1. Executive Summary

The AI Fraud Detection System project delivers a comprehensive machine learning platform for real-time fraud identification in transactional systems. It ingests data from various sources, applies feature engineering and a hybrid ML model using XGBoost and PyTorch autoencoders, and deploys on AWS SageMaker with API integration. The solution achieves >95% accuracy, handles up to 1M transactions/day, ensures low-latency predictions, and complies with data privacy standards, providing scalable fraud mitigation for clients.

2. Architecture Overview

The architecture follows an end-to-end flow: data is ingested via ETL pipelines into PostgreSQL, transformed with feature engineering and balancing in the processing layer, trained using hybrid supervised/unsupervised models, and deployed on AWS SageMaker for inference. FastAPI serves predictions via REST endpoints, with monitoring via CloudWatch and Model Monitor, ensuring scalability, security, and integration with client systems.

3. Technology Stack

The system uses Python 3.10+ for development, PyTorch for autoencoders, XGBoost for gradient boosting, AWS SageMaker for training and deployment, and PostgreSQL for warehousing. Additional libraries include Pandas, NumPy, Scikit-learn, and Boto3; tools like Git, Docker, and FastAPI support version control, containerization, and API serving.

4. Data Model and Features

The data model includes raw transaction storage in PostgreSQL with a star schema (facts: transactions; dimensions: users, locations). Feature engineering focuses on velocity, frequency, geo-risk (using Haversine), amount deviation, and reconstruction errors from autoencoders. SMOTE balances classes, enabling anomaly detection and classification for robust fraud identification.

5. ETL Processing

ETL pipelines extract from CSV, APIs, and databases using Pandas; transform with cleaning, feature computation, scaling, and SMOTE; and load into PostgreSQL with indexing and partitioning. Orchestrated via Python scripts or AWS Lambda, with error retries and logging, ensuring data quality, schema validation, and daily backups for reliable warehousing.

6. Project Timeline (10 Months)

• 📅 Month 1-1.5: Planning (Define scope, gather requirements, architecture).
• 📅 Month 1.5-3.5: Data Preparation (Implement ETL, setup PostgreSQL).
• 📅 Month 3.5-6: Model Development (Engineer features, build hybrid model).
• 📅 Month 6-8: Deployment (Deploy on SageMaker, integrate APIs).
• 📅 Month 8-10: Optimization & Go-Live (UAT, optimize, rollout).

7. Testing & Deployment

Testing includes unit (Pytest), integration, performance (latency <100ms), and A/B comparisons. Deployment on SageMaker uses estimators for training and predictors for endpoints, with blue-green strategy for cutover, data capture for monitoring, and rollback via endpoint switching if issues arise.

8. Monitoring & Maintenance

Post-deployment, monitor model drift with SageMaker Model Monitor, logs in CloudWatch, and alerts via PagerDuty. Maintain >99% uptime, quarterly retraining, monthly patches, and cost controls under allocated budget, with proactive dashboards for anomaly detection and performance.