Embedded AI Fraud Detection for Digital Bank
Detecting 94% of fraudulent transactions in real-time with embedded AI models
The Challenge
A growing Australian digital bank was experiencing a sharp increase in sophisticated fraud attempts as its customer base scaled past 800,000 accounts. Existing rule-based detection was catching only 68% of fraudulent transactions while generating excessive false positives that degraded customer experience.
Our Approach
We designed and embedded a multi-layered fraud detection AI directly into the bank's transaction processing pipeline. The system combines real-time behavioural analysis, device fingerprinting, and anomaly detection models trained on Australian transaction patterns. A feedback loop from the fraud investigation team continuously improves model accuracy.
The Outcome
Fraud detection rate improved from 68% to 94%. False positive rates dropped by 47%, meaning fewer legitimate transactions were blocked. The system processes over 2 million transactions daily with sub-50ms latency, and estimated fraud losses reduced by $4.2M annually.
Detection Rate
False Positives
Fraud Losses Saved
Technology Stack
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