When social networks meet payment: a security perspective

Abstract

In the big data arena, opportunities and challenges are mixed. The volume of data in the financial institution is proliferating, which imposes a challenge to big data analytics to ensure safety during each transaction. Moreover, as more and more social networking sites (SNS) are integrating an inbuilt online payment system into their domain, an exponential surge in financial scams is expected in the upcoming days. These scenarios are alarming, and with the rapid growth in the daily addition of new end users and their increasing time spent on SNS, the situations become more vulnerable. With the existing trend of data mobilizations and rapid increase in volume, variety, and velocity of data being produced, big data has a significant role in detecting fraud incidents in financial transactions. However, in the framework of present followed international standards, there is a voluntary compliance obligation on domestic governing bodies, which is a significant source for such voluminous financial frauds on SNS. In order to strengthen the enforcement of international standards to combat financial transactions on SNS, the paper proposes that domestic legislation should comply with international standards with the further addition of machine learning encircled by domestic banking legislation. Eventually, this could solve the security and privacy governance difficulties arising from these financial frauds over SNS. We believe that with our approach of three-layer security i.e. by international standards, domestic legislation, and machine learning, the finan-cial fraud arising due to the SNS payment system will be reduced to a larger extent.