FraudLabs Pro Fraud Prevention plugin that screen the order transaction for online frauds. Fraud Prevention extension for Magento 1.

Credit card fraud detection using Machine learning model with Data Visualization

IEEE-CIS Fraud Detection with Unsupervised Learning

Training a classification model to classify fraud and non-fraud online transaction using python.

FraudShield leverages a semi-supervised learning framework to detect financial fraud. It combines supervised learning and anomaly detection to showcase effective fraud prevention in a Streamlit app, using the BAF Dataset for real-world scenario simulation.

fraud detection in online transactions menggunakan machine learning classification adalah proses membangun model prediksi fraud berdasarkan fitur-fitur tertentu. Dalam proyek ini, saya mengambil pendekatan end-to-end, mulai dari data mentah, eksperimentasi pemodelan, dan API menggunakan FASTAPI.

🐍 Python · Machine Learning · Logistic Regression · Pandas · Scikit-Learn · Fraud Detection · Random Forest · Data-driven Decision Making 🐍

Quantum-Based Fraud Detection (QBFD) is a quantum algorithm designed to detect fraudulent activities in financial transactions using quantum computing principles.

This repository contains the implementation of Self organising maps for fraud detection.

FraudX is a modular Python engine for detecting fraudulent transactions using configurable rule sets, pluggable logic, and robust validation. Built as a production-grade portfolio project.

💳 End-to-End Credit Card Fraud Detection using Machine Learning | Data preprocessing, SMOTE balancing, model comparison (LogReg, RF, XGBoost, KNN), SHAP explainability, and business insights.

Detecting fraudulent insurance claims using machine learning techniques like XGBoost and SMOTE on real-world data to enhance claim verification accuracy.

Project Lumina is a collection of Fraud Detection algorithms using Graph Neural Networks.

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

End-to-end automated modern ELT data pipeline using Python, PostgreSQL, Airflow, Kafka, GCS, BigQuery, and dbt — built to simulate a production-grade e-commerce environment. Completed as part of Purwadhika Data Engineering Bootcamp Final Project

AI-assisted fraud detection project using PostgreSQL (Neon), SQL rule engine, and Python RandomForest model.

Financial transaction validator with fraud detection, AML/KYC compliance checks, and business rule enforcement

📊 Analyze and detect fake job postings with this cleaned and combined dataset of job ads collected in 2015, ideal for research and development.

Complex Event processor for generating business logic from fuzzy high frequency Real Time Data.Angular JS,Angular Material,MongoDB,Express js,Node js,Highland js,Passport Js,D3 js,Async js

Built LDA and QDA models on variables obtained from Principal Component Analysis (PCA) and Kolmogorov-Smirnov (KS) and tuned by leave-one-out cross-validation (LOOCV) to predict fraudulent online advertising click traffic