1. ZAFFAR AHMED SHAIKH - Faculty of Computing Science and Information Technology, Benazir Bhutto Shaheed University Lyari,
2. ABDUL QAYOOM - School of Information Engineering, Southwest University of Science and Technology, Mianyang, PR China and Department of Computer Science, Lasbela University of Agriculture Water and Marine Sciences, Uthal, Pakistan.
3. SHAFIQ UR REHMAN - Department of Computer Science, Lasbela University of Agriculture Water and Marine Sciences, Uthal, Pakistan.
4. ABDULLAH AYUB KHAN - Faculty of Computing Science and Information Technology, Benazir Bhutto Shaheed University Lyari, Karachi, Pakistan and Department of Computer Science, Sindh Madressatul Islam University, Karachi, Pakistan.
5. BOCOUM OUSMANE - School of Computer Science and Technology Southwest University of Science and Technology, Mianyang, PR China.
6. SVETLANA V. MAKAR - Institute of Regional Economy and Interbudgetary Relations, Financial University under the Government of the Russian Federation, Moscow, Russia & Department of Physical and Socio-Economic Geography, Federal State Budgetary Educational Institution, National Research Mordovia State University, Saransk, Russia.
7. SERGEY V. SHKODINSKY - Center for Sectoral economics, Financial Research Institute, Moscow, Russia & Laboratory of industrial policy and economic security, Market Economy Institute of RAS, Moscow, Russia.
8. TAISIA V. DIANOVA = Department of Economics, Moscow State Institute of International Relations (MGIMO), Moscow, Russia.
9. PETER V. ALEKSEEV - Institute of Global Economy and International Finance, Financial University under the Government of Russian Federation, Moscow, Russia.
10. ALEXANDER L. CHUPIN - Law Institute, Peoples’ Friendship University of Russia (RUDN University), Moscow, Russia.
Mobile money transfer systems (MMTS) in countries with limited banking are increasingly becoming the mainstream banking system. The analysis of transactions performed on these systems helps to detect fraudulent and criminal activities. This paper introduces a novel visual analytics framework for visual analysis and exploration of mobile money transactions. Our system enables the empirical analysis of mobile money transactions data using multiple views to reveal the temporal, geospatial, and categorical aspects of the transactions. Several challenges were identified related to the given MMT datasets through the process of implementing of fraud detection framework. In addition, as a step towards recognizing the difficult task of developing versatile and flexible fraud detection models, this work proposes concepts to address the identified challenges.
Visual analytics, Fraud detection, Financial data visualization, Mobile money.