Fraud Detection in E-Commerce Transactions Using Machine Learning Techniques and Quantum Networks

Abstract

Fraud poses a significant threat across various sectors, with the e-commerce industry being particularly vulnerable based on quantum network. Using quantum networks for detecting fraud in e-commerce transactions has the potential to completely change online security. Quantum networks rely on the principles of quantum mechanics to provide the highest level of security when transmitting data. Companies facilitating online payments gather extensive data on user transactions, leveraging machine learning techniques to differentiate between legitimate and fraudulent activities. To enhance expertise in fraud detection, machine learning methods are employed to identify online payment fraud within e-commerce transactions. The dataset, structured at the transaction level, is analysed to uncover patterns distinguishing fraudulent behaviour from normal transactions. Feature engineering, such as incorporating user-level statistics like mean and standard deviation, aids in pattern recognition—a common practice in models like LGBMs (light gradient boosting machines). Detecting fraud presents a challenge due to the imbalance between fraudulent and non-fraudulent data. The performance of the model is evaluated using metrics such as accuracy and F1 score. The current system employs Bayesian optimization techniques to refine LGBM and XGBoost models. The proposed model aims to identify consumer fraud by analysing purchasing patterns and historical data using machine learning methodologies, specifically adopting a classification approach. Tree-based methods, including tree-based bagging and boosting techniques such as LGBM, XGBoost, CatBoost, and deep learning, are utilized. The synthetic minority over-sampling technique (SMOTE) is used to balance the imbalanced data. The primary aim is to create a reliable fraud detection system that is suited to the e-commerce environment.