Survey Paper on Prediction of Fraud Detection in E-Commerce using Supervised Machine Learning
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Abstract
The rapid growth of e-commerce platforms such as Amazon and Flipkart has significantly increased the risk of fraudulent activities, leading to financial losses and reduced customer trust. This survey paper presents a comprehensive review of fraud detection techniques in e-commerce using supervised machine learning approaches. The study focuses on commonly used classification algorithms such as Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), and Neural Networks, which are trained on labeled datasets to distinguish between legitimate and fraudulent transactions.
The paper analyzes various features used in fraud detection, including transaction amount, user behavior, purchase history, IP address, and device information. It also discusses challenges such as class imbalance, data privacy, and evolving fraud patterns. Different performance evaluation metrics, including accuracy, precision, recall, and F1-score, are compared to assess model effectiveness.
Furthermore, this survey highlights the importance of feature engineering, data preprocessing, and real-time detection systems to improve prediction accuracy. Comparative analysis from recent research studies indicates that ensemble methods, particularly Random Forest and Gradient Boosting, often achieve higher detection rates with lower false positives.
The findings of this paper provide valuable insights into selecting appropriate supervised learning techniques for fraud detection in e-commerce systems. It also suggests future research directions, including hybrid models and deep learning integration, to enhance security and reliability in online transactions.
Article Details
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