Scenario-based Synthetic Dataset Generation for Mobile Money Transactions

Abstract

There is limited availability of mobile money transaction datasets from Sub-Saharan Africa for research because transaction data records are sensitive in nature and therefore raise privacy concerns. This has in turn hindered the potential to study fraudulent patterns in mobile money transactions so as to propose realistic mitigation measures based on Machine Learning Approaches to the prevailing financial fraud challenges in the region. This research presents mobile money scenarios that should be considered in order to implement a simulator that can harness synthetic datasets for mobile money transactions from Sub-Saharan Africa so as to carry out fraud detection research. These scenarios include the definition of a mobile money ecosystem with processes used by actors such as mobile money agents, clients, merchants and banks to interact with each other in mobile money operations. There is also a need for a real mobile money dataset to extract statistical information and diverse fraudulent behaviours of actors and fraud examples in mobile money markets. This research uses the design considerations to examine process-driven techniques such as numerical simulation, agent-based modeling, and data-driven techniques such as neural networks that can be leveraged to generate synthetic datasets for mobile money transactions. Common data generation toolkits like PaySim, AMLSim, RetSim and ABIDES that are based on these techniques have been examined. The design considerations are used to design a realistic model known as MoMTSim based on real mobile money processes and agent-based modeling techniques that can be implemented to generate synthetic transaction datasets for mobile money with fraud instances. This will facilitate fraud detection research. The synthetic datasets eliminate data privacy risks, are easy and faster to obtain, and are cheap to experiment with. With the proposed model, different research groups can move to the implementation stage to realise a model for synthetic data generation for mobile money transactions from the Sub-Saharan region.