⚡ <100ms
Score per transaction
🧮 0
Data leakage
📡 Real-time
Feature engineering
📈 Streamlit
Live monitor
Tech Stack
PythonStreamlitscikit-learnPandasFastAPIDuckDBPlotly
Before / After
Before
Before
Batch fraud scoringTransactions
↓
Nightly batch
↓
Stale features
↓
Fraud already happened
×
Features get computed after the fact, so the score is always late.
×
Training quietly leaks future data — the model looks great until prod.
×
No live monitor to watch fraud patterns form.
×
By the time the batch flags it, the money is gone.
After
After
Streaming fraud engineTransaction
↓
Live features
↓
Score <100ms
↓
Catch in the act
✓
Time Machine GuardPoint-in-time correctness means features only ever see the past — zero future-data leakage into training.
✓
Catch-in-the-Act ScoringEach transaction scored in under 100ms, with the feature contributions that flagged it.
✓
Pattern RadarStreamlit dashboard surfaces fraud clusters, score drift, and detection rate as they form.
✓
Streaming FeaturesVelocity + behavioral signals computed live on the event stream, not in last night's job.