A labeled synthetic mobile money transaction dataset

This data article introduces a labeled synthetic mobile money transaction dataset created using MoMTSim, a multi-agent-based simulation platform designed and validated specifically for mobile money transactions. MoMTSim toolkit simulates mobile money interactions, ensuring that the generated synthetic dataset closely mimics the statistical properties of real transaction data. This dataset encapsulates a wide range of transaction features, such as timestamps (step), transaction amounts, the initial and new account balances of both the initiator and recipient, participant IDs, and the types of transactions conducted. The included transaction types span deposits, withdrawals, transfers, payments, and debits. Each record in the dataset also carries a label that identifies whether the transaction is legitimate or fraudulent. The synthesis of this dataset using MoMTSim is described in this article and its structure and summary statistics are also presented. The dataset is particularly suitable for training and testing machine learning algorithms to detect financial fraud. Additionally, it holds the potential for benchmarking fraud detection algorithms and systems and validating synthetic data generation methodologies.