Integrate text_processor with fraud_engine - Hybrid scoring (Step 2/5)

fraud_engine.py

@@ -7,6 +7,7 @@ It coordinates multiple agents and produces the final decision: investigate | al
 import json
 from typing import Dict, List, Any
 from datetime import datetime
+from text_processor import InsuranceTextProcessor
 
 
 class FraudEngine:
@@ -15,6 +16,7 @@ class FraudEngine:
     def __init__(self):
         self.version = "1.0.0"
         self.decision_threshold = 0.65
+                self.text_processor = InsuranceTextProcessor()
         
     def process_claim(self, claim_data: Dict[str, Any]) -> Dict[str, Any]:
         """Process a claim and return fraud decision.
@@ -27,12 +29,22 @@ class FraudEngine:
         """
         # Step 1: Feature Engineering
         features = self._engineer_features(claim_data)
+
+                # Step 1.5: Text Analysis (Maysat Method)
+        text_analysis = None
+        if 'claim_description' in claim_data:
+            text_analysis = self.text_processor.analyze_claim_text(claim_data['claim_description'])
         
         # Step 2: Multi-Agent Analysis
         pattern_analysis = self._analyze_patterns(features)
         anomaly_analysis = self._detect_anomalies(features)
-        risk_score = self._calculate_risk_score(pattern_analysis, anomaly_analysis)
-        
+        # Hybrid scoring: 30% Text + 70% Rules
+        rule_score = self._calculate_risk_score(pattern_analysis, anomaly_analysis)
+        if text_analysis:
+            text_score = text_analysis['fraud_score']
+            risk_score = 0.3 * text_score + 0.7 * rule_score
+        else:
+            risk_score = rule_score        
         # Step 3: Decision Logic
         decision = self._make_decision(risk_score)
         
@@ -53,7 +65,9 @@ class FraudEngine:
             "evidence": explainability["evidence"],
             "confidence": explainability["confidence"],
             "audit_id": audit_log["audit_id"],
-            "timestamp": audit_log["timestamp"]
+            "timestamp": audit_log["timestamp"],
+            "text_analysis": text_analysis,
+            "method": "Hybrid (Text + Rules)" if text_analysis else "Rule-based only"
         }
     
     def _engineer_features(self, claim_data: Dict[str, Any]) -> Dict[str, Any]: