Detecting Online Game Malicious Chargeback by using k-NN

It has been estimated that the global gaming market is worth nearly US$150 billion. Its consumer chargeback services often end up being used by some online gamers as a tool to commit fraud, causing a huge adverse impact on the industry. A gaming company in Taiwan found itself falling victim of malicious chargeback fraud. Nearly NT$10 million of fraudulent chargebacks were made during the period from January to April 2019 alone, making a huge dent in the revenue of the company. To counter chargeback fraud, some gaming companies resorted to manually checking for and blocking malicious accounts of their users, incurring huge labor cost in the process. Manual checking might have alleviated the problems to some extent; however, when new games came online, gaming companies would see a surge of malicious chargebacks, causing subsequent exponential increases in losses. To help reduce labor cost incurred by manual account checking, potential human errors and potential losses that may be caused by malicious chargebacks, this study proposed a k-NN model to detect malicious chargebacks by analysing online gamers' transactional records and gameplay data. The numbers of times and the amounts of prepayment, the numbers of times of chargebacks, and the times of the transactions that the gamers of our study gaming company made were used as characteristics for our k-NN model. The use of these characteristics enabled us to score a minimum of 0.81 in F1-Measure. In addition, three SMOTE (Synthetic Minority Over-sampling Technique) sampling methods were used to deal with the imbalance data provided by our study company and improve the F1-Measure of our proposed k-NN model (scoring up to 0.89 in our experiments). It is hoped that the use of our k-NN model can help reduce potential losses of online gaming companies that may be caused by malicious chargeback fraud, deter to malicious gamers against illegal gains, and prevent the online gaming ecosystem from being sabotaged by malicious chargebacks.