Mobile money is a service for performing financial transactions using a mobile phone. By law it has to have protection against money laundering and other types of fraud. Research into fraud detection methods is not as advanced as in other similar fields. However, getting access to real world data is difficult, due to the sensitive nature of financial transactions, and this makes research into detection methods difficult. Thus, we propose an approach based on a Multi-Agent Based Simulation (MABS) for the generation of synthetic transaction data. We present the generation of synthetic data logs of transactions and the use of such a data set for the study of different detection scenarios using machine learning.

Vi föreslår en strategi som bygger på en Multi-Agent simulering (MAb) för generering av syntetiska transaktionsdata. Vi presenterar generering av syntetiska dataloggarna i transaktioner och användning av sådana en datamängd för att studera olika upptäckt scenarier med hjälp maskininlärning.