Extracting and reasoning about implicit behavioral evidences for detecting fraudulent online transactions in e-Commerce

With the explosive growth of e-Commerce worldwide, there are also growing concerns about collusive fraudulent transaction attacks in e-Commerce. The main contribution of our research work is the design of a novel detection framework that can reason about implicit online user behavior for detecting collusive fraudulent transactions. Based on real transactional and user behavioral data collected from one of the largest e-Commerce platforms in the world, our experimental results confirm that the proposed detection framework can achieve an average true positive detection rate of 83% while the false alarm rate is kept at as low as 2.4%. To the best of our knowledge, this is one of the largest scale studies toward the detection of fraudulent transactions in e-Commerce. The managerial implication of our study is that administrators of e-Commerce platforms can apply our framework to detect and prevent fraudulent transaction attacks, and hence fair electronic trading is upheld in the ever expanding e-Commerce world.