Project 4: Integrated Fraud Detection System

Fraud Detection Engine

The core of the system is the fraud detection engine that processes transactions in real-time:

# Fraud detection model
class FraudDetectionModel:
    def __init__(self, threshold: float = 0.7):
        self.threshold = threshold
        self.feature_engineering = FeatureEngineering()
        
    def predict(self, transaction: Dict) -> Tuple[float, bool]:
        """Predict fraud probability for a transaction"""
        # Extract features
        features = self.feature_engineering.process_transaction(transaction)
        
        # In a real system, this would use a trained ML model
        # Here we use a simplified rule-based approach for demonstration
        fraud_score = self._rule_based_score(transaction, features)
        
        # Determine if it's fraudulent based on threshold
        is_fraud = fraud_score > self.threshold
        
        return fraud_score, is_fraud
                    

The model incorporates:

• Feature engineering to extract relevant transaction attributes
• ML-based scoring to determine fraud probability
• Configurable thresholds for fraud determination
• Integration with downstream processing systems

RAG System for Fraud Investigation

The system includes a Retrieval Augmented Generation (RAG) component for fraud pattern recognition:

# RAG system for fraud investigation
class FraudRAGSystem:
    def __init__(self):
        self.vector_db = VectorDatabase()
        self.initialize_knowledge_base()
        
    def initialize_knowledge_base(self):
        """Initialize the knowledge base with fraud patterns"""
        # In a real system, these would be embeddings from a language model
        # Here we use random vectors for demonstration
        
        # Pattern 1: Card testing
        vector1 = np.random.rand(128)
        metadata1 = {
            "pattern": "Card Testing",
            "description": "Multiple small transactions in quick succession to test if a stolen card works",
            "indicators": ["Multiple small transactions", "Different merchants", "Short time window"],
            "mitigation": "Monitor velocity of small transactions across different merchants"
        }
        self.vector_db.add_vector("pattern_1", vector1, metadata1)
        
    def query(self, question: str) -> Dict:
        """Query the RAG system with a natural language question"""
        # In a real system, this would encode the question into a vector using a language model
        # Here we simulate by creating a random vector with some bias based on keywords
        
        query_vector = np.random.rand(128)
        
        # Search for similar patterns
        results = self.vector_db.similarity_search(query_vector, top_k=2)
        
        # Construct response
        response = {
            "question": question,
            "matches": []
        }
        
        for pattern_id, similarity in results:
            metadata = self.vector_db.get_metadata(pattern_id)
            response["matches"].append({
                "pattern": metadata.get("pattern", ""),
                "description": metadata.get("description", ""),
                "indicators": metadata.get("indicators", []),
                "similarity": float(similarity)
            })
            
        return response
                    

The RAG system provides:

• Vector-based storage of fraud patterns and techniques
• Similarity search for finding relevant patterns
• Natural language querying capabilities
• Structured responses with fraud pattern details

Conversational AI for Fraud Analysts

The system includes a conversational interface powered by RASA and AWS Lex:

# Conversational AI handler for fraud investigation
class FraudInvestigationBot:
    def __init__(self):
        self.intent_classifier = IntentClassifier()
        self.rag_system = FraudRAGSystem()
        
    def process_message(self, message: str, context: Dict = None) -> Dict:
        """Process a message from a fraud analyst"""
        if context is None:
            context = {}
            
        # Classify intent
        intent, confidence = self.intent_classifier.classify(message)
        
        # Generate response based on intent
        response = {
            "text": "",
            "intent": intent,
            "confidence": confidence,
            "data": {}
        }
        
        if intent == "fraud_pattern":
            # Query the RAG system for fraud patterns
            rag_response = self.rag_system.query(message)
            
            if rag_response["matches"]:
                top_match = rag_response["matches"][0]
                response["text"] = f"The pattern '{top_match['pattern']}' might be relevant: {top_match['description']}"
                response["data"] = rag_response
            else:
                response["text"] = "I couldn't find any specific fraud patterns matching your query."
                
        # Other intents handled similarly...
            
        return response
                    

The conversational AI component provides:

• Intent classification for analyst queries
• Integration with the RAG system for knowledge retrieval
• Structured responses with actionable information
• Context-aware conversation handling

GenAI Components

The system leverages several generative AI techniques:

1. Retrieval Augmented Generation (RAG):
   • Vector embeddings of fraud patterns and historical cases
   • Semantic search for relevant information retrieval
   • LLM-based response generation for analyst queries
   • Knowledge graph integration for enhanced context
2. Natural Language Understanding:
   • Intent classification for analyst queries
   • Entity extraction for transaction details
   • Sentiment analysis for customer communications
   • Multi-turn dialogue management
3. Explanation Generation:
   • Automatic generation of fraud alert explanations
   • Risk factor analysis and summarization
   • Investigation recommendation generation
   • Customer-friendly explanation creation