Beneath AnalyticsFinTech
Automated compliance reporting for a payments business.
A payments company spent three days a week compiling regulatory reports from five systems, and an audit had flagged the manual process as a risk. Beneath Analytics built an n8n pipeline into a validated central store with alerting and scheduled delivery, cutting reporting time 67% to four hours, with zero compliance incidents since.
Payments processing company
What was going wrong.
The risk and compliance team was spending three days per week manually compiling regulatory reports from five different data sources. Errors were common, an upcoming audit had flagged the manual process as a risk, and there was no bandwidth left for anything except reporting.
How we built it.
Steps in delivery order, each one depends on the one before it.
Automated data pipeline using n8n connecting all five source systems on a defined schedule.
Centralised PostgreSQL data store with schema validation and error alerting on every run.
Compliance dashboard with real-time regulatory metrics and anomaly detection for the risk team.
Scheduled daily report delivery with PDF export, ready for regulatory submission without manual assembly.
What changed.
Every metric measured against the baseline we inherited, reported the same way we report to clients.
67% reduction
Reporting time: 3 days to 4 hours
Zero
Compliance incidents: in 18 months post-implementation
40% freed
Team capacity: redirected to strategic risk work
6 weeks
Delivery time
The solutions this ran on.
Each of these engagements drew on the solutions below. Their pages set out the capabilities, platforms, and outcomes you can expect.
- AI Consulting & Development
AI Workflow Automation
AI workflow automation removes the manual work between your systems: compliance reports, risk summaries, inventory updates, and weekly reporting compiled by hand. Beneath Analytics maps the process, builds pipelines with n8n, Python, or dbt, then monitors them, so reports arrive on time without someone spending days assembling them.
View solution - AI Consulting & Development
AI-Ready Data Platforms
AI-ready data platforms means Beneath Analytics builds the warehouses, pipelines, and governance that AI and analytics depend on: BigQuery and Snowflake warehouses with dbt transformation, data quality checks, and documented lineage you can defend. Pipelines scale with your data volume, so models and dashboards run on data your team trusts.
View solution
Common questions.
Reporting time dropped 67%, from three days a week to four hours. An n8n pipeline pulls from all five source systems on a schedule, validates the data, and delivers the finished report, so nobody extracts or compiles it by hand.
Zero in the 18 months since go-live. Validation rules run before submission rather than after, so data quality issues are flagged while there is still time to correct them, which is what the audit had flagged as risk.
40% of the risk team's time was redirected to strategic work. Before the pipeline, that capacity went into copying data between five systems each week; now validation and delivery run automatically, and the team reviews exceptions instead of assembling figures.
Six weeks from process mapping to scheduled delivery. That included connecting all five systems, building validation and alerting, the compliance dashboard, and daily report delivery. Additional reports built on the same sources usually take one to two weeks each.
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