This is an Online Transaction Fraud Detection System (FDS) to detect payment frauds. Made using Django.

Building an online payment fraud detection system using machine learning algorithms. It utilizes three primary classification algorithms - Logistic Regression, Decision Tree, and Random Forest - to analyze and classify transactions as either legitimate or fraudulent.

Led the development of a comprehensive online payment fraud detection system for a financial services company, incorporating features such as real-time transaction monitoring, anomaly detection algorithms, risk scoring models, and automated alert generation.

Machine Learning based Online Payment Fraud Detection System with Flask Deployment

Online Payment Fraud Detection System

🛡️ Online Payment Fraud Detection System using Python, Pandas, Scikit‑Learn & ML models. 📊 Cleaned & processed transaction data, ⚡ applied classification algorithms, and 🎯 delivered accurate fraud detection insights to reduce financial risks & enhance payment security.

Machine Learning-based Online Payment Fraud Detection Web App built using Python, Scikit-learn, XGBoost, and Flask. The system analyzes transaction details like amount, type, and account balances to predict whether a transaction is fraudulent or legitimate, with real-time prediction through a user-friendly interface.

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Online Payment Fraud Detection System in "Machine Learning using python". Now a days every thing is online even payments , so this online payment fraud detection system will help us to detect the frauds . In this detection system i have used "decision tree classifier" algorithm to detect the frauds.

This is a web application created using: Python and ML Concepts. The main aim of this system is identify the real time fraudulent transactions and report them.

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The Online Payments Fraud Detection system leverages machine learning algorithms to analyze large volumes of transaction data, identify suspicious patterns, and classify whether a transaction is fraudulent or legitimate.

Online Payment Fraud Detection using Machine Learning techniques and then Integrating it into the exisiting web based online payment systems

The Online Payments Fraud Detection application is designed to predict fraudulent transactions in online payment systems using advanced machine learning techniques.

Machine Learning-based Online Payment Fraud Detection System using Logistic Regression and Flask for real-time transaction prediction.

Financial Transaction Fraud Detection System is a full-stack web application designed to identify fraudulent activities in various types of financial transactions. The platform features a clean and user-friendly interface that allows users to select the type of fraud detection they need: Online Payment Fraud, Credit Card Fraud, or E-Commerce Fraud.

This repository implements a Transformer-based fraud detection system for online payment transactions, built upon the IEEE-CIS Fraud Detection dataset. The framework combines TimeGAN and Transformer architectures to effectively capture both temporal patterns and anomalous behaviors in financial transactions.

Real-time payment fraud detection system using Kafka, Spark Structured Streaming, and XGBoost. Processes streaming transactions, performs online feature engineering, and serves low-latency fraud risk scores via FastAPI with full monitoring and production-ready architecture.

This is the task where model is build for detecting online payment fraud using machine learning technique i.e Random Forest trained on publicly available financial datasets. The system aims to overcome the limitations of traditional fraud detection.

This project presents a machine learning–based classification system to detect fraudulent transactions in online payments. It leverages multiple classification algorithms and preprocessing techniques to train accurate fraud detection models.

This repository contains the research pdfs and references links for the research that our Team Vajra Dominators did for the Project AI based Online Payment Fraud Detection and Risk Management System .

This project implements an online payment fraud detection system, combining exploratory data analysis (EDA) and classification using a decision tree classifier. The model achieves high accuracy in identifying fraudulent transactions, providing valuable insights for secure online transactions.

“AI/ML-based system for online payment fraud detection using transaction data. Handles imbalanced datasets with SMOTE and advanced models (Logistic Regression, Random Forest, XGBoost). Includes preprocessing, EDA, training, and deployment-ready app with real-time fraud prediction and insights.”

The "Online Payment Fraud Detection" project aims to identify and prevent fraudulent transactions in real-time. By leveraging machine learning models trained on historical transaction data, the system can distinguish between legitimate and suspicious activities.

CardServices is a large-scale online card management system built with ASP.NET Core. It handles card issuance, user management, transactions, and fraud detection. Integrating with payment gateways, it tracks transactions and ensures security and scalability for managing credit, debit, gift, and loyalty cards

The credit card business has increased speedily over the last two decades. Corporations and establishments move components of their business or entire business, towards online services providing e-commerce, data and communications for services for aim of permitting their customers with high potency and accessibility. Regardless of location shoppers will continue to make as they formally did “Over the Desk”. The evolution is a huge step towards potency and accessibility and profitableness of view nevertheless, it additionally has some drawbacks. The is threaten by the bigger vulnerability to threat. The matter with creating business through internet card lie within the fact that neither the card nor the user need to be present at point of sale. Thus, it is impossible for merchandiser to check weather the cardholder is real or not. Payment card fraud is major downside thought the world. Companies loose immense amount of money due to credit card fraud and fraudsters ceaselessly obtain new ways to commit unlawful activities. The good news is that frauds tends to be perpetrated to some patterns which it’s potential to sight such that the fraud. Using information from the credit card issuer, a neural network based fraud detection system was trained on an outsize sample of tagged credit card accounts and tested on holdout account set that consider all the account activities over a resulting two months of periods. The neural network was trained on samples of fraud due to loss, stolen cards, application fraud, counterfeit fraud. As we know Artificial Neural Network works as human brain when trained properly. Yet, it is impossible for ANN to emulate human brain, both depend on small functional unit called Neuron. We have also implemented SOM for accuracy purpose. We tend to discuss the performance of the network and accuracy.

Payment fraud represents a significant and growing issue in the world. With the rise in computing platforms, the scale and diversity of credit card fraud have significantly changed. This is due to the rise in both online transactions and e-commerce platforms. Credit card fraud happens when a credit/debit card or card information is stolen, or even when the fraudster uses the information for his/her personal gains. To control these fraudulent activities, fraud detection systems were introduced. But such systems pose operational challenges because the responsibility of the management and cybersecurity would be uncoordinated sometimes. And moreover, the design of such systems is particularly challenging due to the non-stationary distribution of data. The issue most enterprises face here is the lack of incident data, as there is limited information on smaller attacks as in most cases they are not reported thoroughly. Through this project, we aim to implement and assess the performance of various machine learning models on the dataset to successfully predict fraudulent transactions. Since public data are scarce due to confidentiality, the focus of the project is on predictive performance rather than inference. In this project, we use a rich dataset retrieved from Kaggle that contains 284,807 credit card transactions occurring over two days in Europe. It was collected and also analyzed during a research collaboration of Worldline and the Machine Learning Group (http://mlg.ulb.ac.be) of ULB (Université Libre de Bruxelles) on big data mining and fraud detection. The dataset contains over 31 variables with nearly 284,807 credit card transactions. An important attribute of the dataset is that it has been processed to protect cardholder privacy. Because of privacy concerns, we cannot provide the original features and more background information about the data. This suggests that the data is substantially imbalanced. Positive frauds account for 0.172 percent of total transactions. We only have the following features V1 through V28, which are referred to as the primary components, because it involves confidential data. Aside from that, we've been given time and a transaction amount. Another issue to overcome is the dataset's extreme imbalance. With a large number of non-fraudulent transactions in place, Random Undersampling can be used to reduce the number of non-fraudulent transactions and match it to measure the number of fraudulent transactions.