Dataset Overview

Introduction

This dataset presents a synthetic representation of mobile money transactions, meticulously crafted to mirror the complexities of real-world financial activities while integrating fraudulent behaviors for research purposes. Derived from a simulator named PaySim, which utilizes aggregated data from actual financial logs of a mobile money service in an African country, this dataset aims to fill the gap in publicly available financial datasets for fraud detection studies. It encompasses a variety of transaction types including CASH-IN, CASH-OUT, DEBIT, PAYMENT, and TRANSFER over a simulated period of 30 days, providing a comprehensive environment for evaluating fraud detection methodologies. By addressing the intrinsic privacy concerns associated with financial transactions, this dataset offers a unique resource for researchers and analysts in the field of financial security and fraud detection, scaled to 1/4 of the original dataset size for efficient use within the Kaggle platform. Please note that transactions marked as fraudulent have been nullified, emphasizing the importance of non-balance columns for fraud analysis. This dataset is a contribution to the field from the "Scalable resource-efficient systems for big data analytics" project, funded by the Knowledge Foundation in Sweden.

Dataset Details

PaySim synthesizes mobile money transactions using data derived from a month's worth of financial logs from a mobile money service operating in an African country. These logs were provided by a multinational company that offers this financial service across more than 14 countries globally.

This synthetic dataset has been scaled to one-quarter the size of the original dataset and is specifically tailored for Kaggle.

Important Note: Transactions identified as fraudulent are annulled. Hence, for fraud detection analysis, the following columns should not be utilized: oldbalanceOrg, newbalanceOrig, oldbalanceDest, newbalanceDest.

Dataset Structure

• step: Represents a unit of time in the real world, with 1 step equating to 1 hour. The total simulation spans 744 steps, equivalent to 30 days.
• type: Transaction types include CASH-IN, CASH-OUT, DEBIT, PAYMENT, and TRANSFER.
• amount: The transaction amount in the local currency.
• nameOrig: The customer initiating the transaction.
• oldbalanceOrg: The initial balance before the transaction.
• newbalanceOrig: The new balance after the transaction.
• nameDest: The transaction's recipient customer.
• oldbalanceDest: The initial recipient's balance before the transaction. Not applicable for customers identified by 'M' (Merchants).
• newbalanceDest: The new recipient's balance after the transaction. Not applicable for 'M' (Merchants).
• isFraud: Identifies transactions conducted by fraudulent agents aiming to deplete customer accounts through transfers and cash-outs.
• isFlaggedFraud: Flags large-scale, unauthorized transfers between accounts, with any single transaction exceeding 200,000 being considered illegal.

Previous Research and Acknowledgments

This dataset has been generated through multiple runs of the PaySim simulator, each simulating a month of real-time transactions over 744 steps. Each run produced approximately 24 million financial records across the five transaction categories.

This project is part of the "Scalable resource-efficient systems for big data analytics" research, supported by the Knowledge Foundation (grant: 20140032) in Sweden.

For citations and further references, please use:

E. A. Lopez-Rojas, A. Elmir, and S. Axelsson. "PaySim: A financial mobile money simulator for fraud detection". In: The 28th European Modeling and Simulation Symposium-EMSS, Larnaca, Cyprus. 2016