Best AI Tools for Financial Analysts in 2026

The best AI tool for a financial analyst in 2026 is Kepler, because code computes and cites every number on your firm’s own definitions. For figures that go into a model, an investment committee memo or a client deliverable, every number opens to the exact page, line or cell it came from, and the same question returns the same figure every time.

This list is for analysts in investment roles (equity research, credit, private equity, asset management) and in corporate finance (FP&A, corporate development, audit and controllership). It covers research, modeling, Excel and document review.

We build Kepler, so it’s first and labeled. What we say about the other nine tools comes from their own sites, announcements and press coverage, current to November 2026 and linked inline, and we include a price only when the vendor publishes it or a news outlet has reported it.

How we picked

We put the same five questions to every tool:

• Where do the numbers come from? Pulled from a filing or dataset and computed by code, or written by the model as text.
• Where does it work? Excel, PowerPoint and Word, a browser, its own app, or an API.
• Does it read documents in full? A 10-K’s footnotes, a 200-page CIM, a folder of invoices, and whether it returns everything that matches.
• Does it know your definitions? Your adjusted EBITDA, your segment mapping, your peer set, your templates.
• Is it safe for client and firm data? Permissions that carry over, a written policy on training, and a SOC 2 Type II report (we hold one).

Quick comparison

Tool	Best for	Where it works	Where the numbers come from
Kepler	Numbers a firm decides on, on its own data	App, Excel, PowerPoint, Word, Claude, ChatGPT, Slack, API	Pulled from source and computed by code, cited to the line
Claude for Financial Services	A frontier assistant across Office and licensed data	Claude apps, Excel, PowerPoint, Word, Outlook (beta)	Connected providers and your files; the model writes the answer
ChatGPT for Financial Services	Financial data bundled into ChatGPT	ChatGPT, Excel, Google Sheets	Included datasets and connectors; the model writes the answer
Microsoft Copilot in Excel	Building and cleaning workbooks	Excel and Microsoft 365	The data in your workbook
AlphaSense	Broker research, expert calls, transcripts	Web app, Excel, PowerPoint	Licensed library, cited to text snippets
Daloopa	Source-linked historicals for models	Excel, AI assistants via MCP	Each data point linked to its filing
Hebbia	One set of questions across many documents	Web app, API, MCP	Your documents and licensed feeds
Fiscal.ai	Individual analysts on public companies	Web app	Source table image and PDF (add-on)
DataSnipper	Audit and finance procedures in Excel	Excel and the Alwin platform	Your evidence documents, matched and referenced
Perplexity	Quick cited lookups	Web, apps, Comet browser	Web pages and connected files, linked

The 10 best AI tools for financial analysts

1. Kepler (our pick)

We build Kepler.

Best for: investment and finance analysts whose numbers go into a model, a memo or a client deliverable, and who need every figure computed from source on the firm’s own definitions.

Most of an analyst’s day is numbers that have to tie: a segment margin across twelve quarters, a net debt figure that matches the filing, a comp table adjusted the way the house defines EBITDA. A general model produces those numbers as text, so a wrong one reads as confidently as the rest, and it doesn’t know your team’s definitions. The analyst ends up re-checking every cell.

We built Kepler around the analyst’s firm. Our forward-deployed engineers build your firm’s ontology from its own documents and systems: each company, fund, deal and person becomes one record, each house term gets one definition, and every relationship points back to the sentence that supports it. Before anyone types a question, extraction models we train read every page, from 26M+ SEC filings and 40M+ transcripts and IR documents to the models, CIMs and research notes your team keeps in SharePoint, Box or Google Drive, with tables and scanned pages included.

When you ask, code does the arithmetic. Statements assemble from the filings’ own XBRL, as filed or as restated, and ratios and comps run as code over cited inputs. Each calculated figure expands into its formula, with the source of every input one click away, and each citation is checked against its source while the answer is written. Analysts reach the same engine from Excel, PowerPoint and Word, from inside Claude and ChatGPT, from Slack, and through our API and MCP server, and agents refresh your models when a new 10-Q lands. Our engineers built this layer before at Palantir.

Why it’s our pick for financial analysts

• Numbers that tie. Every figure is pulled or computed by code, so the same question returns the same number with the same sources.
• Click through to the cell. Every number opens on the exact page, line or cell it came from, and calculated figures show their formula and inputs.
• Your definitions, applied every time. Adjusted EBITDA, segment mappings and peer sets live in your firm’s ontology.
• Footnotes included. Every document is read in full at ingest, so a question about off-balance-sheet commitments across five 10-Ks finds every disclosure.
• Your templates, built in. Comps sheets, models and memos are saved once as playbooks and come out in house format every time.
• The right model for each step. Small models we train do the reading. Anthropic’s case study on Kepler reports that they scored 94% on mapping financial statement labels to standardized taxonomy codes, against 38 to 46% for other models. Training one of those models cost us $9.
• Built for regulated firms. SOC 2 Type II, permissions mirrored from your systems, every query logged, and deployment in our cloud or yours.

Questions analysts ask Kepler

• “Build the income statement for the last eight quarters as filed, then as restated, and flag every line that changed.”
• “Trading comps for US vertical software, EV/revenue and EV/EBITDA on each filer’s own adjusted figures, every multiple traced to its line item.”
• “Net debt to adjusted EBITDA for every portfolio company on our definition, this quarter against last.”

“Our analysts used to spend more time verifying AI outputs than generating them. With Kepler, every number is already traced to source.” (Senior Managing Director, Top-10 Global Asset Manager)

Not the right fit if: your work is audit evidence testing in Excel, or you mainly want help with formulas and pivots in a workbook whose inputs are already sourced.

Try it at app.kepler.ai, see how Kepler compares, or book a demo.

2. Claude for Financial Services

Best for: analysts who want a frontier model in Excel, PowerPoint and Word, working on data licenses the firm already holds.

Anthropic launched Claude for Financial Services in July 2025, and for an analyst the center of it is Office. Claude for Excel, PowerPoint and Word are generally available on all paid plans, with Outlook in beta; in a workbook, Claude explains any cell, changes assumptions without breaking formulas and builds multi-tab models. Market data comes through connectors to licenses your firm already holds, among them FactSet, S&P Capital IQ, LSEG, PitchBook, Morningstar, Moody’s and Daloopa, and internal data through Box, Databricks and Snowflake. On Vals AI’s Finance Agent benchmark, Claude Opus 5.5 was first at 67.85% in the October 29, 2026 update. By default, Anthropic doesn’t train on Team or Enterprise data. Our guide on how to use Claude for financial analysis covers setup and prompts.

How it compares: the top-scoring frontier model on that benchmark, used on its own. Premium data arrives through your own provider licenses, and Claude learns your definitions from what you connect or upload. Kepler runs inside Claude, and Anthropic has written up how we built Kepler with Claude.

3. ChatGPT for Financial Services

Best for: bank and research teams that want financial data bundled into ChatGPT.

OpenAI launched ChatGPT for Financial Services on September 10, 2026: a separate plan built on ChatGPT Enterprise, for eligible institutions, running GPT-6 Astra at launch and aimed first at investment banking and equity research. Per OpenAI’s help page, the plan includes SEC filings, Quartr transcripts and presentations, Daloopa (24 hours delayed, capped at 3,000 data points per user a month), Fiscal.ai, PitchBook Essentials and Crunchbase, with LSEG News for US-based users and FMP and Nasdaq prices on a delay. More than 50 connectors include Datasite, Box, Preqin, FactSet and Intapp. Citations highlight the table or passage behind a figure, and admins can publish firm Excel, Word and PowerPoint templates. ChatGPT for Excel, generally available on all plans since May 5, 2026, links answers to the cells it uses and asks before editing a workbook.

How it compares: the quickest route to included data in a familiar interface. OpenAI describes the datasets as “selected coverage”, and the model still writes the numbers. Kepler runs inside ChatGPT too. See our ChatGPT vs Claude for financial analysis comparison.

4. Microsoft Copilot in Excel

Best for: analysts who want help with the mechanics of a workbook, inside the spreadsheet they already use.

Agent Mode lets Copilot build and edit workbooks through multi-step tasks, using tables, charts, PivotTables and formulas. Per Microsoft’s roadmap, it reached general availability on the web in December 2025 and on Windows and Mac in January 2026, where eligible licenses can choose OpenAI or Anthropic models. Microsoft retired the =COPILOT worksheet function on September 14, 2026, and points users to the Copilot pane in Excel.

What users say: “As far as having Copilot put together a full pitch deck, credit memo, model, or other deliverable, it’s a grand waste of time.” (a Wall Street Oasis user whose firm has Copilot Premium, July 2026)

How it compares: strong on formulas, cleanup, pivots and charts. Copilot works on the data in your workbook, so sourcing and checking the inputs stays with the analyst. For model-building tools specifically, see the best AI tools for financial modeling.

5. AlphaSense

Best for: analysts who spend their research time reading broker reports, expert calls and transcripts.

For an analyst, AlphaSense is a reading tool first. By its own count it covers more than 500 million premium documents, including broker research and the expert-call library that came with its 2024 Tegus purchase, now about 300,000 interviews, along with Canalyst models and BamSEC. Generative Search answers with the snippet it drew on, and since July 2026 assistants in Excel and PowerPoint bring that search into the files analysts already have open. SuperAnalyst, its agent for multi-step work, opened in early access in June 2026 and launched on October 6, 2026; it produces memos and models. Prices aren’t public; SpendHound reports average AlphaSense spend of $10,131 a year for smaller customers and $139,455 for enterprise customers.

What users say: “I also wish AlphaSense had more commodity price & volume data, basically supporting market analysis with quantitative figures since AlphaSense already has strong base of qualitative information.” (a G2 reviewer, October 2026)

How it compares: a licensed library built for search. Kepler is built for the figures that have to be computed and cited to the line, on your firm’s own data. See AlphaSense alternatives.

6. Daloopa

Best for: analysts who build models and want every historical data point linked to the filing it came from.

For model builders, Daloopa is a historical database in which every data point, from reported fundamentals to company KPIs and guidance, carries a link back to the document it came from. It covers 6,000+ public companies with as much as 14 years of history. Analysts pull it into workbooks through Scout, Daloopa’s Excel agent, or reach it through MCP connectors in Claude, ChatGPT, Microsoft 365 Copilot, Gemini Enterprise and Perplexity, and a delayed version comes with ChatGPT for Financial Services.

How it compares: a source-linked data layer for public-company historicals. Research, private documents and your own definitions live in other tools. See Daloopa alternatives.

7. Hebbia

Best for: analysts who need the same questions answered across a large stack of documents, such as a peer group’s 10-Ks or a CIM and its data room.

Hebbia is built around Matrix, a grid with a document on each row and a question in each column, where an agent answers every cell. For an analyst, the useful 2026 additions are Matrix 2.0 in August, which carries a finished grid on to a memo, deck or model with a review step before sign-off; a Word plug-in in September; and Headless Hebbia on October 1, which opens the platform through an API and MCP server. Licensed data arrives from providers such as FactSet, S&P Capital IQ, PitchBook and Preqin, and firm files from SharePoint, Box and Egnyte. Sacra puts its Professional tier at $10,000 per seat a year.

How it compares: a model runs over the documents you select, question by question. In Kepler, every document is read once at ingest and numbers are computed in code. See Hebbia alternatives.

8. Fiscal.ai

Best for: individual analysts and investors who want public-company data with source links at a low monthly price.

Fiscal.ai, formerly FinChat, is a public-company data terminal with an AI copilot. A Sourcing to Filing add-on links financial data points to an image and PDF of the source table, and Terminal Pro was reported in October 2026 at $39 a month, billed yearly. Fiscal.ai’s financials and operating metrics are also among the datasets included in ChatGPT for Financial Services.

How it compares: good value for one analyst covering listed companies. Its focus is public-company data, so a firm’s private documents and house definitions live elsewhere.

9. DataSnipper

Best for: auditors, controllers and finance teams who test figures against evidence documents in Excel.

DataSnipper calls itself an agentic platform for audit and finance. It started as an Excel add-in that matches workbook figures to source documents, and now adds Excel Agents for procedures such as journal entry testing and payroll reconciliation, DocuMine for questions across documents with references to the source, and Alwin, a cloud platform where agents run end-to-end procedures and the team reviews and signs off. Plans are Start, Accelerate and Elevate, with prices on request, and DataSnipper says it doesn’t train on customer data.

How it compares: the specialist for audit and controllership evidence. It isn’t a market data or investment research tool.

10. Perplexity

Best for: quick cited lookups on companies, markets and news.

Perplexity Enterprise is the quickest way for an analyst to get oriented on an unfamiliar name: it searches the web and connected company files and links every source it used. Its finance offering adds SEC filings from EDGAR and Quartr transcripts, and Perplexity Computer spreads longer tasks across 20 models.

How it compares: fast for orientation and news. An answer is only as good as the pages and data behind it, so figures headed for a model should be pulled from the filing.

How to choose by the job you’re doing

• Reading and summarizing filings, transcripts and research: Claude, ChatGPT, AlphaSense or Perplexity.
• Building and fixing workbooks: Copilot’s Agent Mode, Claude for Excel or ChatGPT for Excel.
• Historicals for a model: Kepler for statements built from XBRL with every cell cited, as filed or as restated; Daloopa for a source-linked data feed.
• Many documents at once: Kepler when the list has to be complete, since every document is read at ingest; Hebbia for question-by-question grids over a set you choose.
• Audit and controllership: DataSnipper.
• Numbers the firm decides on, on its own definitions: Kepler, inside the assistant your team already uses.

For investment teams, our best AI tools for investment research and best AI tools for equity research lists go deeper.

Test any tool in an afternoon

1. Click ten citations. Every one should open on the exact page, line or cell, with period and units in view.
2. Recompute one derived figure. A margin or a multiple should show its formula and inputs.
3. Ask the same question twice. The numbers should match.
4. Ask for your definition. Your team’s adjusted EBITDA, on a company you know cold.
5. Ask for a complete list. For example, every 10-K in your coverage that disclosed a material weakness in three years. Count what comes back.

The full checklist is in our guide on how to check AI-generated numbers, and Can ChatGPT do financial analysis? covers where general assistants slip.

Try Kepler with your own coverage at app.kepler.ai, or book a demo to see your comps and statements built from your firm’s documents.

Frequently asked questions

What is the best AI tool for financial analysts?

Kepler is our pick for analysts whose numbers go into a model, an IC memo or a client deliverable. Code computes every figure from the source, every number is cited to the exact page, line or cell, the firm's own definitions are built in, and the same engine works in Excel, PowerPoint, Word and the AI assistants analysts already use.

Can AI build a financial model in Excel?

Yes. Claude for Excel, ChatGPT for Excel and Copilot's Agent Mode all build and edit workbooks with real formulas. The weak point is the inputs: a general assistant chooses and types the historical figures, so each one needs checking. In Kepler, statements build from the filings' own XBRL, as filed or as restated, and arrive in Excel with every cell cited to its source.

Is ChatGPT or Claude better for financial analysis?

Claude leads Vals AI's Finance Agent benchmark: on the October 29, 2026 update, Claude Opus 5.5 is first at 67.85%, and OpenAI's highest-scoring model on the leaderboard, GPT-6.1 Sol, scores 61.82%. ChatGPT for Financial Services bundles more data into the plan, while Claude connects to the data licenses your firm already holds. Kepler runs inside both, so analysts can keep either assistant and get numbers computed in code.

How accurate are AI tools for financial analysis?

Accurate enough for reading and drafting, and short of reliable on numbers when a general model writes them. The best score on Vals AI's Finance Agent benchmark is 67.85%, and the top Financial Modeling score is 57.0%. Test any tool by clicking ten citations, recomputing a derived figure, asking the same question twice and asking for your firm's definition of a metric.

Is Microsoft Copilot enough for financial analysis?

Copilot is good at the workbook itself: building tables, formulas, PivotTables and charts, and Agent Mode builds and edits whole workbooks. It works on the data in front of it, so sourcing and checking the inputs stays with the analyst. Kepler sources the inputs: code computes every figure from the filings and your firm's documents, cites it to the page, line or cell, and writes it into the workbook.