Project Goals & Objectives

Summary

The objectives.md document defines the high-level mission and technical milestones for the RiskFabric project. It outlines the strategic intent behind building a high-fidelity synthetic data generator and the specific problems it aims to solve for the financial technology community.

Design Intent

RiskFabric is designed to address the "Data Paradox" in fraud detection: researchers require large volumes of labeled data to develop effective models, but real-world financial data is sensitive and often inaccessible. By creating a high-fidelity, "white-box" alternative, the project provides a safe environment for testing machine learning algorithms and the operational infrastructure required for real-time fraud detection.

A key strategic objective is the promotion of Infrastructure-as-Code for Simulation. Transitioning from static CSV datasets to dynamic, configuration-driven environments allows organizations to "stress-test" systems against hypothetical scenarios—such as doubling transaction volumes—without requiring production data.

🎯 Key Milestones

  1. High-Fidelity Generation: Reaching 180k+ TPS while maintaining spatial and temporal realism.
  2. Streaming Parity: Ensuring models trained on batch data perform consistently in real-time Kafka environments.
  3. Adversarial Diversity: Expanding the fraud library to include multi-stage attacks like money laundering and mule-account networks.

Known Issues

Focus is currently placed on Individual and Coordinated Fraud, but Macroeconomic Factors remain unimplemented. The simulation assumes spending patterns are unaffected by external events such as inflation or holidays. Implementing a "Global Event Engine" is necessary to simulate seasonal surges and economic shifts, providing a more challenging baseline for detection models.

Furthermore, the project lacks Multi-Currency Support. The simulation is anchored to a single base currency, preventing the modeling of international fraud or cross-border remittance scams. Refactoring the transaction engine to handle dynamic currency conversion and exchange-rate fluctuations is required to support global fintech use cases.