Banking & Finance · United Kingdom
Northbridge Bank stops 3x more fraud with real-time ML scoring
Rule-based fraud checks were both missing sophisticated fraud rings and generating false-positive friction for legitimate customers.
✓
3.1x increase in fraud caught
58%
58% reduction in false positives
✓
Sub-200ms scoring latency in production
Before
Existing architecture
Batch rule engine running every four hours against transaction exports.
After
Proposed architecture
A streaming ML scoring pipeline evaluating every transaction in under 200ms, with a feedback loop from fraud analysts back into model retraining.
Machine LearningKafkaPostgreSQLAWS
"We finally trust the model enough to act on it in real time."
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