credit-card-fraud

A curated list of data mining papers about fraud detection.

Reproducible Machine Learning for Credit Card Fraud Detection - Practical Handbook

iPython notebook and pre-trained model that shows how to build deep Autoencoder in Keras for Anomaly Detection in credit card transactions data

Credit card fraud is a significant problem, with billions of dollars lost each year. Machine learning can be used to detect credit card fraud by identifying patterns that are indicative of fraudulent transactions. Credit card fraud refers to the physical loss of a credit card or the loss of sensitive credit card information.

Analysis of credit card fraud data

Credit Card Fraud Detection Project with Code and Documents

Implementation of feature engineering from Feature engineering strategies for credit card fraud

Anomaly Detection Pipeline with Isolation Forest model and Kedro framework

Classification of fraudulent credit card transactions.

Credit Risk Analysis with Machine Learning

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.

Full Stack Credit Card Fraud Detection Using Machine Learning with Code and Documents Plus Youtube Explanation Video

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

A deep exploration of how human psychology shapes fraud behavior and how those patterns become measurable signals in transaction data. This article reveals the behavioral, cognitive, and economic forces behind fraud, explaining how ML models detect deviations, anomalies, and intent hidden within financial transactions.

A credit card mass checker tool that could check a card's validity based on luhn algorithm.

💳 Creates a new gym environment for credit-card anomaly detection using Deep Q-Networks (DQN) and leverages Open AI's Gym toolkit to allocate appropriate awards to the RL agent.

Anomaly detection with autoencoders — credit-card-fraud case study

Python app for detecting credit card frauds using a graph database

{PySpark, R, Python}: Several Data Science projects