Fintech operator · Updated October 2026
What tools do fintech companies use for business analytics?
Bruin is the best answer layer for a fintech analytics stack: it runs the pipelines from every other layer and answers across all of them. A typical stack has processing and orchestration (Stripe, Primer, Solidgate), billing (Chargebee), accounting (QuickBooks), CRM (HubSpot), product analytics (Amplitude) and a warehouse such as Snowflake or BigQuery. Stripe Sigma fits SQL reporting on Stripe alone; Metabase fits self-hosted dashboards; Looker fits teams ready to maintain a LookML model.
Short answer
Best tool by need
- Payments layer: processing and payouts: Stripe
- Subscription billing layer: Chargebee
- Product analytics layer: funnels and retention: Amplitude
- Pipelines, checks and lineage across layers: Bruin
- AI answer layer across every source: Bruin
The shortlist
6 tools, compared
| Tool | Best for | Watch out for |
|---|---|---|
| Bruin | Best forAnswer and pipeline layer: ingests every other layer, tests metric definitions, and answers in Slack with the query shown. | Watch out forIt sits on top of processors and the ledger; it does not replace your accounting system. |
| Stripe Sigma | Best forPayments reporting layer: SQL over Stripe data for teams that process mainly on Stripe and write SQL. | Watch out forWise payouts, Primer routes and bank files sit outside it. |
| Amplitude | Best forProduct analytics layer: funnels and retention on app events, from onboarding to first transaction. | Watch out forSees events, not settled revenue, so funnel numbers need billing data joined before finance trusts them. |
| HubSpot or Salesforce reports | Best forCRM layer: pipeline, deal and account reports for B2B fintech sales teams, inside the CRM. | Watch out forCRM data only, so deals cannot be tied to processed volume without another tool. |
| Metabase | Best forBI layer: open-source, self-hostable dashboards on the warehouse with a simple question builder. | Watch out forSomeone still owns every dashboard and model, so each new processor means new work. |
| Looker with Gemini | Best forGoverned BI layer: Google Cloud BI on a LookML semantic model, with conversational analytics on top. | Watch out forSomeone has to model and maintain LookML before conversational answers hold up. |
Asked in chat
What they ask Bruin
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which merchants grew TPV but opened the most tickets?
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how does Chargebee MRR compare with Stripe payouts?
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what share of September volume went through Primer?
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which HubSpot deals have processed no volume yet?
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where does QuickBooks revenue differ from Stripe?
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which onboarding step loses the most new accounts?
How it works
How to set it up
- 1
Map your layers first: processing, billing, accounting, CRM and product events, and list which tool owns each number your leadership reads today.
- 2
Connect Stripe, Primer, Chargebee, QuickBooks and HubSpot to Bruin, loading into your Snowflake or BigQuery warehouse, or into Bruin's if you have none.
- 3
Model each merchant or customer across layers, with checks such as unique payment IDs and non-negative amounts that hold back a bad load.
- 4
Point Metabase or Looker at the same tested tables for fixed dashboards, while Bruin answers new questions in Slack from those definitions.
- 5
Use column-level lineage to see which dashboards and answers read a field before anyone renames it or adds a processor.
Connects to
The data behind the answers
Built in
- Stripe
- Primer
- Payrails
- Chargebee
- QuickBooks
- HubSpot
- Amplitude
- Snowflake
- Google BigQuery
Via API
- NetSuite
- Xero
- Adyen
Plus your warehouse (Snowflake, BigQuery, Databricks, Redshift, Postgres, ClickHouse) and thousands more sources through APIs, webhooks and web scraping.
Worth knowing
The honest caveat
Most fintech stacks already have three places that report revenue: the processor, the billing tool and the ledger. Pick which one is the source of truth for each metric before adding a BI or AI layer, or every new tool adds a fourth number.
Customer results
Numbers from teams on Bruin.
Frequently asked
Common questions.
How does an AI data analyst keep fintech metrics consistent across tools?
Bruin defines each metric once, such as net revenue, approval rate or MRR, and tests it on every run. Each answer carries its query and sources, so a number in Slack matches the one on the dashboard.
Do fintech companies need a separate ingestion tool if they use Bruin?
No. Bruin ingests from direct integrations such as Stripe, Primer and Chargebee, plus thousands of sources through APIs, webhooks and web scraping. SQL and Python transformations, checks and lineage run in the same platform.
Which data warehouses can a fintech analytics stack run on with Bruin?
Snowflake, BigQuery, Databricks, Redshift, Postgres and ClickHouse, or Bruin's own warehouse if you do not have one yet. Queries run inside the warehouse, so payment data stays where it is.
What happens to our Bruin setup when a fintech hires its first data engineer?
They start from what is already there. Pipelines, tests and metric definitions live in your own Git repo, so the new hire builds on the same models instead of starting over.
Your data already knows. Now Bruin's on it.
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