Detecting fraud in real-time with machine learning
Australian fintech company | Finance | 5 months
The challenge
The client's existing rule-based fraud detection system had high false positive rates and couldn't adapt to new fraud patterns. This resulted in poor customer experience and significant fraud losses.
Our solution
We implemented a machine learning-based fraud detection system that processes transactions in real-time, using ensemble models to identify suspicious patterns while learning from new fraud attempts.
Results
- ✓60% reduction in fraud losses
- ✓40% decrease in false positives
- ✓Sub-second detection latency
- ✓Continuous learning from new patterns
Technologies used
PythonApache KafkaRedisAWS
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