Real-Time Analytics for Financial Fraud Detection
Leading Financial Services Provider
Banking & Financial Services
Challenge
Batch-based fraud detection system with hours of delay, leading to significant financial losses and high false positive rates overwhelming investigation teams.
Solution
Architected streaming analytics platform using Apache Kafka, Flink, ScyllaDB, and Neo4j. Integrated real-time ML models for anomaly detection
with sub-100ms latency.
Key Results
45%
Reduction in Fraud Losses
<100ms
Transaction Processing Time
60%
Reduction in False Positives
35%
Improved Customer Satisfaction
- Duration: 12 months
- Team: 6 specialists
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