Financial Crime Detection Platform
End-to-end fraud detection system processing real-time transaction streams. From Kafka ingestion through PySpark feature engineering to MLflow-managed models, with autonomous LangChain agents that investigate flagged cases using RAG retrieval over historical fraud patterns.
92.3%
Precision
at 88.7% recall
187ms
p99 Latency
real-time scoring
21,000%
ROI
$210 saved per $1 invested
67%
Automation
case auto-resolution
Ingestion
Feature Engineering
Model Training
Scoring
Investigation
Monitoring
Real-Time Stream Processing
Kafka ingests transaction events at scale. PySpark Structured Streaming applies schema validation, deduplication, and windowed aggregations with exactly-once guarantees.
ML Model Inference
Feature vectors feed into XGBoost models managed by MLflow. Redis caches hot features for sub-200ms scoring. A/B testing compares model versions in production.
Autonomous AI Agents
LangChain agents receive flagged transactions, retrieve similar cases from Chroma vector DB via RAG, apply investigation logic, and auto-resolve 67% of cases without human intervention.
This platform demonstrates end-to-end data engineering and AI capabilities -- from streaming ingestion through model lifecycle management to autonomous investigation.
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