AbstractMobile Money Transfer (MMT) is a fast growing medium of making financial transactions via a mobile device. It is increasingly becoming adopted in growing markets especially in developing countries. The ability of Mobile Money Transfer services (MMT)
to handle large number of small value payments worldwide funds exchange in digital currencies and lack of oversight makes it an attractive target for attackers and fraudsters. Although the
risks inherent in all payments channels exist in the mobile money payment environment. The usage of mobile money transfer technologies introduces additional risks caused by the large
number of non-bank participants, higher speed of transactions and level of anonymity compared to mobile banking and mobile commerce systems. This provides motivation for detecting and preventing fraudulent mobile money transactions in mobile payment systems.
The main objective of this thesis is to investigate and propose a pattern recognition model to predict fraud in Mobile money transfer transactions. To this end, a novel pattern recognition model has been proposed from the find- ings of this thesis. Also, synthetic mobile money transfer transaction dataset was simulated with possible different fraud scenario(s) to explore. The applicability of the proposed pattern recognition model was evaluated using the simulation dataset. From the results of the experiments, a promising recognition performance was achieved. The results also provide the ranking of clusters of transaction neighbours for new cases which may operate as an effective tool for experts to develop preliminary insight into suspicious transactions which can then be investigated in more detail.
|Date of Award||Jun 2018|
|Supervisor||Stelios Kapetanakis (Supervisor), Miltiadis Petridis (Supervisor), Manos Panaousis (Supervisor) & Dr. G Samakovitis (Supervisor)|