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.

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Online Payments Fraud Detection with Machine Learning Algorithms

Machine Learning based Online Payment Fraud Detection System with Flask Deployment

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 Payments Fraud Detection with Machine Learning

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Online payment systems make transactions fast and convenient, but they also increase fraud risks. This project uses machine learning to detect fraudulent transactions using historical payment data. By analyzing transaction type, amount, and balance changes, the model predicts whether a transaction is fraud.

Online Payments Fraud Detection with Machine Learning

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.

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.

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Interactive application built for continuous learning

Built a machine learning model to classify fraudulent vs. legitimate online transactions. By training on a labeled dataset of payment activity, the project identifies patterns commonly associated with fraud. This solution helps in proactively detecting and preventing suspicious financial behavior in real-time.

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Portfolio project showcasing development capabilities

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Hands-on learning experience with modern frameworks

To identify online payment fraud with machine learning, i have train a machine learning model for classifying fraudulent and non-fraudulent payments

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To identify online payment fraud with machine learning, we need to train a machine learning model for classifying fraudulent and non-fraudulent payments.

The introduction of online payment systems has helped a lot in the ease of payments. But, at the same time, it increased in payment frauds. Online payment frauds can happen with anyone using any payment system, especially while making payments using a credit card.

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Online-Payments-Fraud-Detection-with-Machine-Learning The introduction of online payment systems has helped a lot in the ease of payments. But, at the same time, it increased in payment frauds. Online payment frauds can happen with anyone using any payment system, especially while making payments using a credit card.

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