AI-Powered Financial Services Transformation

# Real-time fraud detection inference pipeline
import asyncio
import json
from typing import Dict, List
import numpy as np
from kafka import KafkaConsumer, KafkaProducer
import xgboost as xgb
import shap

class FraudDetectionService:
    def __init__(self):
        self.model = xgb.Booster()
        self.model.load_model('fraud_detection_model.json')
        self.explainer = shap.TreeExplainer(self.model)
        self.feature_columns = self.load_feature_config()

    async def process_transaction(self, transaction: Dict) -> Dict:
        """Process single transaction for fraud detection"""

        # Feature engineering
        features = self.extract_features(transaction)
        X = np.array([features])

        # Model prediction
        fraud_score = self.model.predict(xgb.DMatrix(X))[0]

        # Generate explanation
        shap_values = self.explainer.shap_values(X)
        explanation = self.generate_explanation(shap_values[0])

        # Decision logic
        decision = "APPROVE" if fraud_score < 0.3 else "REVIEW" if fraud_score < 0.7 else "DECLINE"

        return {
            "transaction_id": transaction["id"],
            "fraud_score": float(fraud_score),
            "decision": decision,
            "explanation": explanation,
            "processing_time_ms": self.get_processing_time()
        }

    def extract_features(self, transaction: Dict) -> List[float]:
        """Extract ML features from transaction data"""
        return [
            transaction["amount"],
            transaction["merchant_category"],
            transaction["time_since_last_transaction"],
            transaction["velocity_1h"],
            transaction["device_fingerprint_risk"],
            # ... additional features
        ]

# Kafka consumer for real-time processing
async def main():
    service = FraudDetectionService()
    consumer = KafkaConsumer('transactions', value_deserializer=lambda x: json.loads(x))
    producer = KafkaProducer(value_serializer=lambda x: json.dumps(x).encode())

    async for message in consumer:
        result = await service.process_transaction(message.value)
        producer.send('fraud_decisions', result)