A curated list of data mining papers about fraud detection.

Analysis of credit card fraud data

Anomaly Detection Pipeline with Isolation Forest model and Kedro framework

Monotonic Optimal Binning algorithm is a statistical approach to transform continuous variables into optimal and monotonic categorical variables.

An attempt to detect fraud in online transaction in deep neural network using pytorch

Anomaly detection using isolation forest

A complete end-to-end fraud detection system for financial transactions, featuring data pipelines, cost-sensitive ML modeling, explainability with SHAP, threshold optimization, batch scoring, and an interactive Streamlit dashboard. Designed to simulate real-world fintech fraud-risk workflows.

An implementation of a distributed machine learning algorithm using Spark able to identify fraud in credit card transactions

using HMM to detect credit card fraudulent transaction

Our underwriting python module for underwriting credit card accounts. For enterprise partners wanting to do their own underwriting in-house.

AntiCCScam is a Python script that combats credit card fraud by sending fake data to scam websites, disrupting their malicious operations. This tool generates and automates the flooding of scam sites with random credit card information for educational and ethical purposes.

This repository contains an implementation of credit card fault detection using Luhn's algorithm. Luhn's algorithm is a checksum formula used to validate credit card numbers, as well as other identification numbers. The algorithm is based on performing a set of arithmetic operations on the digits of a given number, resulting in a checksum value.

Successful work completed as Intern at CodSoft in September 2023

It Works on Credit card fraud dataset, which is bias where we make it unbaised and We using Adaboost Classifier which give a greater Efficiency of classification .

A complete machine learning project to detect fraudulent credit card transactions. It includes data preprocessing, feature scaling, model training (Logistic Regression), evaluation, and deployment using Streamlit. Built with modular, production-ready code and a simulated dataset for privacy-safe demonstrations.

projects based on machine learning

Credit card fraud detection using concepts of self organizing maps.

Fraud Detection using Autoencoders