Automatic Fraud Detection in e-Commerce Transactions using Deep Reinforcement Learning and Artificial Neural Networks

Fraud is a serious issue that has plagued e-commerce for many years, and despite significant efforts to combat it, current fraud detection strategies only catch a small portion of fraudulent transactions. This results in substantial financial losses, with billions of dollars being lost each year. Given the expected surge in the volume of online transactions in the upcoming years, there is a critical need for improved fraud detection strategies. To tackle this problem, the article proposes a deep reinforcement learning approach for the automatic detection of fraudulent e-commerce transactions. The architecture's policy is built on the implementation of artificial neural networks (ANNs). The classification problem is viewed as a step-by-step decision-making procedure. The implementation of the model involves the use of the artificial bee colony (ABC) algorithm to acquire initial weight values. After that, in each step, the agent obtains a sample and performs a classification, with the environment providing a reward for each classification action. To encourage the model to concentrate on detecting fraudulent transactions precisely, the reward for identifying the minority class is higher than that for the majority class. With the aid of a supportive learning setting and a specific reward system, the agent ultimately determines the best approach to achieve its objectives. The performance of the suggested model is assessed utilizing a publicly available dataset contributed by the Machine Learning group at the Université Libre de Bruxelles. The experimental outcomes, determined using recognized evaluation measures, indicate that the model has attained a high level of accuracy. As a result, the suggested model is considered appropriate for identifying deceitful transactions in e-commerce.

@article{Tang2023,
  title     = {Automatic Fraud Detection in e-Commerce Transactions using Deep Reinforcement Learning and Artificial Neural Networks},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {7},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Yuanyuan Tang},
  doi       = {10.14569/IJACSA.2023.01407113},
  url       = {https://doi.org/10.14569/IJACSA.2023.01407113}
}