Best AI for Building Financial Models (2026)

As an investment banker, private equity professional, or finance leader looking for the best AI to build financial models, you might be trying to:

• Reduce the long hours of manual work needed to build DCFs, LBOs, M&A models, forecasts and more in Excel, without sacrificing accuracy
• Manage complex models with multiple formulas and assumptions that are prone to errors and difficult to maintain, debug, and verify
• Consolidate fragmented data and produce reliable models fast enough for live deals and investment decisions

There are many tools out there that can help with these tasks, from generative AI assistants to finance-specific platforms. Choosing one, however, is only part of the challenge. To deliver consistent results, AI for financial modeling needs to access the right data, fit into existing financial workflows, and operate with appropriate governance so outputs remain accurate, auditable, and trustworthy.

This guide explains the leading AI tools for financial modeling, how they differ, and what to consider when evaluating them. Drawing on Neurons Lab’s experience of building 100 AI projects across financial services and regulated industries, we’ll also explore when custom AI makes sense, and how to choose the right approach for your organization.

In this guide to the best AI for building financial models, we’ll cover:

• Key takeaways
• The challenges of financial modeling and how AI creates value
• The best AI for financial modeling: Three approaches and tools
• What to know before choosing financial modeling AI tools
• How Neurons Lab helps you choose and implement the right AI solution
• How a leading investment firm cut reporting time by 75% with AI-driven data consolidation
• FAQs

Not sure which AI approach fits your financial modeling workflow? Book a call with Neurons Lab to find out.

Key Takeaways

• The real cost of modeling is the preparation. Data collection, first drafts, and formula-checking eat up more analyst time than the modeling itself, and that’s exactly the work AI removes. It’s also where most spreadsheet errors start. Research puts the rate at 94% for spreadsheets used in business decisions.
• The right AI approach depends on model complexity, data maturity, and team readiness. How complex your models already are, how mature your data is, and how ready your team is to trust AI with any of it determine whether an AI-assisted spreadsheet workflow, a purpose-built platform, or a custom AI-native system fits best.
• Most ‘hallucinations’ are specification problems. An AI updating a DCF without clear instructions on units, currency, or which tab to update is more likely to return a number that looks right but doesn’t match your model’s conventions.
• Guardrails matter for two different kinds of risk. An assumption like a discount rate or IRR can drift outside a reasonable range by accident, while a pitch deck can be manipulated on purpose, like a founder hiding instructions to influence an AI-powered screening process. Both need to be caught inside the model, before the output reaches a client.
• The fastest way to implement AI for financial modeling is with an experienced partner. Firms that pair AI adoption with an AI specialist like Neurons Lab bake governance and risk controls into the model from day one, instead of catching problems after they’ve already reached a client.

The Challenges of Financial Modeling and How AI Creates Value

Building or updating a model rarely starts with analysis. An investment banking analyst building a discounted cash flow (DCF) or merger model may spend several hours or even days collecting data, linking spreadsheets in Excel or Google Sheets, and checking formulas before testing a single assumption or running a scenario to value a company.

An example of DCF forecasting model output within a balance sheet – Image source: Corporate Finance Institute

The same pattern repeats across the financial services industry, just with different inputs:

• A private equity or venture capital associate underwriting an acquisition, screening a deal, or stress-testing a startup’s projections works through the same data collection and formula-building before they can test an investment thesis and calculate IRR.
• A corporate finance team closing the books each month, or an analyst doing real estate financial modeling or credit underwriting on a new asset, hits the same bottleneck earlier in the process than it should. The manual work happens before the real analysis does, and that limits how many opportunities a team can evaluate and how quickly it can act on the ones that matter.

The data itself makes financial modeling harder. Financial information comes from multiple sources and rarely follows one standard. Companies report balance sheets and other financials in different formats, while accounting, market, and internal data is spread across disconnected systems. Analysts have to clean and standardize all of it first, and every manual copy or adjustment adds another chance for an error to reach a valuation. .

Scale just adds more risk. Enterprise financial models span dozens of interconnected worksheets, so a changed assumption can become difficult to trace. Research published in Frontiers of Computer Science found that 94% of spreadsheets used in business decision-making contain errors, highlighting how easily mistakes can be found in complex financial models.

There’s also little time to slow down. Investment committees, client presentations, acquisitions, and financing decisions often require revised forecasts within hours. Balancing speed, accuracy, and risk under tight deadlines can increase the likelihood of mistakes.

How AI Creates Value

Rather than replacing financial modeling, AI removes a lot of what happens around it. Depending on the approach, AI can look like:

• Automating data collection and first-draft model builds: AI can pull data from company filings and financial statements, then build a working DCF, three-statement model, or comp set, so an analyst starts from a draft instead of a blank spreadsheet.
• Managing complex multi-tab models: AI can explain a formula, trace how one assumption ripples through a 40-tab model, and update linked cells without an analyst manually retracing every dependency.
• Improving data consistency: AI can standardize information from filings, income statements, market feeds and internal sources before it enters a model, so formatting issues don’t become valuation errors.
• Speeding up scenario analysis : AI can rerun a model against new assumptions and create a revised forecast quickly enough to meet same-day deadlines.

AI gives analysts back the time they’d otherwise spend on setup, which is time better spent on interpreting results and actual decisions. But which approach to AI fits your team, workflows, and the technological infrastructure you already have? That’s what we explore below.

Best AI for Financial Modeling: 3 Approaches and Tools

Picking the right approach comes down to how you work. This includes how complex your models are, how consolidated your data is, and how ready your team is to trust AI with any of it. The more mature your data infrastructure, the more advanced an AI approach you can realistically use.

That maps into three broad categories: AI-assisted spreadsheet workflows, purpose-built financial modeling platforms, and custom AI-native modeling systems. Below, we break down each, including what each is for, the trade-offs, and which tools are worth knowing.

1. AI-Assisted Spreadsheet Workflows

These tools integrate AI into the Excel or Google-Sheet-based processes analysts already use every day, rather than replacing them with a new platform.

This approach suits small teams with well-established Excel models who want a productivity gain without switching systems, or teams testing AI for the first time who want a low-risk starting point.

It’s a weaker fit once you need AI to run a full workflow rather than speed up individual steps within one. Analysts still need to review and validate AI-generated outputs, and it may be difficult to maintain organization-wide governance if different teams use AI-assisted workflows in different ways.

When to use these tools: Investment banking analysts updating DCFs, FP&A teams refreshing forecasts, and private equity associates who primarily build and maintain company financials and cash flow statements in Excel and want to improve productivity without replacing existing workflows.

Typical cost: Low to medium, depending on the tool and enterprise license.

Some of the best tools in this category include:

1. Excel + Microsoft Copilot

Microsoft Copilot in Excel⁷ is an AI assistant that can create formulas and charts, visualize data, identify trends, format workbooks and tables, extract insights, and more. Because it works within Excel, analysts can maintain full editing control and retrieve data from other workbooks, while using the built-in features to speed up repetitive tasks.

Best for: Finance teams using Microsoft that want AI assistance with a range of tasks including chart creation, formulas, and data analysis, without leaving their existing workbooks.

2. ChatGPT

OpenAI’s LLM, ChatGPT⁸, is a general-purpose generative AI tool that supports financial research, data analysis, writing code, generating formulas, and creating custom analytical tools. Rather than replacing Excel, it’s typically used alongside it to build the code or tools that are then used to process or analyze financial data within spreadsheets.

Best for: Teams looking for a versatile AI assistant to complete financial analysis, coding, creating formulas, and building custom financial workflows.

3. Claude for Excel and Claude for Powerpoint

Anthropic’s Claude⁹ is particularly useful for time-intensive document workflows. It can review lengthy financial statements, annual reports, contracts, and other long documents, before pulling out the information that’s needed to build or update financial models. This can save time and reduce the manual effort needed to gather supporting information.

Claude for Excel applies this directly inside spreadsheets to help build and audit models, while Claude for PowerPoint uses that same document review to help draft and update decks and pitch materials.

Best for: Teams reviewing large documents to extract details required for building and supporting financial models.

4. Shortcut

Shortcut¹⁰ is an Excel-based AI Agent built specifically for financial modeling. It includes editable financial model templates alongside the ability to build complex models from scratch, helping analysts automate repetitive modeling tasks, generate formulas, and speed up common financial modeling workflows without leaving Excel.

Best for: Investment banking, private equity, and FP&A teams who create complex financial models in Excel and want to speed up repetitive workflows.

2. Purpose-Built Financial Modeling Platforms

Purpose-built financial modeling platforms are specifically built for analysis, market intelligence, research, valuation, and workflows. Unlike general-purpose AI assistants, these enterprise platforms connect directly to institutional financial and market data sources, allowing analysts to access verified, up-to-date information without having to manually combine data from multiple sources.

These platforms are often the next step for organizations looking to standardize AI use across teams. Because they create a shared environment, analysts can collaborate more effectively while organizations can ensure governance and oversight.

While these platforms offer specialized financial functionality, they’re designed around common organizational workflows, not the proprietary processes of a specific organization.

When to use these tools: Organizations that already have licensed institutional data (Bloomberg, FactSet, etc.) and want to standardize AI use across investment banking, private equity, FP&A, or corporate finance teams.

Typical cost: High

Purpose-built platforms include:

1. Rogo

Rogo¹¹ is an integrated, secure AI platform designed to help financial professionals increase productivity, unify financial data, and automate workflows. Developed by former bankers and investors, it integrates with your existing systems and data, produces institutional-grade outputs, and uses trusted data providers to combine your firm’s data with high-quality financial information.

Best for: Investment banks, private equity, and other teams that need AI-powered deal screening, company research, financial analysis, and automated workflows.

2. AlphaSense

AlphaSense¹² is an AI-powered market intelligence platform that aggregates public, private, and proprietary sources into a single research environment. Designed for every major industry, including investment banking, private equity, asset management, and hedge funds, it helps analysts quickly find the information they need for financial models and investment decisions.

Best for: Investment banking, private equity, hedge funds, asset managers, and corporate strategy teams that need comprehensive market research and company intelligence to support financial analysis and modeling.

3. Custom AI-Native Modeling Systems

As financial models grow more complex, many organizations move beyond spreadsheets and off-the-shelf AI platforms, often by rebuilding models as code. Written as Python scripts instead of worksheets, a model becomes easier to maintain, version, and extend with AI.

Custom AI-native modeling systems are designed around an organization’s own systems, data, processes, and requirements. Rather than adapting existing workflows to fit an AI-assistant or platform, custom systems allow organizations to build an AI system that reflects their proprietary data, compliance requirements, and internal operations. These AI solutions can support a wide range of workflows, including extracting data from documents, generating formulas, supporting scenario planning, or flagging inconsistencies for human review.

Like purpose-built AI platforms, custom AI systems still depend on a foundation of consolidated, well-governed data. Even the most advanced, bespoke system is only as effective as the data it relies on.

When to use these tools: Firms that have outgrown spreadsheet-based AI assistants or purpose-built platforms and need AI built around their own data, processes, governance, auditability, and modeling methodologies.

Typical cost: Depends on the chosen system

An example of a custom agent build with Neurons Lab

Custom AI systems include:

1. Python-based models

Python is a popular programming language that supports data analysis, numerical computing, machine learning, and visualization. Compared to Excel-based financial models, Python models are easier to maintain and extend as models become more complex. Analysts can also use Python libraries to automate calculations, manage data, visualize data, and build repeatable workflows.

Best for: Finance teams, investment firms, and organizations building complex or highly repeatable AI financial modeling workflows.

2. Internal AI agents

Internal AI agents are custom-built systems designed to automate specific, complex, and multi-step workflows. Rather than completing a single task, they can gather information from multiple sources, analyze documents, complete calculations, and work across connected systems.

For example, Claude Code can rewrite or update financial models on the fly as assumptions change, which is very hard to achieve in a spreadsheet-based model. Because they’re built around an organization’s own processes, they can also be designed with specific rules, validation steps, and review processes to minimize risk.

Best for: Organizations looking to automate complex financial models and workflows while maintaining governance, oversight, and human review.

Claude Code

3. Custom AI workflows built around proprietary data

Custom AI workflows connect directly to internal systems, proprietary data, and existing processes. This allows AI to work from the same specific information that analysts already use, including investment criteria, portfolio details, and historical data. These workflows can also be tailored to complex workflows that generic AI tools and off-the-shelf platforms can’t support.

Best for: Investment firms, private equity funds, corporate finance teams, and other organizations with proprietary data, established modeling methodologies, or specialized analytical requirements.

Here’s how these nine tools stack up by complexity and how off-the-shelf they are:

Financial modeling AI tools plotted by complexity and how off-the-shelf vs. custom they are — illustrative positioning, not to scale.

What to Know Before Choosing Financial Modeling AI Tools

Choosing an AI tool for financial modeling involves more than comparing features and pricing. It means understanding how well a tool lets you manage accountability, governance, and risk. Whether you’re using an AI-assistant, a purpose-built platform, or a custom AI system, the same considerations apply:

Someone Still Needs to Own the Model

Even though AI can automate a lot of financial modeling, it doesn’t remove accountability. Someone still needs to understand how a model works, review AI output, and take responsibility for the final decision.

What does change is who that person is. Previously, ownership might have been the responsibility of an analyst. With AI, it’s more likely to fall to someone with analyst-level programming skills and the ability to audit what the model is actually doing. Without this kind of clear ownership, errors can go undetected until they’ve already influenced a valuation, investment decision, or client deliverable. And at this point, the cost of catching them is much higher.

Guardrails Need to Be Built in by Design

AI systems can still make mistakes, so it’s important to include strong guardrails by design rather than as an afterthought when a model breaks. Validation steps, review checkpoints, and defined escalation paths should be built into workflows from the start.

For example, in a DCF valuation that might mean automatically flagging any output where a terminal growth rate or discount rate falls outside your firm’s approved range, before the number reaches a client-facing model.

Designing these controls up front is far less costly than correcting a bad output after it has already shaped a decision.

Many AI “Hallucinations” Are Really Specification Problems

Not every generative AI hallucination or incorrect output is the result of a flawed model and in many cases, the issue is that the expected output format wasn’t defined clearly enough. If you ask AI to update a DCF without specifying units, currency, or which tab to update, you’re more likely to get a number that looks plausible but doesn’t match your model’s conventions.

The more precisely these requirements are specified, the more consistent and reliable AI-generated outputs become.

This changes how organizations should respond to incorrect outputs. Rather than assuming you need to change the tool, try adjusting your prompts to see how it affects outputs. When evaluating AI tools, it’s also worth considering not only the quality of the tool, but whether it supports structured prompts, constrained outputs, and repeatable workflows.

Regulatory Disclosure and Traceability Are Mandatory

As AI is used more frequently in financial decision-making, firms need to disclose its use and ensure the process is fully traceable.

The EU AI Act already requires this for systems that influence financial consequences, with governance and transparency obligations built in. US federal law hasn’t caught up in the same way, though the Department of Treasury has issued guidance for AI use in financial services.

Either way, the standard is the same. When using AI, know which data sources fed a model, how the output was generated, and where a person reviewed it before it reached a decision.

Security and Prompt Injection Are Real Risks

Any workflow that uses AI to analyze external documents can potentially be impacted by security risks and prompt injection. For example, a startup founder could hide instructions within a pitch deck, and use these to influence a venture capital or private equity firm’s AI-powered screening process.

An example of how a company can hide instructions within a pitch deck that can go unseen by human eyes but AI can flag

Similar risks exist when using AI to evaluate borrower financial statements, working capital schedules, acquisition documents, vendor submissions, or other adversarial content. This is distinct from other data-quality problems because the external party has an incentive to exploit the process.

Getting these safeguards right requires experience building AI workflows specifically for regulated, high-stakes financial environments. That’s exactly where an experienced AI enablement partner earns its place in the process, regardless of which approach you choose.

How Neurons Lab Helps You Choose and Implement the Right AI Tools and Systems

Long-term success depends on creating the right infrastructure to minimize costly financial and regulatory compliance risks.

Neurons Lab helps financial services firms choose the best approach with AI, implement it effectively, and build the foundations needed for the safe, consistent use of AI-powered financial models.

As an AI enablement partner serving organizations across the US, Europe, and Asia, Neurons Lab combines executive training, AI adoption programs, and custom agentic AI solutions to support secure, practical deployment. Clients build operational AI capability aligned with core workflows, governance, and business priorities.

Trusted by 100+ clients, including HSBC, Visa, and AXA, we’ve accelerated AI integration in banking, wealth management, private equity, investment firms, fintechs, and other highly regulated industries.

Having delivered multiple AI projects tailored to organizations operating in highly-regulated environments, we can advise whether an AI-assisted workflow, purpose-built platform, or custom system is the right choice.

Choose the Right Approach for Your AI Financial Modeling Workflow with FSI Adoption Expertise

Many organizations know AI can improve financial modeling but don’t know whether to use AI workflows, invest in a purpose-built platform, or build a custom AI system.

Our financial services expertise helps you understand how FSI workflows and regulatory requirements actually function, so guidance on which approach to take is grounded in your firm’s realities. This approach helps you avoid the implementation mistakes that introduce errors, risk, or security issues.

For example, our AI adoption diagnostic assesses your existing workflows, data readiness and model complexity, before recommending the right approach or tier for your team. From there, we develop an AI roadmap that includes a step-by-step plan for moving from isolated ad hoc AI use to a consistent, firm-wide approach.

You can also choose tailored workshops that align leadership teams. They help understand which parts of the modeling process are safe to automate, how to specify AI-generated outputs so they’re actually usable, and who signs off before a number reaches a client or investment committee.

Move Your Team Off Manual Excel Work with Hands-On AI Enablement

Many finance teams still spend too much time collecting data, updating formulas, refreshing models, and repeating manual Excel processes that leave less time for analysis and strategic decision-making.

As an AI-enablement partner, Neurons Lab provides hands-on support for analysts, finance teams, and portfolio managers, helping them automate workflows and adopt AI consistently and accurately. Through role-specific training, teams learn how to use AI for the repetitive parts of day-to-day model building like data collection, formula updates, and refreshing models, freeing up time for actual analysis and judgment.

Our tailored training also covers who owns checking any AI output before it’s used for decision-making, emphasizing that using AI tools effectively also includes knowing when and how to verify it.

Beyond training, you’ll also receive help embedding AI into the workflows your teams already use. Through knowledge capture and context engineering, we translate how your team actually builds models, including formats, conventions, standard formulas, and typical data sources. This helps turn that information into structured context your AI systems can apply consistently.

With AI-native workflow design, you can integrate AI directly into existing Excel models (e.g., updating DCF’s assumptions or rebuilding a three-statement model or merger model) without forcing anyone to learn a new system.

With continuous knowledge transfer, you can ensure your team can maintain, improve, and expand these AI workflows independently over time. Instead of creating long-term dependency on an external vendor, your organization builds the internal capability to scale financial modeling with AI, increasing output and analytical capacity without increasing headcount.

Consolidate Financial Data into Reliable Models with Custom Development and Governance

Financial data is often fragmented across internal systems, market data providers, spreadsheets, and company documents. Custom AI agents reduce the manual work required to collect data from multiple sources, while in-built safeguards protect you from adversarial external inputs, not just ordinary data errors.

Neurons Lab’s forward-deployed engineers work alongside your teams to build custom AI model infrastructure around your specific data, workflows, and technology stack. Our AI-native model development also includes building three-statement models as code with tools like Claude Code, rather than spreadsheets, helping standardize disparate data and ensure your calculations run the same way, every time.

Every AI project is built with governance by design. Validation rules, structured output formats, usage policies, disclosure readiness, auditing, and security controls are embedded from the outset, with ongoing evaluation against your firm’s own standards to help identify model drift and maintain regulatory compliance as AI adoption grows.

How a Leading Investment Firm Cut Reporting Time by 75% with AI-Driven Data Consolidation

A Luxembourg-based investment firm was spending around 20 days per month manually collecting and validating data from a range of sources to create its investment reports. The process was slow, resource-intensive, and error-prone.

By implementing an AI-powered data consolidation system through Neurons Lab, the firm automated data extraction, standardized information from multiple sources, and used AI to turn raw data into comprehensive reports, automatically generating key sections including market analysis, variance analysis, and risk assessments. As a result, it:

• Reduced reporting time by 75%
• Reduced the error rate by 90%
• Improved report turnaround time by 60%
• Increased investor satisfaction scores by 40%

Results like this show that the greatest value of AI for building financial models often comes from removing the manual work surrounding the modeling process. Rather than replacing the investment firm’s team, AI gave them back approximately 15 of the 20 days a month it used to spend on manual reporting.

From Time-Consuming Manual Financial Modeling to Efficient, AI-Powered Workflows

Building a leveraged buyout (LBO) model or closing the books each month all start the same way, with days of struggling through fragmented data before any real analysis happens. Most finance teams live with patchy data sets, error-prone spreadsheets, and deadlines that don’t leave room to double-check.

With the right AI approach, analysts stop losing the entire week just to set up models. They get a working structure and a data set they can trust, so they can spend time on what matters like interpreting the numbers and deciding what to do.

Picking a tool is only the start. The most important part is knowing if your team needs an AI-assisted spreadsheet workflow, a purpose-built platform or a custom build, and putting governance in place so the ouputs hold up to questioning and audits. That’s what Neurons Lab helps financial services firms figure out.

Ready to find the right AI approach to your financial modeling workflows? Book a call with Neurons Lab to find out.

FAQs

What is the best AI for building financial models?

There’s no single best AI for financial modeling, instead the right choice depends on the complexity of your existing models, how much control you need, the maturity of your data infrastructure, and how familiar your team is with AI. For example, Excel users may feel more comfortable with AI-assisted workflows, while an organization looking to automate end-to-end financial workflows may prefer purpose-built financial modeling platforms, or custom AI agents.

What’s the best AI for creating financial models in Excel?

For Excel users, the best option is an AI-assistant that integrates within existing spreadsheet workflows. These can create formulas, analyze data, and automate time-consuming, manual tasks. This makes it easier to build, review, and update financial models while still using familiar spreadsheets and workflows.

Is AI safe to use for financial modeling in a regulated environment?

It can be, provided the right guardrails are in place. For example, you need validation steps before an output reaches a decision, clear ownership of who reviews AI-generated numbers, and a traceable record of which data and prompts produced a result. Without a safe implementation and structure, even a good tool introduces risk.

Sources

¹ https://www.microsoft.com/en-us/microsoft-365-copilot/pricing

² https://chatgpt.com/pricing/

³ https://claude.com/pricing

⁴ https://shortcut.ai/pricing

⁵ https://toolradar.com/tools/rogo/pricing

⁶ https://costbench.com/software/financial-data-terminals/alphasense/

⁷ Copilot in Excel: https://www.microsoft.com/en-us/microsoft-365/blog/2026/06/25/copilot-in-excel-built-for-the-era-of-frontier-finance/

⁸ChatGPT: https://chatgpt.com/

⁹ Claude: https://claude.com/

¹⁰Shortcut: https://shortcut.ai/

¹¹ Rogo: https://rogo.ai/

¹² AlphaSense: https://www.alpha-sense.com/