparing it with similar public companies (peers). Common valuation multiples include price-to-earnings (P/E), enterprise value to EBITDA or revenue multiples. 

Because it is based on actual market pricing, this method can be applied quickly and is widely used by analysts on Wall Street. But its accuracy depends heavily on choosing the right peer companies and on current market conditions.

A consolidation model is used when a parent company has multiple subsidiaries or business units. It combines the financials of each unit into one set of statements. It also handles issues like intercompany transactions (where subsidiaries do business with each other) so that revenues or expenses are not double counted.

When a private company plans to go public, the IPO model estimates the offering price, the number of shares to be issued and the impact of underwriting and regulatory costs. It also shows how ownership stakes change and accounts for the additional expenses of being a public company, such as reporting and compliance costs. The model helps both the company and potential investors understand the financial picture before the offering, often summarized in PowerPoint presentations.

The LBO model is common in private equity. It’s used to estimate investor returns when acquiring a company primarily with borrowed money. The model includes a detailed schedule of debt repayment and interest, the amount of equity invested and assumptions about the eventual sale of the company (exit). 

Analysts usually measure results through internal rate of return (IRR) and money-on-money multiples. Because heavy debt magnifies results, these models are highly sensitive to factors like interest rates, growth, profit margins and exit value.

The M&A model is used when one company acquires or merges with another. It helps estimate the combined financial results, often shown as pro forma statements and incorporates expected gains or costs from synergies such as expense reductions or new revenue opportunities. It also examines whether the deal is accretive or dilutive, meaning whether per-share results are improved or weakened after the transaction.

These techniques are advanced models used in cases of high uncertainty. Option pricing models determine the value of financial instruments with embedded options, such as convertible debt or stock options. Monte Carlo simulations use random variations in inputs to run thousands of possible future scenarios, creating a range of outcomes. This approach is useful for risk analysis and for projects with uncertain returns.

The SOTP model is used when a company has multiple divisions or lines of business that are valued differently. Each part of the company is valued separately, based on the method most suitable for that unit, such as a DCF or multiples from comparable companies. The values are then added together to show what the whole company might be worth if considered as a collection of separate businesses instead of one large enterprise.

Financial modeling has many applications across business and finance. Its value lies in helping decision-makers understand how different actions, assumptions or market conditions affect financial performance. The most common uses include:

Business planning and forecasting: Companies use financial models to plan for the future. Forecasting revenues, expenses and cash flows help management prepare budgets, allocate resources and set performance targets. Forecasting also makes it possible to compare actual results to expectations and adjust strategy when needed, often working alongside enterprise resource planning (ERP) systems.

Investor communication: Investors, lenders and other stakeholders expect financial clarity. Models provide a clear way to explain assumptions and outcomes, showing how the business expects to grow and how risks are managed. This builds trust and helps secure support for company strategies.

Mergers and acquisitions (M&A): In transactions, financial models are used to assess whether an acquisition or merger makes sense. Models project combined results, estimate synergies and test how different deal structures impact earnings and shareholder value. They also help determine how much a buyer should pay or whether a seller’s asking price is reasonable.

Performance monitoring and decision support: Models help managers and executives evaluate and validate performance by comparing projections to actual results. They also provide a framework for testing strategic choices, such as entering new markets, introducing products or cutting costs. This approach makes decision-making more data-driven and less reliant on intuition alone.

Raising capital: When companies seek funding, financial models demonstrate how much capital is needed, how is used and whether the company can meet repayment obligations. Models are also used to test different financing structures, such as debt versus equity and to show potential investors or lenders how the company expects to perform.

Risk management and scenario analysis: Financial models allow businesses to test “what if” scenarios. For example, what happens if interest rates rise, if sales fall short or if supply costs increase. Running these scenarios helps companies improve risk management and build strategies that are resilient under different conditions.

Valuation of companies or assets: Models are used to estimate the value of a business, a project or a specific asset such as machinery or real estate. Valuation is crucial for investors, acquirers or business owners who want to know what something is worth today based on future cash flows or comparable companies. Methods such as the Discounted Cash Flow (DCF) valuation model or comparable company analysis are standard in this area.

Financial modeling is most useful when it is built on a foundation of accuracy, adaptability and sound fundamentals. Modelers follow best practices to ensure that models serve their purpose over time and remain understandable to different users. Here are some of the core modeling techniques and practices that organizations follow:

Errors can undermine trust in a model. Accuracy can be supported by reconciling projections with historical data, incorporating built-in error checks and performing sensitivity analysis, while carefully handling items such as depreciation.

A well-structured model is easy to navigate. Inputs, calculations and outputs should be clearly separated with consistent formatting. Using conventions such as color coding for inputs helps others quickly understand the logic without confusion.

Formulas and layouts should be consistent across worksheets and sections of the model. This practice reduces errors and makes it easier to follow how assumptions flow through the model. Consistency also helps when multiple people are working on the same file.

A good model allows for quick updates when assumptions change. This means avoiding hardcoded values in formulas, linking inputs logically and designing the model so scenarios can be run without restructuring. Flexibility helps ensure that the model stays relevant over time.

AI can enhance the financial modeling process by automating data collection, identifying hidden patterns in large datasets and improving the accuracy of forecasts. As large players like JPMorgan, Goldman Sachs and Morgan Stanley embed AI into modeling and other areas,² smaller firms and startups face pressure to adapt or risk being left behind.

• Automation can also streamline repetitive tasks such as updating historical data or running scenarios, giving analysts access to results in near real time.
  
  
•  Finance-focused gen AI can draft management reports, summarize forecast results or generate scenario narratives that explain “what-if” outcomes in plain language. This helps bridge the gap between technical outputs and executive decision-making. In a survey of finance managers, conducting financial analysis and creating forecasts was the area where they saw gen AI having the most impact.³
  
  
• Agentic AI takes automation further by managing end-to-end workflows. It can plan, execute and adapt forecasts while still leaving room for human oversight. Gartner predicts that by 2028, 33% of enterprise software applications include agentic AI, up from less than 1% in 2024.⁴
  
  
• Explainable AI (XAI) makes complex models more transparent by showing which variables drive results, a critical feature in a highly regulated industry. While AI does not replace traditional modeling, it adds speed and depth to the process.

Decision-making is rarely about a single outcome. Models should allow users to test different scenarios (for example, optimistic, base and pessimistic cases) and sensitivity analyses to see how changes in one variable affect results.

Well-designed models are easier to use, maintain and trust over time. Strong design principles help ensure that a model is not only accurate but also durable and adaptable as business needs evolve.

• Modular structure: Breaking a model into logical modules (such as inputs, calculations and outputs) makes it easier to update, expand and audit. A modular approach reduces errors and improves long-term usability.
  
  
• Version control: Tracking model versions prevents confusion when multiple updates are made. Version control can be as simple as clear file naming conventions or as advanced as using collaborative tools that log changes.
  
  
• Documentation: Adding notes, instructions or a “user guide” tab helps others understand how to use the model through step-by-step guidance. Documentation also makes it easier for new team members or external stakeholders to follow assumptions and methods without guesswork, especially when models are paired with a case study for illustration.

Models should be easy to audit. Avoid overly complex models with formulas or hidden sheets that obscure calculations. Transparency builds trust with stakeholders and makes the model a reliable communication tool, even if a new modeler inherits the file.

While financial modeling is a powerful tool, it also comes with limitations and potential pitfalls. Understanding these challenges helps organizations use models more effectively and avoiding misleading conclusions.

Complexity and human error: Financial models can become complex, with numerous formulas and interlinked spreadsheets. Even small errors in formulas or links can produce significant mistakes in outputs, potentially affecting decisions.

Data quality: Accurate historical data is critical for building reliable models. Incomplete, inconsistent or outdated data can undermine the model’s accuracy and lead to flawed projections.

Dependence on assumptions: Models are only as good as the assumptions they are based on. If growth rates, costs or market conditions are estimated incorrectly, the results can be misleading. Decision-makers must critically evaluate assumptions and update them as conditions change.

Difficulty in adapting to rapid change: Fast-moving markets, economic shocks or new technologies can quickly render assumptions or structures outdated. Models must be maintained and updated frequently to remain useful.

Overreliance on models: While models provide quantitative insight, they cannot capture every aspect of reality, such as unexpected market shifts, regulatory changes or behavioral factors. Overreliance on a model can lead to misplaced confidence in projections.

Potential miscommunication: If models are not clearly documented or organized, stakeholders can misinterpret the results. Complexity or lack of transparency can reduce confidence in the model and its outputs.

Time and resource requirements: It takes time to build a detailed, accurate model. Organizations must balance the level of detail with the resources available and the urgency of the decisions to be made.