Cybercrime has astronomically risen with technological advancements alongside the business opportunities in cyberspace. Cybercrime is now viewed as one of the top 10 global risks. In recognition of the threat posed by cybercrime, organisations are investing in controls and countermeasures that would combat the threat of cybercrime and its impact. However, incidences of successful cyberattacks are still on the rise. The advent of mobile devices has created a means of providing financial services to over two billion people globally that hitherto had no access to formal banking services. Also, banks and other financial institutions use mobile platforms as an alternative delivery channel for financial services. However, the dark side of using mobile devices to bridge the banking gap is that mobile devices are now an added vector for cybersecurity threats. This has affected trust in the use of the system and consequently slowed down the uptake of mobile financial services (MFS). This article presents an in-depth analysis of the opportunities mobile platforms provide for the unbanked and how cybersecurity is hampering the uptake of MFS. Furthermore, the article proposes an approach for mitigating cybercrime in the complex MFS ecosystem and presents preliminary results from the research conducted so far.
Advances in mobile computing have presented a huge opportunity to provide Mobile Financial Services (MFS) to half of the world's population who currently do not have access to financial services. However, cybersecurity concerns in the mobile computing ecosystem have slowed down the adoption of MFS. The adoption of MFS is further hampered by the lack of a clear understanding of the interaction between the complex infrastructures and human factors that exist in the ecosystem for Mobile Financial Services Socio-Technical Systems (MFSSTS). This paper presentsthe work in progress of investigating the problem of MFSSTS. It discusses the preliminary results and understanding obtained from using Human Factor approaches to build and analyse the model for MFSSTS.
Abstract-Customer profiles are by definition made up of factual and transactional data. It is often the case that due to reasons such as high cost of data acquisition and/or protection, only the transactional data are available for data mining operations. Transactional data, however, tend to be highly sparse and skewed due to a large proportion of customers engaging in very few transactions. This can result in a bias in the prediction accuracy of classifiers built using them towards the larger proportion of customers with fewer transactions. This paper investigates an approach for accurately and confidently grouping and classifying customers in bins on the basis of the number of their transactions. The experiments we conducted on a highly sparse and skewed real-world transactional data show that our proposed approach can be used to identify a critical point at which customer profiles can be more confidently distinguished.
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