Fathom vs LLMs: Which Should You Trust with Your Financial Reporting?

Why finance teams are turning to LLMs

Ask ChatGPT or Claude to build you a cash flow forecast, and it will in seconds, in a tone you choose, at no extra cost beyond what you're already paying for the tool. For an SMB owner closing the books at 11pm or an advisor juggling twenty clients, that speed is genuinely appealing. There's no onboarding, no license to buy, and no learning curve beyond typing a question in plain English.  

That's exactly why LLMs have found their way into financial reporting and forecasting workflows that used to be the sole territory of dedicated software. However, the question isn't whether LLMs can help. They can and increasingly do. The question is where that help holds up under real client, board, or lender scrutiny, and where it quietly introduces risk that only shows up later.

What LLMs are actually good at

1. Drafting commentary and narrative first passes

LLMs are strong at turning a set of numbers into readable prose quickly. Given a P&L or a few KPIs, they'll draft a narrative summary, paraphrase it for a different audience, or restructure the same information into a shorter version, all in the same conversation, without a template.

2. Quick, one-off sense-checks on numbers you already have

For a single, contained question, which includes figuring out if a margin looks reasonable, or if a variance explanation makes sense, an LLM can be a useful sounding board. Nevertheless, it's not building or storing anything. It is simply reasoning over whatever you've pasted into that one conversation.

Where LLMs fall short for financial reporting and forecasting

It's worth being precise when it comes to your financial reports and forecasting, and LLMs aren't incapable of any of these. Technically, an LLM can talk through a forecast, attempt a consolidation, or even connect to accounting data through an integration system, though not necessarily as a live, always-current connection.  

The real difference of AI in financial reporting isn't capability. It's what happens without a purpose-built engine underneath: more manual setup, more room for error, and a lot more work to prove the numbers are right after the fact. You may find yourself at risk of these:

• ‍No live connection to your accounting data by default: Without additional setup or connectors, most people using an LLM for reporting are copying and pasting figures in, which means the numbers are a snapshot the moment they're typed, not a live feed, and every manual transfer is a chance to mistype a number.
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• ‍No built-in audit trail: A general-purpose chat interface isn't designed to keep a record of how a figure was calculated or where it came from, which matters when a number goes to a board or a lender, and someone asks you to defend it.
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• ‍Calculations aren't guaranteed to be consistent: Without a fixed calculation engine behind it, an LLM can reason its way to slightly different answers on the same prompt asked twice, which is a hard thing to explain in a client meeting.
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• ‍Pasting client financials into a general-purpose chat window carries confidentiality risk: Unless enterprise-grade data controls are explicitly configured, there's a real question about what happens to that data afterwards, which can be detrimental to the client’s data confidentiality.

Fathom vs LLMs: Quick comparison snapshot

LLMs

Cash flow forecasting

Purpose built engine

Possible manually, no guardrails

Reporting

Dedicated software

Draft-quality, manual assembly

Consolidated reporting

Up to 300 entities

Possible manually, harder to audit

Financial analysis

Live accounting data connection

Only via manual setup (e.g. MCP), but not live by default

Traceable AI commentary

Symbolic attribution

Not by default

Security and confidentiality

ISO 27001, SOC 2 Type 2

Depends on configuration

Fathom vs LLMs: Feature-by-feature comparison

Reporting capabilities

Fathom strengths:

• Offers a custom management reporting software builder that creates polished, board-ready reports by combining commentary, charts, and data.
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• As a financial analysis software, provides a central KPI library plus a KPI builder for tracking profitability, cash flow, and efficiency against metrics specific to the business.
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• Adds narrative commentary that stays linked to the underlying figures, which supports stronger financial storytelling than a static report.
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• As reporting, KPIs, and commentary are all included in a Fathom subscription, there is no separate cost for specific modules as usage grows. LLM pricing works differently, as heavier or more frequent use can run into usage limits or higher-tier pricing.

LLMs strengths:

• Can draft narrative summaries or KPI commentary almost instantly, provided the numbers are typed or pasted into the prompt.
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• Can restructure or rewrite a report's tone and format on request, without needing a fixed template.
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• Adapts freely to a specific ask, such as a board-ready summary versus a casual client update, within the same conversation.

Forecasting and planning

Fathom strengths:

• Provides built-in three-way forecasting as part of its cash flow forecasting software, so the P&L, balance sheet, and cash position update automatically whenever assumptions or scenarios change.
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• Driver-based modelling and scenario comparisons make it possible to run "what-if" tests and weigh outcomes against budgets and actuals directly inside the platform.
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• Because forecasting runs on a purpose-built engine rather than open-ended reasoning, the numbers come with guardrails against the kind of inaccuracy that's hard to catch in freeform output, and every result is auditable back to its assumptions.

LLMs strengths:

• There's no purpose-built AI forecasting engine, but the model can generate one on request. If configured, it can build an actual forecast, not just talk through the assumptions behind one.
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• AI in financial forecasting can stress-test forecasting logic conversationally before it goes into a formal model.
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• Needs no license or setup to start experimenting with forecasting logic, but the trade-off is auditability. Forecast built this way doesn't usually come with the same guardrails, so errors are more likely to slip through and harder to trace back to their source.

Multi-entity consolidation

Fathom strengths:

• As a consolidated reporting software, Fathom supports consolidations of up to 300 entities for single-currency groups and up to 50 for multi-currency groups, with support for 97 currencies.
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• Group accounts stay accurate through account-level eliminations, applied in full or as specific-value adjustments, with foreign exchange handled through provided or custom historical exchange rates.
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• The connection to source accounting systems means consolidations can be automated and scheduled rather than rebuilt by hand, which is guaranteed accurate, quick to run, and easy to audit. Also, there's no black box between the source data and the consolidated result.

LLMs strengths:

• Can be asked to perform eliminations and pull together a consolidated view but must be manually prompted and set by the users.
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• The difference is similar to forecasting. Without a dedicated consolidation engine, the output is more likely to contain errors and is harder to audit line by line.
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• Can explain consolidation concepts, such as eliminations or FX adjustment logic, clearly to someone learning the process.

AI & automation capabilities

Fathom strengths:

• Pre-processing built into the platform handles groundwork automatically, including consolidating entities, filling reports with your own data, laying out the chart of accounts, and building KPIs from financial and non-financial data.
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• ‍Commentary Writer is shaped by Business Context (goals, strategy, market conditions) and Report Context (what happened this period) and uses Symbolic Attribution so every figure in the commentary is traceable back to its source, which is a level of auditability a general-purpose LLM doesn't provide by default.
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• Commentary Writer is built specifically around financial reporting and advisory workflows, where a general-purpose LLM is built for open-ended tasks generally.
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• Client data is not sent to external AI models for training, and the platform has received ISO 27001 certification and been issued a SOC 2 Type 2 report.

LLMs strengths:

• General-purpose reasoning across a far wider range of topics than a finance-specific tool, useful when a question goes beyond reporting.
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• No fixed calculation engine means real flexibility for unusual or one-off requests, but that same flexibility is what allows inconsistent or hallucinated numbers across repeated prompts.
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• Can work from messy or unstructured input without needing it pre-cleaned into a specific format first. A screenshot of a spreadsheet, a pasted email thread, or a half-formatted table can all be handed over as-is, and the model will work with whatever structure it's given.
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• Iterates conversationally, refining tone, depth, or framing of a report draft through follow-up prompts. If a first draft of commentary is too technical for a client or too brief for a board, asking for a revision produces a new version immediately.

Integration ecosystem

QuickBooks integrations:

• ‍Fathom: An Intuit Accountant Platinum Partner and integrates with QuickBooks Online and Desktop.  Online data syncs automatically once every 24 hours, with a manual sync available at any time.
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• ‍LLMs: Some now connect through an MCP server, but this isn't a native, always-on connection in most consumer chat tools today. Data typically has to be exported or copied manually, making it a one-off snapshot rather than a live sync.

Xero integrations:

• ‍Fathom: An official Xero partner with a 5/5 rating on the Xero App Store. You can allow daily syncs or sync manually.
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• ‍LLMs: Same limitations as above. An MCP-based connection is technically possible, but a live, native sync isn't the default experience.

Excel/Google Sheets:

• ‍Fathom: Supports Excel/CSV/Google Sheets imports for financial data, which is useful if your accounting system isn’t directly supported. You can also import non-financial or operational data via Excel or Google Sheets to enrich reports.
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• ‍LLMs: Can read pasted or uploaded spreadsheet data within a conversation, but nothing refreshes automatically and nothing persists once the session ends.

Comparison deep dive

LLMs

Core reporting and KPIs

Full reporting, forecasting, consolidations and KPIs are available only under the Pro plan. Portfolio plan is limited to summary reports and dashboard.

No built-in reporting or KPI engine. Output depends entirely on what's pasted into the prompt each time.

Forecasting

Purpose-built specialised forecasting tool included in Fathom Pro plans and not on Portfolio.

No persistent model. A forecast can be built on request, but it has to be rebuilt and reviewed each time rather than maintained.

Client management

Both Portfolio and Pro plans give access to the Insights Dashboard for client-over-view, while full reporting, forecasting and consolidation tools are reserved for the Pro plan.

No client workspace. Conversations aren't organised by client or entity.

Consolidation

Included in the Pro plan, with multiple entities up to 300 for single-currency consolidations, and up to 50 for multi-currency consolidations.

No dedicated consolidation engine, but an LLM can be asked to perform eliminations directly. The trade-off is a higher error rate and less auditability than a purpose-built engine.

Report presentation

Sleek, interactive visuals designed for client presentations. Report builder also updates live as you make changes.

Increasingly capable of producing formatted output, including HTML, on request, but this must be prompted rather than a built-in feature.

Security

ISO 27001 certified and issued a SOC 2 Type 2 report; client data is not sent to external AI models for training.

Standard consumer chat interfaces aren't built or certified for confidential financial data. Pasted data may be used to improve the underlying model, which may be a breach of confidentiality.

Pricing model simplicity

Uses a tiered subscription model. Pricing varies by plan (Pro or Portfolio) and scales with the number of companies added.

Typically, a flat subscription or free tier, though heavier or more advanced use can hit usage limits or require a higher-priced plan.

User experience comparison  

Learning curve

Fathom:

• ‍Intuitive setup: The interface is designed so a new user can log in and begin building a first report without waiting on a lengthy implementation process. Onboarding support is there as an option for those who want extra guidance, so the pace of getting started is up to the user.
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• ‍Minimal configuration: Rather than building a KPI library or report structure from scratch, Fathom ships with a default set already in place, so the first report can go out without a long setup phase. Custom KPIs and templates can still be built later as specific needs come up.
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• ‍User-friendly: Financial insights dashboard software is designed for all stakeholders across the business, making it easy to navigate and interpret data. The tool is accessible, as the dashboard is built to be read by non-finance stakeholders as well as accountants.

LLMs strengths:

• ‍Immediate use: There's no configuration and no template to build before the first question can be asked, which makes it the fastest possible starting point when someone needs an answer instantly about their financial data.
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• ‍Familiar chat interface: Low-barrier of entry, as the same interface is already used for drafting emails, summarising documents, or general research. There's no new mental model to learn before applying it to financial questions.
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• ‍No dedicated training material needed: Anyone can type a question in plain English on day one, without reading documentation or sitting through onboarding.  

Daily usage

Fathom:

• ‍Dashboard navigation: Reporting, forecasting, and consolidation can be accessed smoothly from one interface. There's no switching between separate tools or exporting data between systems, so the numbers stay consistent and secure.
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• ‍Quick report creation: Pre-built templates allow for consistent client-ready reporting packs that can be scheduled for automatic delivery. Once a template is set up for a client or entity, the same structure can be reused every period instead of being rebuilt from scratch each time.
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• ‍Visual storytelling: Beautiful, interactive, visual reports that spark meaningful client conversations. Charts and dashboards turn static set of figures into something a client can explore during a meeting, which helps shift conversations away from just reciting numbers and towards what those numbers actually mean for the business.

LLMs strengths:  

• ‍Flexible for ad-hoc questions: Any request can be typed in without navigating a fixed menu. There's no specific screen to navigate before asking a question, so whatever comes to mind can be typed directly into the chat.
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• ‍No persistent workspace: Each new conversation starts from scratch unless data is re-pasted. A clean slate means no leftover context from a previous client bleeding into a new conversation, which can be useful when you deliberately need to start fresh every report.
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• ‍Format-agnostic output: The same answer can be dropped straight into an email, a Slack message, a doc, or a presentation without needing a specific export function. The trade-off is that nothing comes pre-formatted, so getting it client- or board-ready still takes manual work on the user's end.

Support  

Fathom:  

• ‍Highly rated support: Provides in-app messaging, email (support@fathomhq.com), and phone support 24/5 globally, with messages typically receiving a response within 15 minutes. All subscription plans include full access to customer success teams, with no tiered restrictions.
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• ‍High satisfaction ratings: Fathom consistently maintains customer satisfaction scores (CSAT) of above 95%.
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• ‍Award-winning service: Recognised as “Customer Service Organisation of the Year” at the 2022 Australian Service Excellence Awards.
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• ‍Comprehensive help centre and resources: Provides detailed guides, tutorials, and articles and a dedicated Fathom Help Centre covering all platform features. Also offers on-demand resources, such as product walkthroughs, webinars, and best-practice sessions to support ongoing learning.

LLMs:

• ‍Community-driven support: Community forums, general help centres, and public documentation are available.
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• ‍Largely self-serve: No dedicated account manager or customer success contact for financial reporting use cases. Support is largely self-serve, with no phone line or guaranteed response time for this use case.

Use case scenarios

Choose Fathom when...

• ‍You need board-ready reporting, that includes visual storytelling, client- and board-facing output: Fathom's reports combine commentary, charts, and financial statements into a polished format built specifically for presenting stakeholders, not just displaying numbers.
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• ‍You require dedicated reporting and forecasting software: With three-way forecasting and scenario modelling built in, as well as AI commentary that's traceable back to the numbers through Commentary Writer and Symbolic Attribution.
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• ‍You need confidential financial data handled securely: With ISO 27001 certification and a SOC 2 Type 2 report, and no client data sent to external AI models for training. For anyone handling client or board data day-to-day, that's a materially different risk profile from a general-purpose chat interface with no such certification behind it.

Choose LLMs when...

• ‍You want a fast first draft of your reports: When it comes to simply needing a quick first draft of commentary or narrative text, LLMs can turn a set of numbers into readable prose in seconds, without needing a template or report structure to set up first.
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• ‍You're working with non-sensitive figures or have enterprise-grade data controls in place: If the numbers involved aren't confidential, or your organisation has configured an enterprise LLM plan with data protections turned on, pasting figures into a chat window carries less risk than it would otherwise.
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• ‍You want a free or low-cost tool for one-off, exploratory questions: For a single sense-check, an LLM can be a reasonable choice when you're exploring an idea rather than producing something that needs to hold up under scrutiny later.

New MCP server coming: Bringing Fathom and AI together

The comparison above isn't really Fathom versus AI. It's purpose-built software against a general-purpose tool being asked to do a job it wasn't originally designed for.

That's why Fathom is exploring how an MCP server could let AI tools like Claude and ChatGPT work directly with your Fathom data, rather than relying on financials copied and pasted into a chat window. The idea is that a calculation that already lives inside Fathom could be pulled through in far fewer tokens than an LLM would need to reconstruct the same figure from raw data, without the security risk of pasting confidential numbers into a public interface.  

By combining these two powerful tools, we're bringing the flexibility of conversational AI together with the accuracy and audit trail of a purpose-built platform.

Try Fathom today

Start with a free 14-day trial to try Fathom for yourself or explore our feature page for more details.

Frequently asked questions

1. ‍What's the main difference between Fathom and using an LLM for financial reporting?
   
   ‍Fathom is a purpose-built engine for reporting, forecasting, and consolidation, with a live connection to your accounting data and an audit trail behind every number. An LLM can approximate much of this on request, but without a dedicated engine or prompt configuration behind it, the output relies more on manual setup and carries a higher chance of inconsistency.
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2. ‍Is it safe to put financial data into ChatGPT or another LLM?
   
   ‍It depends on the tool and its configuration. Standard consumer chat interfaces generally aren't built or certified for confidential financial data, and pasted data may be used to improve the underlying model unless enterprise-level data controls are explicitly turned on.
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3. ‍Can an LLM do three-way forecasting?
   
   ‍An LLM can be asked to build a three-way forecast, but there's no purpose-built forecasting engine behind it, so the result doesn't come with the same guardrails or auditability as forecasting done in dedicated software.
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4. ‍Why does traceability matter in AI-generated financial commentary?
   
   ‍When a figure goes to a board, investor, or lender, being able to show exactly where it came from matters. Fathom's Commentary Writer uses Symbolic Attribution so every number in a piece of commentary can be traced back to its source, which most general-purpose LLMs don't offer by default.
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5. ‍Will Fathom work directly with AI tools like ChatGPT or Claude?
   
   ‍Fathom is exploring an MCP server that would let AI tools connect directly to Fathom data. This is coming soon.
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6. ‍Should I stop using LLMs for financial reporting altogether?
   
   ‍Not necessarily. They're genuinely useful for drafting commentary and thinking through a question quickly. The distinction is what they're being asked to carry: a first draft or a quick sense-check is a reasonable use, while numbers going to a client, board, or lender benefit from the guardrails a purpose-built platform provides.