Python & GCP

High-Volume Transaction Engine

A scalable Python/GCP engine capable of handling >50M transactions daily with integrated gradient boosting models for real-time fraud detection.

CLOUD-NATIVE ARCHITECTURE

Built on GCP with auto-scaling microservices, Pub/Sub messaging, and BigQuery for analytics at scale.

ML-POWERED FRAUD DETECTION

Gradient boosting models trained on historical transaction data, reducing fraud losses by 23%.

REAL-TIME MONITORING

Comprehensive observability with custom dashboards, alerts, and anomaly detection systems.

System Architecture

This high-throughput transaction processing system was designed to handle peak loads of over 1,000 transactions per second with sub-100ms latency. The architecture leverages GCP's managed services to ensure reliability and scalability.

Core Technologies

  • Python FastAPI microservices
  • Google Kubernetes Engine (GKE)
  • Cloud Pub/Sub for event streaming
  • BigQuery for data warehousing
  • XGBoost for ML models

Performance Metrics

  • 50M+ daily transactions processed
  • 99.99% system availability
  • P95 latency under 100ms
  • 23% reduction in fraud losses
  • Horizontal scaling to handle 3x spikes

System Architecture

API Gateway

Transaction Service

Fraud Detection

Event Stream

Data Warehouse

Simplified architecture diagram of the transaction processing system

Sample Code Snippet

# Transaction processing with fraud detection
from fastapi import FastAPI, BackgroundTasks
from google.cloud import pubsub_v1
import xgboost as xgb
import asyncio

app = FastAPI()
publisher = pubsub_v1.PublisherClient()
fraud_model = xgb.Booster()
fraud_model.load_model("gs://models/fraud_detection_v3.model")

@app.post("/api/transactions")
async def process_transaction(transaction: Transaction, background_tasks: BackgroundTasks):
    # Enrich transaction data
    enriched_tx = enrich_transaction_data(transaction)
    
    # Real-time fraud score prediction
    fraud_score = predict_fraud_score(enriched_tx)
    
    # Process based on risk level
    if fraud_score > 0.85:
        background_tasks.add_task(trigger_manual_review, enriched_tx)
        return {"status": "pending_review", "tx_id": enriched_tx.id}
    
    # Publish to event stream for async processing
    background_tasks.add_task(publish_transaction_event, enriched_tx)
    
    return {"status": "approved", "tx_id": enriched_tx.id, "score": fraud_score}

def predict_fraud_score(tx_data):
    # Convert transaction to DMatrix format
    features = prepare_features(tx_data)
    dmatrix = xgb.DMatrix(features)
    
    # Get prediction from model
    return float(fraud_model.predict(dmatrix)[0])