Best AI Tools for Financial Modelling in 2025: Complete Guide

Introduction

Financial modelling has always been central to decision-making in business and investment. It helps you project revenues, expenses, and cash flow, and evaluate risk. The process once relied entirely on spreadsheets and manual input. That method worked but was slow and vulnerable to errors.

AI is now reshaping this process. Machine learning, automation, and natural language processing allow finance teams to build, test, and validate models faster. You no longer need to spend days building complex Excel models by hand. AI platforms integrate data from multiple sources, test hundreds of assumptions, and update forecasts in real time.

In this guide, you will learn about the best AI tools for financial modelling in 2025. Each tool is reviewed with details on pricing, integrations, strengths, and weaknesses. You will also find guidance on features to look for, real-world examples, common mistakes to avoid, and trends shaping the future of financial modelling.

What is Financial Modelling and Why Use AI?

Financial modelling is the practice of building a structured numerical model to represent business performance. Models are used to create budgets, forecast growth, value companies, and assess investments.

Traditional financial models are built in spreadsheets. Analysts link data using formulas, assumptions, and macros. While this approach works, it is slow, requires manual maintenance, and is often prone to mistakes.

AI improves the process in three ways:

Automation. AI tools pull data directly from accounting systems, CRMs, and ERPs. You spend less time copying data into spreadsheets.
Forecasting accuracy. Machine learning algorithms test large datasets and predict outcomes with greater accuracy.
Ease of use. Natural language queries let you type or speak questions and get answers instantly.

AI does not replace analysts. Instead, it reduces the manual work and lets finance professionals spend more time on interpretation and strategy.

Evolution of AI in Financial Modelling

Financial modelling has gone through several stages over the last three decades.

1990s: Excel was the dominant tool. Analysts built everything from scratch using formulas and pivot tables.
2000s: BI platforms like Tableau and Qlik added visualization and basic data integration.
2010s: Cloud financial planning platforms brought collaboration and integration across teams.
2020s: AI became integrated into planning systems. Predictive analytics and automation transformed how models were built and maintained.
2025: Generative AI and AI copilots now build scenarios, answer finance questions in plain language, and provide real-time rolling forecasts.

Startups use AI to create investor-ready models within days. Mid-market firms adopt tools to cut closing cycles and improve cash flow planning. Large enterprises integrate AI into FP&A to coordinate global operations and regulatory compliance.

Key Features to Look for in AI Financial Modelling Tools

When evaluating AI tools for financial modelling, look for features that align with your company’s needs:

Predictive analytics for revenue and expense forecasting.
Scenario and sensitivity analysis to test multiple assumptions.
Real-time data integration with ERP, CRM, and data warehouses.
Natural language queries for faster insights.
Collaboration features for distributed teams.
Security and compliance with financial standards like GAAP, IFRS, and SOX.

The right tool depends on your company size, industry, and data needs.

Best AI Tools for Financial Modelling in 2025

Below is a detailed review of the top tools. Each section includes pricing, integrations, use cases, strengths, and limitations.

1. Microsoft Excel with Copilot

Microsoft Excel Copilot is the natural upgrade for any finance team already dependent on Excel. It brings AI into the most familiar tool in finance.

You can ask questions in plain language, and Copilot builds models, forecasts, and charts instantly. For example, typing “create a five-year projection for revenue based on the last three years” delivers a working model in seconds. Analysts can then refine it as needed.

Pricing: Available as an add-on to Microsoft 365 enterprise licenses at $30 per user per month.
Integrations: Native with Excel and Microsoft 365 apps like Power BI and Teams.
Best for: Individual analysts, small teams, or companies that rely heavily on Excel.
Strengths: Easy to adopt, fast learning curve, integrates with existing workflows.
Limitations: Still bound by Excel’s structure, less suited for large-scale collaboration.

2. Datarails

Datarails is built for FP&A teams that need automation and consolidation. It connects with accounting and ERP systems, pulls data into Excel, and automates reports and dashboards.

FP&A teams use Datarails to cut manual reporting time. A global manufacturer reduced its financial close from 15 to 9 days using the platform. Board reporting becomes faster and more accurate.

Pricing: Starts around $1,000 per month depending on company size.
Integrations: Excel, NetSuite, QuickBooks, SAP, and more.
Best for: Mid-market companies and large finance teams.
Strengths: Automates manual consolidation, strong reporting functions, native Excel integration.
Limitations: Pricing is high for small businesses.

3. Quantrix

Quantrix specializes in scenario and sensitivity analysis. It lets you test hundreds of variables and assumptions in minutes, making it ideal for businesses in industries with volatile pricing or demand.

For example, a retailer can model how pricing changes across multiple regions impact overall revenue and profitability. Analysts can build multidimensional models that scale beyond what spreadsheets can handle.

Pricing: Custom enterprise contracts.
Integrations: APIs and data connectors for ERP and BI platforms.
Best for: Enterprises needing advanced scenario testing.
Strengths: Handles complex models at scale, advanced analytics.
Limitations: Learning curve for teams used to Excel.

4. Cube Software

Cube combines spreadsheet flexibility with modern FP&A features. It connects Excel and Google Sheets to centralized data, so teams can build budgets, forecasts, and management reports without duplicating work.

Mid-sized businesses use Cube when they outgrow manual spreadsheets but are not ready for enterprise FP&A systems.

Pricing: Starts at $1,250 per month.
Integrations: Excel, Google Sheets, NetSuite, Salesforce, and more.
Best for: Mid-sized businesses scaling their FP&A capabilities.
Strengths: Easy adoption, strong integration with existing workflows.
Limitations: May lack advanced features needed by very large enterprises.

5. Grid.is

Grid makes spreadsheets collaborative and interactive. You can turn static models into live dashboards shared online. Instead of sending files, you send a link where stakeholders interact with the model directly.

This is especially useful for distributed teams or when presenting to investors.

Pricing: Free plan available, paid plans from $19 per month.
Integrations: Google Sheets and Excel.
Best for: Teams needing collaborative and shareable models.
Strengths: Affordable, easy to share live models, simple interface.
Limitations: Less powerful for complex enterprise-scale modelling.

6. Pigment

Pigment is designed for startups and scaling businesses. It focuses on headcount planning, runway analysis, and fundraising scenarios. Founders use Pigment to create investor-ready forecasts that can be updated quickly.

For startups preparing for fundraising, Pigment helps present clear, visual forecasts to investors.

Pricing: Custom, based on company size.
Integrations: Salesforce, NetSuite, Workday, Snowflake, and more.
Best for: High-growth startups and scale-ups.
Strengths: Strong visuals, collaboration, easy to adapt during growth.
Limitations: Custom pricing can be high for early-stage startups.

7. Anaplan

Anaplan serves large enterprises with complex planning needs. It connects finance, HR, supply chain, and operations planning in a single platform.

Enterprises use Anaplan for multi-department scenario planning. For example, a Fortune 500 company can test how supply chain disruptions affect financial forecasts and HR headcount planning at the same time.

Pricing: Enterprise-level, often six figures annually.
Integrations: ERP, HR, and CRM systems.
Best for: Large enterprises with global operations.
Strengths: Enterprise scale, powerful integrations, cross-functional planning.
Limitations: Complex implementation, high cost.

8. Fathom

Fathom focuses on small businesses and accountants. It creates dashboards, KPI tracking, and AI-driven insights. Accounting firms often use Fathom to deliver visual reporting to clients.

For example, a small business can use Fathom to monitor profit margins, cash flow, and growth metrics in one place.

Pricing: Starts at $55 per month.
Integrations: QuickBooks, Xero, MYOB.
Best for: Small businesses and accounting firms.
Strengths: Affordable, easy to use, visual reporting.
Limitations: Limited depth for complex enterprise modelling.

9. Jirav

Jirav helps startups and SMBs create budgets, forecasts, and headcount plans. It integrates with accounting systems and produces investor-ready financial reports.

Startups use Jirav to speed up fundraising preparation. A SaaS startup reported saving weeks by building financial models in Jirav instead of Excel.

Pricing: Starts at $10,000 per year.
Integrations: QuickBooks, Xero, Netsuite.
Best for: Startups and SMBs raising funding.
Strengths: Purpose-built for startups, good integrations.
Limitations: Pricing may be high for very small teams.

10. Oracle Fusion Cloud EPM

Oracle EPM supports enterprise-scale financial planning, consolidation, and compliance. It handles multiple entities, currencies, and regulatory requirements.

Large corporations use Oracle EPM for global reporting, consolidation, and compliance. AI forecasting is built in, making it suitable for multinational complexity.

Pricing: Custom enterprise contracts.
Integrations: Oracle ecosystem, SAP, and more.
Best for: Global enterprises with complex reporting needs.
Strengths: Scales globally, compliance-ready, strong forecasting.
Limitations: Expensive and requires IT resources for setup.

Comparison Table of Tools

Tool	Pricing	Best For
Microsoft Excel with Copilot	$30 per user per month (add-on to Microsoft 365)	Analysts and teams already working in Excel
Datarails	From $1,000 per month	Mid-sized and large FP&A teams
Quantrix	Custom enterprise pricing	Enterprises needing advanced scenario modelling
Cube Software	From $1,250 per month	Mid-sized companies scaling beyond Excel
Grid.is	Free plan, paid from $19 per month	Teams sharing interactive financial models
Pigment	Custom pricing	Startups and scale-ups
Anaplan	Six-figure enterprise contracts	Large global enterprises
Fathom	From $55 per month	Small businesses and accounting firms
Jirav	From $10,000 per year	Startups and SMBs raising capital
Oracle EPM	Enterprise contracts	Global corporations with complex reporting needs

Best Free and Affordable AI Financial Modelling Tools

Free and low-cost plans allow startups and individuals to experiment with AI-driven modelling without heavy investment.

Excel with Copilot: included in Microsoft 365 subscriptions. If you already use Excel, this is the cheapest way to start.
Grid.is: offers a free tier that lets you share interactive dashboards. Ideal for small teams or consultants.
Fathom: priced at $55 per month, making it one of the most affordable paid options for small businesses.
Cube: while not free, it offers a demo and trial that lets you test before committing.

Affordable plans are often limited in features. As a company grows, expect to move into higher-tier pricing or enterprise solutions.

AI for Different Business Sizes

Startups
Startups need to focus on investor reporting, runway management, and fundraising scenarios. Pigment and Jirav are strong fits here. Both tools generate investor-ready reports quickly and adapt easily when assumptions change. Startups that use AI often cut weeks off their fundraising timeline.

Small and Medium Businesses (SMBs)
SMBs need budgeting, payroll forecasting, and expense control. Fathom and Cube are popular in this group. These tools reduce manual spreadsheet work and give owners and CFOs a clear view of cash flow.

Enterprises
Large corporations require consolidation across multiple subsidiaries, currencies, and compliance frameworks. Anaplan and Oracle EPM are built for this. They allow cross-functional planning across finance, HR, and supply chain. Enterprises value scalability and compliance above cost.

Real-World Case Studies

Startup Case
A SaaS company raising Series B used Pigment to prepare forecasts. Investor decks were built in five days instead of three weeks. The startup closed its round faster because forecasts were clear and investor-ready.

Mid-Market Case
A manufacturer adopted Datarails to reduce manual reporting. Their monthly close dropped from 15 days to 9 days. The FP&A team reported saving 40 percent of its time on data consolidation.

Enterprise Case
A global investment firm used Quantrix to model M&A opportunities. Analysts tested 500 scenarios in one day, compared to weeks with manual spreadsheets. This helped them make faster acquisition decisions.

Pricing Models in Detail

Pricing structures vary widely across tools.

Subscription per user: common with smaller platforms like Fathom, Cube, and Grid. Easy to start, scales with team size.
Flat monthly fee: Datarails charges based on company size, not user count. This suits larger FP&A teams.
Enterprise contracts: Anaplan, Quantrix, and Oracle EPM often require custom pricing that can run six figures or more. These contracts include integrations, onboarding, and support.
Freemium models: Grid offers a free version with limited features to attract small teams.

Companies should also account for hidden costs:

Training staff on new tools.
Data migration from legacy systems.
IT resources for enterprise deployments.

Integration with Existing Software

Integration is one of the most overlooked factors when buying financial modelling software. Without smooth integration, AI-driven models will still depend on manual data entry.

Excel and Google Sheets: Most tools connect here because spreadsheets remain central in finance.
ERP systems: SAP, NetSuite, and Oracle integrations are essential for enterprises.
CRM platforms: Salesforce and HubSpot integration is important for revenue forecasting.
BI tools: Power BI and Tableau help visualize results for stakeholders.
Data warehouses: Snowflake and BigQuery are becoming common integrations for larger teams.

Always confirm integration before buying. Lack of compatibility is a frequent reason companies abandon software.

Security and Compliance Considerations

Financial data is highly sensitive. AI tools must provide security features and compliance certifications.

Encryption: All financial data should be encrypted in transit and at rest.
Audit logs: Necessary for tracking changes in financial models.
SOC 2 compliance: Many vendors use this standard to validate security.
Regulatory compliance: Enterprises should confirm that tools support SOX, GAAP, and IFRS standards.

A breach in financial data can cause major reputational and regulatory risk. Always verify a vendor’s compliance certifications.

Step-by-Step Guide to Getting Started

Define your objectives. Are you planning for fundraising, budgeting, or global consolidation?
Select the tool that matches your company size and data needs.
Clean your financial data. AI models depend on high-quality inputs.
Import data from accounting systems, CRMs, and ERPs.
Build your first AI-driven model. Start with one use case like cash flow forecasting.
Validate outputs against historical performance.
Train your finance team on the tool’s core features.
Review outputs regularly. Adjust assumptions and update models as data changes.

Pros and Cons of AI in Financial Modelling

Pros

Faster model building.
Higher accuracy through predictive analytics.
Real-time updates when data changes.
Collaboration across teams.
Scales with business growth.

Cons

Subscription costs can be high.
Quality of outputs depends on input data.
Over-reliance on AI without human review creates risk.
Implementation can take time in larger organizations.

Alternatives to AI Tools

AI is not required for every business. Alternatives include:

Traditional Excel modelling: Still effective for small companies or individuals.
Manual scenario planning: Useful when data is limited and assumptions are simple.
Hybrid approach: Analysts continue to build models, but AI is used for validation or forecasting.

Common Mistakes to Avoid

Relying completely on AI forecasts without review.
Feeding incomplete or messy data into AI models.
Skipping validation against actual results.
Ignoring compliance and security requirements.
Failing to train staff properly on new systems.

Expert Tips and Best Practices

Always validate AI-generated results with historical data.
Keep a human review step in every workflow.
Document assumptions to prepare for audits and investor scrutiny.
Use version control so teams know which model is final.
Train staff and encourage adoption across the finance team.

Future Trends in AI Financial Modelling

The next wave of AI in finance will go beyond automation:

Generative AI: Will create scenarios automatically, suggesting best and worst case outcomes.
CFO copilots: AI assistants will become part of executive decision-making.
Continuous forecasting: Real-time updates will replace static monthly reports.
Automated compliance: Systems will automatically align outputs with GAAP and IFRS standards.
Cross-department adoption: AI will extend from finance into HR, supply chain, and operations planning.

FAQs

What is the best AI tool for financial forecasting?
Excel Copilot works best for individuals. Datarails and Cube are strong for mid-market companies. Anaplan and Oracle EPM are best for global enterprises.

Can AI replace financial analysts?
No. AI handles automation and forecasts, but analysts are still needed for judgment, context, and decision-making.

How secure are AI financial modelling tools?
Most leading vendors offer encryption and SOC 2 certification. Enterprises should confirm compliance with SOX, GAAP, and IFRS.

Do startups need AI for financial planning?
Yes. Tools like Pigment and Jirav save time and create investor-ready reports quickly.

Which industries benefit most?
Banking, insurance, SaaS, manufacturing, and retail are adopting AI-driven modelling the fastest.

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