Mule Trace

Abstract

In the current cyber digital era, financial fraud has evolved into a sophisticated threat that often bypasses conventional detection systems. Fraudsters exploit fake accounts and unregulated payment gateways, making it challenging for legacy systems to keep up. To address these modern threats, Mule Trace offers an intelligent and real-time fraud detection framework. It extends its capabilities by integrating blockchain technology, specifically Ethereum, for logging suspicious activities, ensuring transparency and immutability of flagged data. Utilizing machine learning models such as Isolation Forest and Gaussian Mixture Models (GMMs), Mule Trace is capable of identifying irregularities in financial transactions with improved accuracy and minimal false positives. The platform operates in real time through a Web3.js interface, removing reliance on centralized systems and enhancing system resilience. Coupled with a React.js dashboard, users can visualize transactions, detect anomalies, and respond promptly to threats. Mule Trace thus provides a robust, scalable solution for modern financial institutions to combat illicit financial behaviors.