A labeled synthetic mobile money transaction dataset

IF 1 Q3 MULTIDISCIPLINARY SCIENCES

Data in Brief Pub Date : 2025-04-07 DOI:10.1016/j.dib.2025.111534

Denish Azamuke, Marriette Katarahweire, Engineer Bainomugisha

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引用次数: 0

Abstract

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.

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标记合成移动货币交易数据集

这篇数据文章介绍了一个使用MoMTSim创建的标记合成移动货币交易数据集，MoMTSim是一个专门为移动货币交易设计和验证的基于多代理的仿真平台。MoMTSim工具包模拟移动货币交互，确保生成的合成数据集密切模仿真实交易数据的统计属性。此数据集封装了广泛的事务特征，例如时间戳（步骤）、事务金额、发起者和接收者的初始和新帐户余额、参与者id以及所执行的事务类型。所包含的事务类型包括存款、取款、转账、支付和借记。数据集中的每条记录还带有一个标签，用于标识交易是合法的还是欺诈的。本文描述了使用MoMTSim对该数据集的合成，并给出了数据集的结构和汇总统计。该数据集特别适合训练和测试机器学习算法，以检测金融欺诈。此外，它还具有对欺诈检测算法和系统进行基准测试以及验证合成数据生成方法的潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Data in Brief MULTIDISCIPLINARY SCIENCES-

CiteScore

3.10

自引率

0.00%

发文量

996

审稿时长

70 days

期刊介绍： Data in Brief provides a way for researchers to easily share and reuse each other''s datasets by publishing data articles that: -Thoroughly describe your data, facilitating reproducibility. -Make your data, which is often buried in supplementary material, easier to find. -Increase traffic towards associated research articles and data, leading to more citations. -Open up doors for new collaborations. Because you never know what data will be useful to someone else, Data in Brief welcomes submissions that describe data from all research areas.