Beneath AnalyticsIndustry
FinTech
For payments and financial services firms, the cost is in the manual work around the data: reports compiled by hand, fraud found after the money has moved, and figures nobody can trace for an auditor. We automate the pipelines, put checks and an audit trail behind every number, and build detection on live data. For one payments business, reporting that took three days now takes four hours.
Where this sector loses ground.
For financial services and FinTech teams who need numbers that stand up to an audit. We build the pipelines, checks and reporting that replace manual compilation, using automation and AI where they pay back.
Compliance reporting assembled by hand
Regulatory reports are pulled together from five or more systems by people whose time should go to risk work.
Fraud found after settlement
Detection rules run in batches on stale data, so suspicious patterns surface once the money has already moved.
Risk numbers that disagree
Finance, risk and operations each keep their own extracts, and reconciling them takes days before any decision is made.
No audit trail behind the figures
When a regulator asks how a number was produced, the answer is a spreadsheet with no record of where it came from.
How we work in this sector.
Sequence matters: govern the data first, make every reported number traceable, then build the models and automation that act on it.
Automate extraction from payment, KYC, core and risk systems into one validated store, with alerts when a load fails.
Add validation rules and lineage, so every reported figure can be traced back to its source.
Build detection and forecasting on live data, so risk signals arrive before settlement.
Schedule report generation and delivery, with an audit trail attached to every output.
Where this usually starts.
The solutions we most often deploy in this sector. Each page sets 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
Applied AI
Applied AI puts machine learning and language models into the workflows where they create measurable value: recommendations, classification, document processing, and anomaly detection. Every engagement starts with a business problem, is trained on your data, and ships with monitoring so you know when model performance starts to drift.
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.
Yes. EU data residency, consent and access controls, documented lineage and audit trails are part of the architecture from the start, not added afterwards.
Often not. Many compliance problems are integration problems: data in five systems that nobody has connected. We automate the pipeline with tools you already own, and tell you plainly if a dedicated platform is the better answer.
We build and test models on your own transaction data, starting with anomaly detection that surfaces cases for review. Rules and models are tuned with your risk team, never shipped as a black box.
Get your Free AI Readiness Snapshot
A 30-minute session, no pitch: you get a one-page opportunity map showing where AI can make a real difference in your business.