Leveraging Artificial Intelligence and Machine Learning for Real-Time Fraud Detection in E-Commerce Transactions
DOI:
https://doi.org/10.29070/dkvj8906Keywords:
Artificial Intelligence, Machine Learning, Fraud Detection, E-Commerce Transactions, Random Forest, SMOTENC, SHAP Analysis, Class Imbalance, Cybersecurity, Predictive AnalyticsAbstract
With more and more people making purchases online, fraud detection has become an important issue for online marketplaces due to the explosion of e-commerce. However, traditional approaches often are inadequate for catching more sophisticated and emerging fraudulent activities in real-time. The aim of this research is to investigate the effectiveness of ML and AI techniques in real-time detection of online shopping fraud. The number of 50,000 records were selected from marketplaces using stratified sampling to ensure representative sampling of classes. Data preparation involved dealing with missing data, removing duplicate data, address outliers, scaling features, and encoding categorical data for analysis. To overcome the problem of class imbalance, the use of SMOTENC, SMOTENC + ENN, & SMOTENC + Tomek Links approaches were performed. Numerous ML classifiers, such as Random Forest & Stochastic Gradient, were tested. The model was assessed for several parameters such as recall, accuracy, precision, F1 score, and AUC-ROC. Random Forest (RF) out performed all the other classifiers in both balanced and unbalanced datasets and Stochastic Gradient (SG) performed the next best. The most important factors that go into fraud detection judgements were determined via SHAP analysis. The study highlights potential opportunities for trust in e-commerce platforms, risk mitigations in terms of financial losses, and enhanced transaction security through AI-driven fraud detection.
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