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The Fraud Dataset Benchmark (FDB) is a compilation of publicly available datasets relevant to fraud detection . The FDB aims to cover a wide variety of fraud detection tasks, ranging from card not present transaction fraud, bot attacks, malicious traffic, loan risk and content moderation.
This project aims to detect credit card fraud using various machine learning techniques. It explores the Credit Card Fraud Detection dataset, handles imbalanced data, trains models, and evaluates their performance. The project also investigates the impact of outlier removal on model accuracy.
This repository provides TensorFlow source code for building and training credit card fraud models using an LSTM and a GRU. The models included in this repository are multi-layer LSTM or GRU models that analyze time series data to predict whether a credit card transaction is fraudulent. The models ...
Credit card fraud detection is one of the most important issues for credit card companies to deal with in order to earn trust from its customers. As machine learning techniques are robust to many tackle classification problems settings such as image recognition, we aim to explore various machine learning classification algorithms on this ...
This project aims to build a model to detect fraudulent credit card transactions in real-time. The dataset used in this project contains transactions made by credit cards in September 2023 by European cardholders. Credit card fraud is a significant probblem in the financial industry. Detecting ...
This project aims to build a machine learning model to detect fraudulent credit card transactions. Using historical transaction data, the model identifies suspicious activity, helping to prevent potential fraud. The dataset used for this project is from Kaggle. It contains transactions made by ...
This repository contains the code for a Credit Card Fraud Detection project using a highly unbalanced Kaggle dataset of 284,807 transactions with only 492 frauds. To address the imbalance the project implements voting classifier and a neural network with focal loss in PyTorch, achieving an F1-score of 0.86 and PR_AUC of 0.85 for the positive class.
Credit card fraud detection project using machine learning. Utilizing Kaggle data and Python with Scikit-learn, we apply LOF and Isolation Forest algorithms, aiming for high precision and recall to aid financial institutions and customers in fraud prevention.
Credit card fraud is a significant challenge in the financial industry, leading to substantial financial losses every year. By utilizing an Artificial Neural Network (ANN), this project aims to accurately predict fraudulent transactions based on historical transaction data.
Recent trends show a steep rise in credit card fraud, which can be costly to consumers, taxpayers, banks, and payment networks that issue refunds to consumers alike. According to FTC reports, Credit card fraud tops the identity theft reports from Q2 2017 until Q3 2020. Reports of credit card fraud increased by 107% from Q1 2019 to Q4 2020 ...