Blossom bank plc wants to build a machine learning model that will predict online payment. The aim of this project was to develop a model that will predict online payment fraud. Data was collected and EDA was carried out on the dataset for proper visualization and understanding. ML algorithms were used to train and test the dataset.

A Data Science/Machine Learning Project. According to Bolster , Global Fraud Index (as at June 2022) is at 10,183 and growing. This is high risk to businesses and customers transacting online. This indicates that traditional rules-based methods of detecting and combating fraud are fast becoming less effective. It becomes imperative for stakeholders to develop innovative means to make transacting online as safe as possible. Artificial intelligence provides viable and efficient solutions via Machine Learning models/algorithms. In this project, I trained a fraud detection model to predict online payment fraud using Blossom Bank PLC as case study. Blosssom Bank ( BB PLC) is a multinational financial services group, that offers retail and investment banking, pension management, assets management and payment services, headquartered in London, UK. Blossom Bank wants to build a machine learning model to predict online payment fraud. Here is the dataset used for this task. With this model, BB PLC will: Keep up with fast evolving technological threats and better prevent the loss of funds (profit) to fraudsters. Accurately detect and identify anomalies in managing online transactions done on its platforms which may go undetected using traditional rules-based methods. 3.Improve quality assurance thus retaining old customers and acquire new ones. This will increase credit/profit base. Improve its policy and decision making. Steps: 1.Loading necessary python libraries. Loading Dataset. Exploratory Data Analysis. Higlighting Relationships and insights. Data Transformation; Using resampling techniques to address Class-imbalace.. Feature Engineering. Model Training. Model Evaluation. Challenges: I encountered a number of challenges during coding which made me run into error reports. these were due to improper documentations, syntax, especially during feature engineering (one-hot encoding: 'fit.transform'). This aspect consumed most of my time I was able to solve these challenges by making extensive research and paying close attention to syntax. I was able to selve the encoding by using 'pd.get_dummies() and making some specifications in the methods.

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.

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Due to the ongoing pandemic, there is a sudden boom in the E-commerce industry and hence many online sites have increased the online payment modes, increasing the risk for online frauds. Increase in fraud rates, researchers started using different machine learning methods to detect and analyse frauds in online transactions. The main aim of the project is to design and develop a fraud detection model based on the given Dataset.

Kotak pay is a new payment gateway implemented to provide seamless protection and ease the online payment method. It is far more protected than any other payment gateway as it uses Machine learning algorithms to verify the user leading to a high level fraud detection.

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.

Online Payment Fraud Detection using Machine Learning analyzes transaction data to identify suspicious patterns and detect fraudulent activities in real time.

Task of online payments fraud detection with machine learning using Python and SQL.Detecting online payment frauds is one of the applications of data science in finance.

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

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Online Payment Fraud Detection using Machine Learning and Flask

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Online Payment Fraud Detection using Machine learning

Online Payments Fraud Detection using Machine Learning models

To Analysis the online payment fraud detection using Machine Learning Algorithm

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.

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 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.

🔍 Online Payments Fraud Detection using Machine Learning An end-to-end ML project that detects fraudulent transactions using real-world payment data. Features a high-performance XGBoost classifier, and a Flask-powered web app for real-time fraud prediction. Fully modular, scalable, and built for production-ready deployment.

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.

Online Payments Fraud Detection using Machine Learning is a system that detects fraudulent digital transactions by analyzing payment data such as amount, time, location, and user behavior. It uses ML models to identify suspicious activity, reduce fraud risks, and improve online payment security in real-time.

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