Anthropic Financial Services EMEA

Qonto

Customer Service Automation · Document Processing

Qonto wanted small-business customers to stop spending up to eight hours a month on repetitive financial admin such as transfers, invoicing, payroll, and cash-flow questions, but any agent touching bank accounts had to satisfy compliance, latency, and accuracy constraints.

2X faster bank transfers
3X less time to create client invoices
Generated one customer's 500-plus monthly invoices in a single batch
Runs payroll 5X quicker

Reported impact

Business outcome

Qonto made bank transfers 2x faster, cut invoice creation time 3x, sped payroll runs 5x, and saw customers trust agents with transfers as large as €100,000 while preserving final human approval for every action.

  • Compliance
  • Customer Experience
  • Productivity Improvement

Solution

Qonto's AI Lab built in-app agents on the Claude Platform to act as both operators and analysts inside business banking workflows. Users upload invoices or payroll documents and chat with the agent instead of navigating the product manually. Complex steps run on Claude Opus 4.5 and Sonnet, while smaller steps go to Haiku, all through Amazon Bedrock so Qonto can use structured outputs, prompt caching, quota controls, and provider-level GDPR and EU AI Act compliance. The operator agent prepares transfers, drafts invoices, and converts payroll runs into bulk payments but always pauses for final customer approval before money moves. On the analysis side, agents answer questions over transaction data so customers can understand cash flow without exporting numbers into spreadsheets. Internally, Qonto's builders use Claude Code, Claude Cowork, and Claude Design to ship and refine the experience quickly.

Data flow

Customers upload invoices, payslips, or ask a question in the Qonto app, the workflow routes each step to the right Claude model tier, the agent prepares transfers, invoice drafts, or payroll actions, and the customer gives the final approval before money or documents are sent.

Solution architecture

5 components · 3 layers
  1. Compute
    • Claude Opus 4.5 Runs the most complex agent missions around financial administration and insight generation.
    • Claude Sonnet Handles many of the production financial-admin steps alongside Opus.
    • Claude Haiku Covers simpler, latency-sensitive steps in Qonto's routed workflows.
  2. Serving
    • Claude Platform Provides the model platform behind Qonto's in-app operator and analyst agents.
  3. Uncategorized
    • Amazon Bedrock Supplies the controlled runtime and provider-level compliance envelope for Claude inside Qonto.

Architecture clues

  • Every money-moving workflow ends with explicit customer approval.
  • Prompt caching, structured outputs, and quota management all run through Amazon Bedrock.
  • Qonto routes each workflow step by latency, performance, and cost across multiple model tiers.

Evidence from the source

Access runs through Amazon Bedrock, with GDPR and EU AI Act compliance handled at the provider level.
Cuts client invoice creation time 3X, with its AI agents drafting every invoice and the customer confirming before it's sent
The agents' complex missions run on Claude Opus 4.5 and Sonnet, while simpler steps go to smaller models, including Haiku.
We were quite surprised to see transfers of 100,000 euros with agents.
sends bank transfers 2X faster, with no manual beneficiary search, amount entry, or reference copying, then waits for the customer's final approval before the money moves
Transparency Evidence & methodology 100/100 reference value · 87% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value100/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    3 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence87%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    5 direct source quotes retained.
  • Date provenance8/10
    The publication date comes from publisher-controlled metadata.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. Anthropic

    Qonto customer story

    First-party
    https://claude.com/customers/qonto
    Source type
    Vendor-published customer story
    Published
    Sep 3, 2026
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

  • Publisher catalog date Sep 3, 2026 · Anthropic · First-party
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.