RiskFabric

RiskFabric is a fraud intelligence platform that generates synthetic Indian payment transaction data, processes it through a Medallion ETL pipeline, and produces trained fraud detection models.

✨ Key Features

• Extreme Throughput: Achieves ~182,000 Transactions Per Second (TPS) using a parallelized "One-Pass" architecture.
• Agent-Based Realism: Simulates the full lifecycle of Customers, Accounts, and Cards, with behavioral spend profiles driven by real-world heuristics.
• Geographic Fidelity: Integrates OpenStreetMap (OSM) India data and Uber H3 hexagonal indexing for hyper-realistic spatial spend patterns and location anomalies.
• Sophisticated Fraud Injection: Includes signatures for UPI Scams, Account Takeover (ATO), Card Not Present (CNP) fraud, and coordinated campaigns.
• Medallion Data Architecture: A full pipeline taking data from Bronze (Raw) to Silver (Feature Engineered) to Gold (ML-Ready).
• ML Mastery: Built-in leakage prevention and simulated label noise (False Positives/Negatives) to ensure models are robust and production-ready.

🛠️ Tech Stack

• Core Engine: Rust (Rayon for parallelization, Rand for deterministic simulation).
• Real-time Streaming: Redpanda (Kafka-compatible), rdkafka, and Tokio async runtime.
• Data Processing: Polars 0.51.0 (Lazy API & high-performance transformation).
• Data Warehouse: PostgreSQL (Spatial/OSM staging), ClickHouse (High-volume transactions), and dbt (Analytical enrichment).
• Feature Store: Redis (Low-latency state for real-time Z-scores and behavior).
• Data Ingestion: dlt (Data Load Tool) for MDS integration.
• Machine Learning: Python (XGBoost) with real-time inference via scorer.py.
• Infrastructure: Docker/Podman orchestration with Prometheus and Grafana for observability.

📁 Project Structure

🧠 Core Simulation (src/)

• generators/: Agent-Based Modeling (ABM) logic, entity creation, and fraud mutation engines.
• models/: Rust structures for Customers, Accounts, Cards, and Transactions.
• bin/: CLI binaries for data generation (generate.rs), streaming (stream.rs), and preparation.
• config.rs: Centralized, type-safe configuration engine for simulation parameters.

🥈 ETL & Data Warehouse (src/etl/ & warehouse/)

• etl/: Multi-stage Polars transformation pipeline (Silver/Gold feature engineering).
• warehouse/: dbt project for geographic enrichment and merchant risk profiling using PostGIS.
• dlt/: MDS integration for automated data lake ingestion.

🤖 Machine Learning (src/ml/)

• train_xgboost.py: Training pipeline with Feature sanitization and OOT validation.
• scorer.py: Real-time inference service consuming from Kafka and stateful Redis features.
• seed_redis.py: Point-in-time state synchronization between the warehouse and feature store.

🛠️ Infrastructure & Docs

• docker-compose.yml: Orchestrated local stack (ClickHouse, Postgres, Redpanda, Redis, Grafana).
• documentation/: Arichitectural docs and theory of operation (mdBook).
• data/config/: Behavioral rules and system tuning YAML configurations.

📈 Benchmarks (150k Txns)

Developed by harshafaik