Advanced Fraud Detection System

System Architecture

A comprehensive fraud detection system that combines multiple detection strategies with real-time processing and automated response mechanisms.

Core Components

1. Real-time Transaction Monitor

class TransactionMonitor:
    def __init__(self, config: Dict[str, Any]):
        self.stream_processor = KafkaStreamProcessor(
            config['kafka_config']
        )
        self.feature_extractor = RealTimeFeatureExtractor(
            config['feature_config']
        )
        self.rules_engine = RulesEngine(config['rules_config'])
        self.alert_manager = AlertManager(config['alert_config'])
        
    async def process_transaction(
        self,
        transaction: Transaction
    ) -> FraudPrediction:
        # Extract features in real-time
        features = await self.feature_extractor.extract_features(
            transaction
        )
        
        # Apply rule-based checks
        rule_violations = await self.rules_engine.check_rules(
            transaction,
            features
        )
        
        if rule_violations:
            await self.alert_manager.send_alert(
                transaction,
                rule_violations
            )
            
        return FraudPrediction(
            transaction_id=transaction.id,
            risk_score=self._calculate_risk_score(features, rule_violations),
            rule_violations=rule_violations
        )

class RealTimeFeatureExtractor:
    def __init__(self, config: Dict[str, Any]):
        self.redis_client = redis.Redis(**config['redis'])
        self.feature_calculators = {
            'velocity': VelocityFeatures(config['velocity']),
            'location': LocationFeatures(config['location']),
            'device': DeviceFeatures(config['device']),
            'network': NetworkFeatures(config['network'])
        }
        
    async def extract_features(
        self,
        transaction: Transaction
    ) -> Dict[str, float]:
        feature_tasks = [
            calculator.calculate(transaction)
            for calculator in self.feature_calculators.values()
        ]
        
        results = await asyncio.gather(*feature_tasks)
        return {k: v for d in results for k, v in d.items()}

2. Machine Learning Pipeline

class FraudDetector:
    def __init__(self, config: Dict[str, Any]):
        self.models = {
            'xgboost': self._load_model('xgboost', config['xgb_config']),
            'lightgbm': self._load_model('lightgbm', config['lgb_config']),
            'neural_net': self._load_model('neural_net', config['nn_config'])
        }
        self.ensemble = ModelEnsemble(config['ensemble_config'])
        self.threshold_optimizer = ThresholdOptimizer(
            config['threshold_config']
        )
        
    async def predict_fraud(
        self,
        features: Dict[str, float]
    ) -> FraudScore:
        # Get predictions from all models
        predictions = {}
        for name, model in self.models.items():
            predictions[name] = await model.predict_proba(features)
            
        # Combine predictions using ensemble
        ensemble_score = self.ensemble.combine_predictions(predictions)
        
        # Apply optimal threshold
        threshold = await self.threshold_optimizer.get_threshold(
            features['transaction_amount']
        )
        
        return FraudScore(
            score=ensemble_score,
            is_fraud=ensemble_score > threshold,
            model_scores=predictions
        )

class ThresholdOptimizer:
    def __init__(self, config: Dict[str, Any]):
        self.thresholds = defaultdict(float)
        self.updater = AsyncThresholdUpdater(
            update_interval=config['update_interval']
        )
        
    async def get_threshold(
        self,
        amount: float
    ) -> float:
        amount_bucket = self._get_amount_bucket(amount)
        return self.thresholds[amount_bucket]
        
    async def update_thresholds(
        self,
        recent_transactions: List[Transaction]
    ) -> None:
        # Calculate optimal thresholds using cost-sensitive analysis
        new_thresholds = await self._optimize_thresholds(
            recent_transactions
        )
        self.thresholds.update(new_thresholds)

3. Behavioral Analysis

class BehavioralAnalyzer:
    def __init__(self, config: Dict[str, Any]):
        self.profile_store = UserProfileStore(config['profile_config'])
        self.sequence_analyzer = SequenceAnalyzer(
            config['sequence_config']
        )
        self.pattern_detector = PatternDetector(
            config['pattern_config']
        )
        
    async def analyze_behavior(
        self,
        user_id: str,
        transaction: Transaction
    ) -> BehaviorScore:
        # Get user profile
        profile = await self.profile_store.get_profile(user_id)
        
        # Analyze transaction sequence
        sequence_score = await self.sequence_analyzer.analyze(
            profile.recent_transactions,
            transaction
        )
        
        # Detect suspicious patterns
        pattern_score = await self.pattern_detector.detect_patterns(
            profile,
            transaction
        )
        
        return BehaviorScore(
            sequence_score=sequence_score,
            pattern_score=pattern_score,
            risk_level=self._calculate_risk_level(
                sequence_score,
                pattern_score
            )
        )

class SequenceAnalyzer:
    def __init__(self, config: Dict[str, Any]):
        self.model = TransformerSequenceModel(
            config['model_config']
        )
        self.tokenizer = TransactionTokenizer(
            config['tokenizer_config']
        )
        
    async def analyze(
        self,
        history: List[Transaction],
        current: Transaction
    ) -> float:
        # Tokenize transaction sequence
        sequence = self.tokenizer.tokenize(history + [current])
        
        # Get sequence probability
        return await self.model.predict_probability(sequence)

4. Response System

class FraudResponseSystem:
    def __init__(self, config: Dict[str, Any]):
        self.action_manager = ActionManager(config['action_config'])
        self.notification_service = NotificationService(
            config['notification_config']
        )
        self.case_manager = CaseManager(config['case_config'])
        
    async def handle_fraud_detection(
        self,
        transaction: Transaction,
        fraud_score: FraudScore,
        behavior_score: BehaviorScore
    ) -> ResponseAction:
        # Determine appropriate action
        action = await self.action_manager.determine_action(
            transaction,
            fraud_score,
            behavior_score
        )
        
        # Execute action
        if action.requires_blocking:
            await self.block_transaction(transaction)
            
        if action.requires_notification:
            await self.notification_service.notify_stakeholders(
                transaction,
                action
            )
            
        if action.requires_investigation:
            await self.case_manager.create_case(
                transaction,
                fraud_score,
                behavior_score
            )
            
        return action

Usage Example

# Initialize fraud detection system
config = {
    'stream_config': {
        'kafka_brokers': ['localhost:9092'],
        'topic': 'transactions'
    },
    'model_config': {
        'xgb_config': {'max_depth': 6, 'n_estimators': 1000},
        'threshold_config': {'update_interval': 3600}
    },
    'behavioral_config': {
        'sequence_length': 10,
        'pattern_window': '24h'
    }
}

fraud_system = FraudDetectionSystem(config)

# Process transaction
transaction = Transaction(
    id='TXN123',
    amount=1000.00,
    merchant='SHOP123',
    timestamp=datetime.now(),
    user_id='USER456',
    device_info={'ip': '1.2.3.4', 'device_id': 'DEV789'}
)

# Get fraud prediction
fraud_prediction = await fraud_system.process_transaction(transaction)

if fraud_prediction.is_fraud:
    response = await fraud_system.handle_fraud_detection(
        transaction,
        fraud_prediction
    )

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