How Generative AI Streamlines Scoping, Planning, and Designing Financial Models

Financial Modelling with AI: Part 2

Generative AI can support the early stages of financial model development—specifically, the scope, plan, and design phases—before the actual building begins. This article focuses on how AI tools like ChatGPT and Copilot can assist in these preparatory steps.

Scoping the Model: Defining Purpose and Audience

The first step is to clarify the model’s purpose, its primary goals, and expected outputs. This includes identifying issues in existing reporting, the data sources, and the key stakeholders or end users. Without a clear scope, building an effective model becomes difficult.

Planning: Mapping the Components and Relationships

Once the scope is set, the next phase involves detailing the model’s components. This means deciding which inputs, constants, calculations, and outputs to include—and just as importantly, what to leave out. Agreeing on specifications and timelines here helps keep the project on track.

Design: Structuring the Model

Design focuses on the model’s architecture and how it integrates with existing management information systems. Modellers bring expertise in creating spreadsheets that follow best practices, are fit for purpose, and easy to use. However, subject matter knowledge is often required to guide what the model should do and how calculations should be structured.

Collaboration with end users or clients during this phase is crucial. Models built without input from those who will use them risk being ineffective or ignored.

How AI Can Assist

AI tools like ChatGPT and Microsoft’s Copilot are practical aids during these stages. For example, by prompting ChatGPT to outline the scope of a financial model, you can quickly generate a list of relevant financial statements and line items. It can suggest typical elements such as cash flow considerations and equity or debt financing, helping confirm your approach.

AI also offers value in developing subject matter expertise. If you’re preparing a model for venture capital funding, for instance, ChatGPT can provide tailored advice on terms like carried interest and hurdle rates, as well as performance metrics commonly used by investors. While you must verify this information independently, it gives a solid starting point and highlights areas worth researching.

Beyond financial inputs, AI can propose advanced valuation techniques such as Monte Carlo simulations, comparable company analysis, and exit multiple valuations. These insights help align the model with industry expectations and investor requirements.

Understanding AI’s Strengths and Limitations

AI’s responses depend heavily on how questions are phrased. Vague prompts tend to generate broad, sometimes generic answers. More detailed prompts yield more specific and practical suggestions.

For example, asking who typically uses financial models returns a wide range of stakeholders—from business owners and finance teams to consultants and regulators. However, AI might omit certain users like venture capitalists unless explicitly prompted. This highlights the need for careful prompt design and independent validation.

Comparing ChatGPT with Copilot reveals differences in focus. Copilot integrates closely with Microsoft Excel and other Microsoft tools, providing productivity-focused assistance with high-level task summaries. ChatGPT, on the other hand, offers broader, more detailed responses and can drill down into specifics when prompted further.

Estimating timelines for model development illustrates these differences. Copilot might give a general estimate, while ChatGPT can provide a more nuanced breakdown—though neither should be blindly trusted without input from experienced professionals.

Practical Tips for Using AI in Early Model Development

• Use AI to generate initial ideas and frameworks but always verify with domain experts and real data.
• Refine your prompts to get more tailored and relevant responses.
• Collaborate with stakeholders throughout the process to ensure the model fits its intended purpose.
• Don’t rely solely on AI outputs—use them as a guide, not a guarantee.
• Leverage AI to expand your own knowledge of financial modelling concepts and industry standards.

Both ChatGPT and Copilot can be valuable resources during the scoping, planning, and design phases of financial modelling. They can help reduce the risk of missing critical elements and suggest areas for further research, especially when subject matter knowledge is limited.

To learn more about practical AI applications in finance, explore relevant courses on Complete AI Training.

In the next article, the focus will shift to how AI can assist after the model has been built—covering testing and implementation.