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.

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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

@Bruin
which merchants grew TPV but opened the most tickets?
@Bruin
how does Chargebee MRR compare with Stripe payouts?
@Bruin
what share of September volume went through Primer?
@Bruin
which HubSpot deals have processed no volume yet?
@Bruin
where does QuickBooks revenue differ from Stripe?
@Bruin
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.

All customer stories

4x faster
time to insight
Read the Workhy story
60% less
data quality issues
Read the Workhy story
Month → 1 day
from idea to a production data source
Read the ProphetX story

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.

Keep reading

What is Bruin?

Bruin is an open-source, AI-powered data platform. One project covers the whole path from source to answer: data ingestion (with ingestr), SQL and Python transformations, quality checks, column-level lineage, a data catalog and scheduling. On top of that data, an AI data analyst answers questions in Slack, Microsoft Teams, Google Chat, WhatsApp, Discord, Telegram, email or the browser, and builds dashboards from a prompt.

Pipelines are plain SQL, Python and YAML files in your own Git repository, and the work runs inside your own warehouse: Snowflake, BigQuery, Databricks, Redshift, Postgres, ClickHouse, DuckDB and others. The Bruin CLI is open source under Apache 2.0 and runs on a laptop, in CI or on-prem. Bruin Cloud is optional and adds scheduling, the AI data analyst, governance, SSO and audit logs.

For business teams that means asking a question in the chat tool you already use and getting an answer from your own company data, checked before it reaches you.

Replaces: Separate tools for ingestion, transformation, orchestration, data quality, cataloguing and BI
Open source: Bruin CLI under Apache 2.0; ingestr source-available under the Functional Source License
Your data: Stays in your warehouse; pipelines stay in your Git repository
Security: ISO/IEC 27001:2022 certified and SOC 2 Type 2 attested
Used by: Data teams at Lessmore, StarBerry, NativeMinds, SCG, M8 Games and Fabrikatör

Why Bruin for Fintech

Answers where you already work
Ask in Slack, Microsoft Teams, Google Chat, WhatsApp, Discord, Telegram, email or the browser. There is no new tool to learn and no ticket to file with the data team.
Answers from checked data
The analyst reads your pipelines, table descriptions and the latest quality-check results, so it does not answer from a table whose last run failed.
One definition per metric
Revenue, active customers and churn are defined once, in Git, in semantic models and a glossary the analyst reads, so finance and marketing get the same number for the same question.
Built on a real data platform
Bruin also loads and models the data. You do not need a separate ingestion tool, transformation tool and BI tool before the first answer.

How it works

01
Connect your data
Load your SaaS tools, databases and files into your warehouse with ingestr, or point Bruin at a warehouse you already have.
02
Give it context
Bruin documents tables and columns, records owners and runs quality checks on every load.
03
Ask
Ask in Slack, Teams or the browser. Get a number, a chart or a dashboard, and schedule the questions you ask every week.

How the AI data analyst works Read the docs