Initial PayGuard fraud detection system implementation

- 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]>

models/__init__.py

@@ -0,0 +1,56 @@
+"""
+Model definitions for PayGuard fraud detection
+"""
+
+import numpy as np
+
+class Layer1MockModel:
+    """Fast, lightweight model for initial fraud scoring"""
+    
+    def predict_proba(self, features):
+        """
+        Layer 1: Fast model
+        Score < 0.4: Approve
+        Score >= 0.4: Escalate to Layer 2
+        """
+        amount = features[0][0]
+        avg_amount = features[0][1]
+        
+        if avg_amount == 0:
+            ratio = 1.0
+        else:
+            ratio = amount / avg_amount
+        
+        fraud_prob = min(0.95, max(0.05, (ratio - 1.0) * 0.2))
+        legit_prob = 1.0 - fraud_prob
+        
+        return np.array([[legit_prob, fraud_prob]])
+
+class Layer2MockModel:
+    """Deep, complex model for suspicious transactions"""
+    
+    def predict_proba(self, features):
+        """
+        Layer 2: Deep model
+        Score <= 0.7: Approve
+        Score > 0.7: Block
+        """
+        amount = features[0][0]
+        avg_amount = features[0][1]
+        tx_count = features[0][2]
+        
+        fraud_prob = 0.1
+        
+        if avg_amount > 0:
+            ratio = amount / avg_amount
+            fraud_prob += (ratio - 1.0) * 0.25
+        
+        if tx_count < 3:
+            fraud_prob += 0.2
+        elif tx_count < 10:
+            fraud_prob += 0.1
+        
+        fraud_prob = min(0.95, max(0.05, fraud_prob))
+        legit_prob = 1.0 - fraud_prob
+        
+        return np.array([[legit_prob, fraud_prob]])