John Patrick TeruelandCopilot
aa4dd497e2
- Complete dual-layer architecture for fraud detection - Phase 1: Docker Compose infrastructure (Kafka, Zookeeper, Redis) - Phase 2: Mock ML models (Layer 1 fast, Layer 2 deep) - Phase 3: FastAPI transaction ingestion with JWT auth - Phase 4: Kafka consumer pipeline with feature extraction - Phase 5: Test transaction producer with demo scenarios - Phase 6: Clean code structure and comprehensive documentation - Phase 7: End-to-end validation and demo readiness Demonstrates key system design patterns: - Microservices architecture with decoupled components - Asynchronous event processing via Kafka - Tiered decision-making (fast path vs deep analysis) - Resilience patterns (circuit breaker for graceful degradation) - Behavioral profiling with Redis caching Ready for academic demonstration with clear pipeline observability. Co-authored-by: Copilot <[email protected]>
57 lines
1.4 KiB
Python
57 lines
1.4 KiB
Python
"""
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Model definitions for PayGuard fraud detection
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"""
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import numpy as np
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class Layer1MockModel:
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"""Fast, lightweight model for initial fraud scoring"""
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def predict_proba(self, features):
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"""
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Layer 1: Fast model
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Score < 0.4: Approve
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Score >= 0.4: Escalate to Layer 2
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"""
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amount = features[0][0]
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avg_amount = features[0][1]
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if avg_amount == 0:
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ratio = 1.0
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else:
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ratio = amount / avg_amount
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fraud_prob = min(0.95, max(0.05, (ratio - 1.0) * 0.2))
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legit_prob = 1.0 - fraud_prob
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return np.array([[legit_prob, fraud_prob]])
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class Layer2MockModel:
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"""Deep, complex model for suspicious transactions"""
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def predict_proba(self, features):
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"""
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Layer 2: Deep model
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Score <= 0.7: Approve
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Score > 0.7: Block
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"""
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amount = features[0][0]
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avg_amount = features[0][1]
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tx_count = features[0][2]
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fraud_prob = 0.1
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if avg_amount > 0:
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ratio = amount / avg_amount
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fraud_prob += (ratio - 1.0) * 0.25
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if tx_count < 3:
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fraud_prob += 0.2
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elif tx_count < 10:
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fraud_prob += 0.1
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fraud_prob = min(0.95, max(0.05, fraud_prob))
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legit_prob = 1.0 - fraud_prob
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return np.array([[legit_prob, fraud_prob]])
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