This repository contains my online payment fraud detection project using Python

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

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.

https://avdhesh-varshney.github.io/online-payment-fraud-detection-app/

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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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.

Online Payments Fraud Detection with Machine Learning Algorithms

This project is aimed at detecting online fraud using machine learning techniques. We've developed a model that can classify transactions as either fraudulent or legitimate

This project focuses on detecting fraudulent online payment transactions using machine learning, specifically leveraging a Random Forest Classifier enhanced with SMOTE (Synthetic Minority Over-sampling Technique) to handle class imbalance.

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Online Payment Fraud Detection

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This project applies machine learning techniques to classify transactions as fraudulent or legitimate using a dataset of over 6 million records. It includes data preprocessing, handling class imbalance, and training multiple models to enhance fraud detection effectiveness.

Blossom Bank also known as BB PLC is a multinational financial services group, that offers retail and investment banking, pension, management, asset management and payments services, headquartered in London, UK.

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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.

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.

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 System with Flask Deployment

A minor project on detecting online payment fraud using ML techniques

Online Payment/Transaction Fraud detection model built with Azure AutoML

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Online Payment Fraud Detection System

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Fraud Detection Model_DecisionTree

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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. For this, we need a dataset containing information about online payment fraud, so that we can understand what type of transactions lead to fraud.