Fraud Detection model build with Python (numpy, scipy, pandas, scikit-learn), based on anonymized credit card transactions. The dataset is publicly available here: https://clouda-labs-assets.s3-us-west-2.amazonaws.com/fraud-detection/creditcard.csv.zip

利用Logistic回归实现信用卡欺诈检测

Implementation of an intelligence system to detect the fraud cases on the basis of classification.

This project utilizes Generative Adversarial Networks (GANs) to tackle the problem of credit card fraud detection. GANs are a powerful deep learning technique that can be used for generating synthetic data, which can be beneficial in situations with imbalanced datasets, such as fraud detection.

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

Credit Card Fraud Detection using machine learning

Credit Card Fraud detection System Using Artificial Intelligence and Machine learning.

Although digital transactions in India registered a 51% growth in 2018-19, their safety remains a concern. Fraudulent activities have increased severalfold, with around 52,304 cases of credit/debit card fraud reported in FY'19 alone. Due to this steep increase in banking frauds, it is the need of the hour to detect these fraudulent transactions in time in order to help consumers as well as banks, who are losing their credit worth each day. Machine learning can play a vital role in detecting fraudulent transactions. Imagine you get a call from your bank, and the customer care executive informs you that your card is about to expire in a week. Immediately, you check your card details and realise that it will expire in the next 8 days. Now, in order to renew your membership, the executive asks you to verify a few details such as your credit card number, the expiry date and the CVV number. Will you share these details with the executive? In such situations, you need to be careful because the details that you might share with them could grant them unhindered access to your credit card account.The aim of this project is to predict fraudulent credit card transactions using machine learning models. The data set that you will be working on during this project was obtained from Kaggle. It contains thousands of individual transactions that took place over a course of two days and their respective labels.

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Developed an advanced GCN-based Credit Card Fraud Detection system using cutting-edge deep learning techniques. Achieved high accuracy in real-time detection and prevention of fraudulent transactions, ensuring robust security and risk mitigation in the finance industry.

Deteccion de fraudes de tarjetas de credito usando Machile Learning implementando distintos algoritmos y haciendo comparaciones de rendimiento con respecto a la clasificacion de transacciones.

The task is to classify credit card transactions as fraudulent or legitimate.

IS525E Data Science for Business — Credit Card Fraud Detection using ML (Finance domain)

Cost-aware credit card fraud detection pipeline: time-based split, probability calibration, and business-aligned threshold tuning (AUPRC-first).

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This project is focused on building a Credit Card Fraud Detection System that utilizes machine learning techniques to detect fraudulent transactions. The project will incorporate MLOps practices to ensure the entire lifecycle of the machine learning model is automated and scalable.

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Repository for completing the Kaggle Credit Card Fraud Detection competition

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Tutorial for credit card fraud detection

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An end to end credit card fraud detection project built with PyTorch on anonymized transaction data, trained under extreme class imbalance and evaluated using PR AUC, ROC AUC, threshold tuning, and top K review queue simulation to mirror real world fraud analyst workflows.

In this project, the goal is to try and identify whether a purchase made was Fraudulent or Legit. (by a stolen credit card information, as well as stolen credit cards). dataset used: https://www.kaggle.com/datasets/kartik2112/fraud-detection?select=fraudTest.csv

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A Credit Card Fraud Detection System based on Machine Learning algorithms

Kaggle Competiton: Using Anomaly detection technique on a highly imbalanced high dimensional data to detect fraud. Used variety of technique including SMOTE to balance the data set and other machine learning technique to build the model.

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Detect credit card fraud with machine learning