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).

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.

A curated list of Financial Crime Compliance (FCC) resources: transaction monitoring, trade surveillance, e-comms surveillance, fraud detection, case management, sanctions screening, KYC/KYB, graph analytics, datasets, regulations and more.

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.

Reproducible FCC synthetic data factory for transaction monitoring - customers, accounts, transactions, alerts, cases - ready for analytics & model testing.

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

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

Customer segmentation for transaction monitoring (FCC synthetic DB). Feature extraction → k-sweep → K-Means → PCA report → grouped permutation importance (sklearn). Reproducible CLI, SQLite-backed.

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.

Cash smurfing (structuring) detector for TM datasets - scans SQLite for sub-threshold CASH/ATM bursts and writes clusters + optional alerts.

Name Matching ML model for entity resolution and transaction monitoring

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.