@article{4728,
  author = {Hsing-Cheng Liu, Yao-Liang Chung},
  title = {AI-Driven Counterfeit and Fraud Detection in E-Commerce: A Dual-Layered Machine Learning Approach},
  journal = {Journal of Information & Systems Management},
  year = {2026},
  volume = {16},
  number = {2},
  doi = {https://doi.org/10.6025/jism/2026/16/2/37-55},
  url = {https://www.dline.info/jism/fulltext/v16n2/jismv16n2_1.pdf},
  abstract = {The rapid expansion of e-commerce has intensified cybersecurity threats, particularly counterfeit product
listings and fraudulent transactions, which severely undermine consumer trust and marketplace integrity.
Traditional rule based monitoring systems increasingly struggle to detect these sophisticated, evolving
fraud patterns. This study develops and evaluates a dual layer predictive analytics framework that leverages
supervised machine learning to enhance counterfeit detection and fraud governance in digital marketplaces.
Utilizing a Random Forest classifier, the research analyzes two complementary datasets: static productlevel
metadata comprising seller credentials and listing characteristics, and dynamic transaction-level
behavioral indicators capturing purchasing anomalies and device signatures. Following rigorous
preprocessing, categorical encoding, and an 80/20 stratified train-test split, the models were benchmarked
using accuracy, precision, recall, F1 Score, and ROC-AUC. The product-level classifier achieved perfect class
separability, demonstrating the deterministic power of structural listing indicators. Conversely, the
transaction-level model attained 99.33% accuracy, 97.99% precision, and a 0.9999 ROC-AUC, effectively
capturing probabilistic behavioral fraud signatures with minimal false negatives. These findings confirm
that integrating static asset metadata with dynamic transactional analytics significantly improves predictive
robustness. The study concludes by proposing a unified, AI-driven marketplace governance framework
capable of real-time surveillance, automated threat mitigation, and scalable regulatory compliance,
ultimately advancing the deployment of transparent artificial intelligence solutions across global digital
commerce networks.},
}