Real-time AI fraud detection
We built a machine-learning fraud detection system for a fintech that needed to flag suspicious transactions before they caused damage, not after.
Project profile
- SectorFintech
- TagsStartup · AI · Cybersecurity
Results
Figures are representative of projects of this kind, not audited results from a specific client.
The challenge
The fintech was catching fraud almost always too late, relying on static rules that experienced attackers learned to dodge, with a team manually reviewing alerts that arrived after the transaction was already settled. Every case missed in time meant direct losses and reputational risk with its own customers.
The solution
We designed a full pipeline: transactional data ingestion and preparation, a machine learning model trained to detect anomalous patterns, an API scoring each transaction in real time, and a monitoring dashboard so the risk team could review and tune the model against real usage data.
Tech stack
- Python
- Machine Learning
- API REST
- Grafana
Results
The system started blocking or flagging most fraud before the transaction completed, sharply cutting losses and freeing the risk team from manually reviewing every low-value alert.
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