Real-time Fraud Detection at Scale
Implemented a sub-second response fraud detection system processing 10M+ daily transactions.
Client
FinBank Global
Industry
Fintech
Duration
6 Months
Services
2 Core
The Challenge
FinBank was experiencing a high volume of sophisticated fraudulent transactions. Their existing batch-based detection system was too slow, leading to significant financial losses and customer dissatisfaction.
The Solution
We architected a real-time streaming pipeline using Kafka and Spark Streaming. We deployed custom ML models for anomaly detection and integrated them into the transaction flow. A vector database was used to store and quickly retrieve historical fraud patterns for context-aware scoring.
The Impact
The new system reduced fraudulent losses by 60% in the first quarter. False positives were reduced by 40%, significantly improving the customer experience. The system maintained a 99.99% uptime during peak loads.
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