Scanner, signatures and the largest collection of Magento malware

This research goal is to build binary classifier model which are able to separate fraud transactions from non-fraud transactions.

MER is a software that identifies and highlights manipulative communication in text from human conversations and AI-generated responses. MER benchmarks language models for manipulative expressions, fostering development of transparency and safety in AI. It also supports manipulation victims by detecting manipulative patterns in human communication.

🔍 | 📈 | Life / Health Insurance Fraud Detection | 📋 | (Codeshahstra Round 1 Hackathon)

This solution performs Anomaly Detection with Statistical Modeling on Spark. The detection is based on Z-Score calculated on cpu usage data collected from servers.

Report on the performance of different machine learning algorithms in identifying persons of interest in the Enron Fraud Case

🛡️ SecureCard-AI: A high-performance credit card fraud detection system implemented in a Jupyter Notebook, achieving 99.97% accuracy.

Fraud Detection Research - Data Science Capstone Project at Penn State University, University Park Campus

This repository contains the code components of work carried out for analyzing the Medical Provider Fraud Detection dataset with the intent to find most important features to crack down the potentially fraud providers.

This GitHub repository provides a comprehensive set of tools and algorithms for detecting fraud anomalies in various data sources. Fraudulent activities can have severe consequences, impacting businesses and individuals alike. With this repository, we aim to empower researchers with effective techniques to identify and prevent fraudulent behavior.

Detecting fraud on online customer transactions

This project uses machine learning models like Logistic Regression, Random Forest, and XGBoost to detect fraudulent credit card transactions. It handles class imbalance using SMOTE and visualizes key fraud patterns through an interactive Power BI dashboard.

Test repo for the Smartboard project

Using R Language to predict whether a user will download an app after clicking a mobile app advertisement. Click on the link below to see more details!

The objective of this project is to explore and learn various Machine Learning Algorithm and see how it solves different Business problems. There are various models like Decision tree, Random Forest, Naive Bayes Classifier, linear regression, Logistic regression etc.

The goal of the competition was to predict fraudulent transactions on a dataset with about 40 million instances, with some characteristics similar to the datasets processed by Feedzai.

The objective of this project is to develop a robust classification model capable of identifying and flagging potentially fraudulent job postings on LinkedIn.

Machine learning system for credit card fraud detection using XGBoost with advanced feature engineering

This project implements an end-to-end pipeline for detecting SMS spam using LLM-based embeddings (Mistral), interpretable machine learning, and risk-aware reporting.

This project is a credit card fraud detection system using machine learning and speech recognition to identify fraudulent transactions. It employs a Support Vector Machine (SVM) model to classify transaction types based on clues provided via speech inputs.