Blockchain mechanisms to reduce internet crimes in electronic payment gateways

Electronic payment systems, as one of the main components of electronic commerce, have seen a lot of growth worldwide in recent years, and governments, business owners, and institutions have provided their customers with a wide range of payment options by creating designed systems. Therefore, the purpose of this research is to present a model that can explain blockchain adaptations in order to reduce internet crimes in electronic payment portals.

The applied research was written in a descriptive-correlational survey type, which was completed, analyzed and measured by the branch employees of one of the country's banks using a standard questionnaire. The statistical population of the research is 77 people, according to Cochran's formula, the determined sample size is 64 people, and the result of Cronbach's alpha coefficient test to check the reliability of the questionnaire is 0.82.

Based on Mastercard's theories, the combination of traditional payment networks with blockchain-based networks has improved the security of electronic payment portals and reduced internet crimes, so in this research, in order to investigate the effects of independent research variables (security features, technical protections, ease of use, trust to the seller) on the dependent variables of the research (perceived security, perceived trust and the amount of use of electronic payments) a hypothetical model is designed based on the background of the research and the hypotheses indicate the causal relationships between the variables in the model.

The results show that the use of the new variable and the proposed model, in order to add law enforcement and supervisory institutions to verify the transactions during the transaction in order to prevent the occurrence of crime before the transaction, will improve the technology of electronic payment portals and create security in transactions and in The result will increase users' trust in electronic payment portals.