FinTech · Case study
Leading FinTech Company in UK
Real-time fraud signal was siloed across acquiring, settlement and dispute systems. Detection lagged by hours.
The context
A leading UK FinTech processes millions of card transactions a day across acquiring, settlement and disputes. Fraud signal lived in three separate systems and reconciled overnight, so detection lagged real-time by hours, long enough for organised fraud to clear before anyone saw the pattern.
At a glance
- Sector
- FinTech
- Headline result
- 62%
- Service lines
- AIData & Cloud Transformation
What we did
The approach.
- 01
Unified the event stream
We built an event-driven data layer that brought acquiring, settlement and dispute events into a single real-time stream, replacing the overnight batch reconciliation.
- 02
Scored transactions in-flight
Owned-weights fraud models scored transactions as they happened, with an evaluation harness that let risk tune thresholds against labelled outcomes rather than guesswork.
- 03
Made it auditable
Every score carries a traceable explanation and audit trail, so disputes and regulators can see exactly why a transaction was flagged.
- 04
Handed it back
The client's own data team now owns the pipeline, the models and the eval harness, with runbooks and training transferred at close.
The outcome
What changed.
62%
fewer fraud incidents within 6 months
Real-time
scoring, down from hourly batches
100%
of flags fully auditable
Let's talk
Want an outcome like this?
A senior engineer on the call from day one. Two weeks to a written architecture and a working reference build.