7 Best AI tools in finance to automate your corporate financial workflows

Key takeaways

• Employment-weighted AI adoption at U.S. firms has reached approximately 78%, meaning most of the American workforce now sits inside a company that uses AI tools in some part of its operations.¹

• The best AI tools in finance now act like agents: they reason through a task and execute it directly, calling APIs, moving between systems, and handling exceptions instead of just following a fixed script the way older automation does.

• Airwallex pairs expense enforcement with the multi-currency accounts and payment rails an AI agent needs to actually move money, something general-purpose card platforms like Brex can’t do beyond flagging a policy violation.

Legacy accounting entries and siloed databases can’t keep pace with a finance function that’s expected to make decisions in real time. Corporate treasury, accounting, and FP&A teams are moving past point-solution automation toward agentic AI that can own entire workflows end to end.² This guide breaks down the best AI tools in finance today, from FP&A platforms to underwriting engines, and the banking infrastructure that makes them work.

Best AI tools in finance at a glance

Evaluating these platforms means looking past the chatbot layer to see how deep the automation actually reaches into your accounting systems and ERP.

Top-rated AI tools in corporate finance

Here’s what each platform actually costs, where it excels, and where it falls short.

Best for AI-powered expense policy enforcement: Airwallex Expense Policy Agent

Ideal for

Global companies running complex, multi-entity card programs that need real-time expense policy enforcement across languages and tax jurisdictions.

Our take

The Airwallex Expense Policy Agent tackles one of the most tedious parts of spend management: auditing receipt compliance. It reads your existing policy documents and converts those unstructured rules into active, real-time guardrails. Because it runs inside Airwallex’s own payment infrastructure, it evaluates expenses the moment they land instead of during a reactive post-facto audit.

Pros

• Reads and evaluates receipts natively across languages, checking transaction amounts and merchant names as it goes.

• Admins can attach internal-only notes to document regional exceptions, like per diem carve-outs, without exposing them to employees, and Airwallex says the policy data involved is never used to train shared AI models.

• According to Airwallex’s own early-access data, the agent clears up to 73% of routine, compliant expenses automatically, with a 99.4% alignment rate against human reviewers.⁴

• Works alongside Airwallex Expense Management and Airwallex Corporate Cards, so the same policy check applies whether spend hits a physical card or a submitted receipt.

Cons

• Access is limited to corporate clients on Airwallex’s Grow and Accelerate plans.

• Hard-coded form settings and approval routing still override the agent’s evaluations, so your core policy rules need to be configured correctly before the agent can enforce them well.

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Best for connected enterprise planning: Anaplan PlanIQ

Ideal for

Big, multinational operations where finance, ops, supply chain, and HR all need to plan off the same numbers.

Our take

Anaplan PlanIQ is built to handle massive, multi-dimensional planning data. It layers machine learning algorithms, including DeepAR+ and Prophet, into Anaplan’s modeling engine so planners can run predictive forecasts without leaving their workspace. The platform also lets you choose between its Classic and Polaris calculation architectures depending on model size, though you’ll need specialized model builders to get the most out of either.

Pros

• Combines internal accounting history with external macroeconomic drivers to automate multi-scenario forecasts.

• Connects to enterprise integration tools like MuleSoft and SnapLogic, on top of Anaplan’s own built-in CloudWorks connector, to keep data flowing between ERPs and warehouses.

• Pure Storage has used these capabilities to speed up quota and territory planning, while Jaguar Land Rover has applied Anaplan more broadly across sales forecasting, workforce planning, and supply chain compliance.

Cons

• Anaplan doesn’t publish pricing either, and third-party estimates vary widely, from roughly $30,000 for an entry-level deployment to well over $750,000 for a large enterprise contract.

• Implementation is consultative and often pulls in Big Four consulting partners at a cost that can match or exceed the first year’s license fees.

• Standard workspaces cap out around 130 GB, so larger datasets require upgrading to a pricier HyperModel environment.

Best for accounts payable automation: Vic.ai

Ideal for

High-volume accounts payable teams processing thousands of monthly invoices that want to move from manual entry to fully autonomous billing.

Our take

Vic.ai reads invoices contextually with a neural network instead of relying on OCR templates that break the moment a layout changes. That approach gets it to high straight-through processing rates, which is why enterprise controllers use it to scale back-office operations without adding headcount. Once an invoice clears, payments can run directly through the platform’s own VicPay.

Pros

• Vic.ai says its automation drops the average cost of processing an invoice from around $12 to under $2.

• Its auto-GL coding engine, trained on more than a billion historical invoices, delivers fast approvals and reliable PO matching.

• The platform is also available directly through the AWS Marketplace.

Cons

• Vic.ai doesn’t publish standard SaaS rates, so you’re looking at a custom sales process or an AWS Marketplace quote.

• Onboarding takes four to eight weeks while the model trains on your specific general ledger patterns.

• Smaller firms with low transaction volumes may find the platform more than they need.

Best for automated month-end close: Numeric

Ideal for

In-house accounting teams ready to ditch spreadsheets for a cloud-native close platform that plugs straight into NetSuite, QuickBooks, or Xero.

Our take

Numeric tackles the month-end scramble with real-time transaction monitoring, bank reconciliation parsing, and automated flux analysis that keeps the general ledger audit-ready year-round. Its pricing is refreshingly straightforward compared to the custom-quote norm elsewhere on this list. That makes it an easy entry point for growing finance teams.

Pros

• Starts at $30 per user monthly, with faster implementation than most legacy close software.

• Automatically drafts flux explanations and variance narratives, freeing senior accountants to review exceptions instead of rebuilding spreadsheets by hand.

Cons

• Dashboard is built for a single organization’s books, which makes it a weaker fit for fractional accounting firms managing multiple clients.

• Live ERP syncing, automatic reconciliations, and custom flux reporting all require upgrading to its custom-priced enterprise tier.

Best for Excel-native planning: Vena Solutions

Ideal for

Finance teams that live and breathe Excel but have hit a wall on version control, real-time data, and forecasting across multiple entities.

Our take

Vena Solutions keeps the Excel interface finance teams already know while housing the actual calculations in a cloud-native database called CubeFLEX. That structure prevents the broken formulas and version conflicts that plague spreadsheet-only planning. The tradeoff is a consultative rollout, so smaller teams with tight budgets may find the setup process heavier than expected.

Pros

• Preserves native Excel functions and shortcuts, which shortens training time and speeds adoption across department heads.

• Its AI assistant, Vena Copilot, lets team members query data in plain language to generate variance analyses and ad hoc reports.

• Role-based licensing splits access into Power User, Contributor, and View Only seats to help control software spend.

Cons

• Vena doesn’t publish pricing, but third-party estimates put first-year implementation costs anywhere from $75,000 to $250,000 once professional onboarding is included.

• The same estimates suggest Contributor seats can run five to ten times the cost of a View Only license, so adding more budget owners gets expensive fast.

• Structural model changes typically require your account manager and a separate developer API fee.

Best for alternative-data credit underwriting: Upstart

Ideal for

Financial institutions, credit unions, and auto dealers that want machine learning to expand loan portfolios and automate borrower underwriting.

Our take

Upstart moves past the narrow lens of a traditional credit score by evaluating more than 1,600 non-traditional variables to assess real creditworthiness. That lets lenders identify “hidden prime” borrowers who get shut out of competitive lending by legacy scoring models. Upstart runs an asset-light marketplace model, which keeps it efficient but exposes its loan volume to swings in the broader private credit market.

Pros

• Upstart says its models fully automate roughly 90% of loan approvals, cutting origination costs and manual document review.⁵

• The company also reports default rates up to 75% lower than traditional rules-based underwriting at the same approval rate.

• It has expanded into secured auto retail and home equity lines of credit to diversify beyond personal loans.

Cons

• Lenders pay variable platform and referral fees that scale with loan volume, and Upstart’s own performance is tied to macroeconomic swings that can compress approval rates quickly when default rates rise.

• To reduce its dependence on funding partners and simplify its regulatory structure, Upstart has applied for a national bank charter to form Upstart Bank, N.A., though the company says this isn’t meant to compete with its existing bank partners for consumer deposits.

Best for explainable, fair lending: Zest AI

Ideal for

Banks and credit unions that need transparent, explainable underwriting models to satisfy fair lending and regulatory disclosure requirements.

Our take

Zest AI is built around the regulatory reality of deploying AI in consumer credit. It generates compliance documentation and adverse action notices alongside every decision, which helps institutions stay inside fair lending rules. It’s a strong fit for auto, personal, and mortgage credit risk scoring, though it still needs dedicated IT work to integrate with existing loan origination systems.

Pros

• Automates up to 80% of application decisions and reports a 25% overall lift in approvals without added portfolio risk, with even larger gains for historically underserved borrowers: up to 49% more approvals for Latino applicants, 41% for Black applicants, and 40% for women, according to the company’s own data.

• Its explainable models remove the “black box” risk that trips up compliance teams.

• A newer module, LuLu Pulse, benchmarks lending performance against peer credit unions using NCUA and HMDA data.

Cons

• Implementation requires custom integration with your existing loan origination system, typically taking around six weeks.

• The platform is heavily tuned for retail and consumer lending, so it’s less useful for unique, unstructured commercial credit.

Other AI tools worth considering by category

These specialized tools solve narrower problems across bookkeeping, spend control, and investment research.

FP&A and forecasting: Datarails, Pigment, BudgetFlow

Datarails automates reporting for Excel-heavy finance teams: budget around $24,000 a year, closer to $75,000 once custom integrations get involved. Pigment ditches the spreadsheet metaphor for a visual, database-first approach to scenario planning. BudgetFlow automates budget collection, pulling inputs straight from across business units.

Accounts payable and bookkeeping: DualEntry, Netgain, Zeni, Docyt

DualEntry is a modern general ledger that uses AI to categorize transactions automatically, though it doesn’t publish pricing. Netgain automates lease accounting and fixed asset schedules to keep enterprises compliant with ASC 842. Zeni pairs machine learning speed with CPA oversight in a hybrid bookkeeping model starting at $549 monthly, while Docyt offers automated expenseFlow, closingFlow, and insightFlow modules starting at $299 monthly.

Investment research and market analysis (outside this guide’s scope): AlphaSense, Hebbia, Fintool

AlphaSense uses semantic search to help treasurers scan earnings transcripts, broker notes, and financial filings. Hebbia applies neural search to extract terms and covenants from dense financial documents. Fintool acts as an SEC filing assistant that answers compliance questions instantly, but all three are built for capital markets analysis, not day-to-day accounts payable or cash flow management.

General-purpose AI assistants for finance teams

Standard generative models are useful for writing formulas, drafting summaries, and checking policies as part of everyday finance work.

These assistants excel at processing text, but none of them touch a live transactional rail on their own. You can’t prompt ChatGPT to execute an international wire transfer or issue a card without separate API engineering behind it. They work best as analytical copilots sitting alongside specialized systems, helping analysts draft variance summaries or structure formulas.

Our methodology for ranking the best AI tools in finance

We ranked these platforms on depth and real-world fit, not surface-level features. ERP integration mattered most, since that’s what keeps new data silos from forming, followed by how much genuine autonomy a platform hands over: does it just digitize a workflow, or actually run it as an agent? Total cost of ownership, implementation speed, and enterprise-grade security rounded out the list, because those are what decide whether a tool pays for itself for a CFO.

Understanding AI in corporate finance

Before you deploy any of these tools, it helps to know what actually separates an autonomous system from the rules-based software finance teams have relied on for decades.

What is agentic AI, and how is it different from rules-based automation?

Rules-based RPA follows a fixed script: it’s fast until an invoice format shifts or the input turns unstructured, and then it just stalls. Agentic AI works differently. It reasons through the problem, calls whatever APIs it needs, and handles exceptions on its own, so instead of waiting for an accountant to catch a bad date, it can pull the original contract, flag the mismatch, and draft the vendor email itself.

How generative AI is used for credit explanations and legacy system modernization

Generative AI is what lets old financial systems talk to modern, digital-first workflows. In consumer lending, that means turning dense credit risk math into adverse action notices a borrower, and a regulator, can actually read. It’s just as useful on the backend, writing, testing, and converting legacy code so institutions can modernize old databases without ripping out the whole system.

Types of AI tools used across the finance function

Each corner of the finance function uses AI differently. Underwriting software leverages non-traditional variables to automate consumer credit checks, while expense management tools scan global receipts to enforce spending rules in real time. Accounts payable platforms use deep learning to categorize invoices and match PO documents, and close management tools continuously monitor general ledger activity to run flux reviews.

Why Airwallex is the foundation for your AI-powered finance stack

Running AI agents safely requires a payment and treasury network built to execute programmatic instructions cost-effectively.

Multi-currency accounts and APIs as the infrastructure layer

Autonomous AI agents need a flexible, global banking infrastructure to plug into, not a legacy, siloed network. Opening an Airwallex Business Account gives finance teams a single interface to manage funds across 20+ currencies, cutting the conversion costs and administrative friction that come with traditional banking. That programmatic foundation is what lets AI-driven expense and invoicing platforms move money without manual intervention.

AgentOS: connecting AI agents directly to Airwallex’s payment rails

Airwallex AgentOS is a developer toolkit that connects AI agents directly to Airwallex’s payment rails.³ Installing the AgentOS plugin in an environment like Claude Code or Cursor lets an agent check account balances and cash flow, pull indicative FX rates, and issue virtual cards. It’s built with a deliberate limit: agents can’t initiate transfers, conversions, or payouts on their own, so a human or a separately configured workflow stays in control of anything that actually sends money out the door.

Security and compliance backing the infrastructure layer

Granting an AI tool access to production financial data raises the stakes on security. Airwallex backs its platform with global licensing and partnerships with 50-plus financial institutions worldwide. AgentOS adds its own permission layer on top, with a sandbox environment for testing new agents safely before anyone deploys them live.

Choosing a banking partner for cross-border and e-commerce operations

FX pricing structure is one of the clearest ways to compare banking partners for global operations. In Airwallex’s own comparison of Revolut Business and Airwallex FX pricing, Revolut grants a monthly fee-free FX allowance that varies by plan, then charges 0.6% on conversions beyond that cap, so fast-growing companies can hit a step change in fees once they outgrow their tier. Airwallex charges a consistent markup of roughly 0.5% to 1% above the interbank rate regardless of plan, which makes costs easier to forecast as volume grows.

For multi-entity enterprises trading globally, an Airwallex Business Account lets AI agents execute international payouts through local rails instead of routing everything through SWIFT. When you’re evaluating the best business bank accounts for global operations, look for a platform that simplifies multi-entity accounting integration. Airwallex’s two-way sync with Xero and NetSuite means global transactions post automatically, which matters just as much for ecommerce business bank accounts juggling payment collection and vendor pay runs as it does for treasury teams running multi-entity ledgers.

If you’re ready to open a business bank account, start with an Airwallex Business Account built for exactly this kind of infrastructure.

Frequently asked questions about AI tools in finance

What is the best AI tool for corporate finance and FP&A?

Vena Solutions is the one to beat for Excel-native modeling, while Anaplan PlanIQ is built for large-scale, connected planning. Vena keeps the spreadsheet interface familiar while centralizing data in the cloud. Bigger companies tend to lean toward Anaplan, since its machine learning models can handle multi-dimensional operational and supply chain forecasting that Vena isn’t built for.

How does AI automate accounts payable and month-end close?

AP platforms like Vic.ai use deep learning to capture invoices, assign GL codes, and match PO files automatically. Close management tools like Numeric continuously parse bank statements and monitor transactions to generate draft flux explanations. That combination cuts out manual entry and can meaningfully shorten close cycles.

Can AI-driven credit scoring models outperform traditional FICO scores?

Yes, platforms like Upstart and Zest AI analyze hundreds of alternative financial variables, from real-time spending habits to employment stability, to price credit risk more accurately than legacy FICO models. Both companies report meaningfully higher approval rates without a corresponding rise in defaults, though the exact lift varies by lender and portfolio.

Are AI tools in finance secure and compliant with federal regulations?

Yes, reputable AI tools protect financial data with role-based permissions, bank-grade encryption, and policies that keep proprietary data out of public model training. Underwriting platforms like Zest AI also generate compliance reports and adverse action notices automatically to help institutions meet fair lending requirements.

What is the difference between traditional rules-based automation and agentic AI in banking?

Rules-based automation runs on a rigid script, and that script breaks the moment a layout or field changes. Agentic AI doesn’t have that problem: it reads context, reasons through anomalies, and calls whatever APIs it needs to finish the task on its own. Take an AP agent: it can catch a non-standard invoice discrepancy, check it against the original contract, and send the vendor a corrective email without anyone getting pulled in.

How do corporate spend platforms track AI agent API and token costs?

Modern spend platforms let controllers issue dedicated virtual cards with custom spending limits to isolate AI API costs in real time. Assigning a distinct card to each AI vendor, whether that’s OpenAI, Anthropic, or Cursor, lets finance teams map token spend directly to departments and catch budget overruns before they compound.

What banking infrastructure do AI-powered finance tools need to operate effectively?

AI-powered tools need a financial partner that provides programmable APIs, local multi-currency accounts, and programmatic card issuing. Airwallex AgentOS serves as that foundation, letting autonomous agents read balances, issue cards, and prepare international pay runs safely, directly on top of Airwallex’s payment rails.

Sources

1. https://www.federalreserve.gov/econres/notes/feds-notes/monitoring-ai-adoption-in-the-u-s-economy-20260403.html

2. https://www.cfoconnect.eu/resources/reports/state-of-ai-in-finance-2026/

3. https://www.airwallex.com/docs/developer-tools/ai/agentos

4. https://help.airwallex.com/hc/en-gb/articles/14736592558223-Admin-guide-Expense-Policy-Agent

5. https://ir.upstart.com/news-releases/news-release-details/upstart-apply-national-bank-charter