The Challenge
Digital payments platforms relying on rule-based fraud systems face a fundamental tradeoff: rules tuned to catch fraud also block 3–5% of legitimate transactions (costing customers and revenue), while sophisticated fraud patterns — especially coordinated fraud rings — remain completely invisible to static rules.
Our Solution
We build real-time ML fraud detection systems that score every transaction in under 80ms using behavioural signals, graph relationships between accounts, and anomaly detection — replacing static rule engines with adaptive models that improve continuously as new patterns emerge.
What We Built
- Analyse transaction history to identify fraud patterns invisible to rule-based systems
- Build graph neural networks modelling relationships between accounts, devices, and payment flows to detect coordinated fraud rings
- Train XGBoost ensembles on 200+ behavioural and contextual features per transaction
- Deploy behind a sub-80ms API endpoint capable of handling hundreds of transactions per second at peak
- Build an explainability layer so compliance teams understand every flagged transaction — essential for regulatory audits
Results: Before vs After
| Metric | Before | After | Change |
|---|---|---|---|
| Fraud loss reduction | Rule-based, high losses | 80–90% reduction typical | ↓ 85%+ |
| False positive rate | 3–5% legitimate blocked | < 0.5% | ↓ 90% |
| Detection latency | Manual review: hours | < 80ms per transaction | Real-time |
| Fraud ring detection | 0 (undetectable by rules) | Fully detectable | ↑ New capability |
| Payback period | — | Typically weeks to 3 months | High ROI |
Timeline
Typically 12 weeks including compliance review
Technologies
“A well-built ML fraud system doesn't just reduce losses — it gives your compliance team explainable decisions, unlocks fraud ring detection that rules can never see, and improves automatically as your transaction patterns evolve.”
What this means for your payments platform
Could this be your business?
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