Open source AML and Fraud Detection using Machine Learning for Real-Time Transaction Monitoring

Welcome to an open-source transaction monitoring engine! This product is designed to simplify the definition and management of business rules while also offering a scalable infrastructure for rule execution and backtesting.

⚡ Real-time fraud & anomaly detection system for streaming transactions. Built with Kafka Streams + Isolation Forest ML. Low-latency processing, online learning, and scalable architecture for detecting fraud patterns in transaction data. 🚨🔍

End-to-end KYC/AML compliance data analysis using mock datasets. Includes customer risk scoring, suspicious transaction flagging, and compliance reporting in Python (Pandas, Matplotlib).

Full-stack digital payments compliance engine—UPI/RTGS simulator, AML rule engine, ML-based STR detection, SHAP explainability, and audit-ready dashboards.

False-Positive Reduction Lab : rule-based transaction monitoring with threshold tuning and cost trade-offs. Demonstrates how adjusting detection rules reduces noise, lowers investigation cost, and improves fraud catch.

An AI-powered fraud detection system that uses machine learning to detect suspicious financial transactions in real time. Features include interactive dashboards, secure authentication, and comprehensive reporting for fintech risk analysis.

Portfolio Project: AI-driven financial transaction risk detection using automation workflows and real-time model scoring.

ML model developed using European credit card transaction data to identify suspicious activities.

High-performance blockchain monitoring service supporting Ethereum, BSC, and Bitcoin with real-time wallet tracking and multi-chain architecture

Name Matching ML model for entity resolution and transaction monitoring

A full-stack fintech case resolution system with multi-agent automation for fraud detection, dispute management, and automated actions with explainable traces and observability.

Rules based KYC Risk Scoring Dashboard -SQL and PowerBI. Automates customer classification into Low/Medium/High risk tiers using onboarding data.

Real-Time Insights into Transaction Activity with Scalable Streaming and Analysis.