用于欺诈检测的综合金融数据集

用于欺诈检测的综合金融数据集

原文：

Synthetic Financial Datasets For Fraud Detection

Synthetic datasets generated by the PaySim mobile money simulator

Context

There is a lack of public available datasets on financial services and specially in the emerging mobile money transactions domain. Financial datasets are important to many researchers and in particular to us performing research in the domain of fraud detection. Part of the problem is the intrinsically private nature of financial transactions, that leads to no publicly available datasets.

We present a synthetic dataset generated using the simulator called PaySim as an approach to such a problem. PaySim uses aggregated data from the private dataset to generate a synthetic dataset that resembles the normal operation of transactions and injects malicious behaviour to later evaluate the performance of fraud detection methods.

Content

PaySim simulates mobile money transactions based on a sample of real transactions extracted from one month of financial logs from a mobile money service implemented in an African country. The original logs were provided by a multinational company, who is the provider of the mobile financial service which is currently running in more than 14 countries all around the world.

This synthetic dataset is scaled down 1/4 of the original dataset and it is created just for Kaggle.

Headers

This is a sample of 1 row with headers explanation:

1,PAYMENT,1060.31,C429214117,1089.0,28.69,M1591654462,0.0,0.0,0,0

step - maps a unit of time in the real world. In this case 1 step is 1 hour of time. Total steps 744 (30 days simulation).

type - CASH-IN, CASH-OUT, DEBIT, PAYMENT and TRANSFER.

amount -

amount of the transaction in local currency.

nameOrig - customer who started the transaction

oldbalanceOrg - initial balance before the transaction

newbalanceOrig - new balance after the transaction

nameDest - customer who is the recipient of the transaction

oldbalanceDest - initial balance recipient before the transaction. Note that there is not information for customers that start with M (Merchants).

newbalanceDest - new balance recipient after the transaction. Note that there is not information for customers that start with M (Merchants).

isFraud - This is the transactions made by the fraudulent agents inside the simulation. In this specific dataset the fraudulent behavior of the agents aims to profit by taking control or customers accounts and try to empty the funds by transferring to another account and then cashing out of the system.

isFlaggedFraud - The business model aims to control massive transfers from one account to another and flags illegal attempts. An illegal attempt in this dataset is an attempt to transfer more than 200.000 in a single transaction.

译文：

用于欺诈检测的综合金融数据集

PaySim移动货币模拟器生成的合成数据集

内容：

缺乏关于金融服务的公共数据集，特别是在新兴的移动货币交易领域。金融数据集对许多研究人员，特别是对我们在欺诈检测领域进行研究非常重要。问题的一部分在于金融交易本质上的私有性，这导致没有公开可用的数据集。

我们提出了一个使用名为PaySim的模拟器生成的合成数据集，作为解决此类问题的方法。PaySim使用来自私有数据集的聚合数据生成一个类似于交易正常操作的合成数据集，并注入恶意行为，以便稍后评估欺诈检测方法的性能。

PaySim根据从非洲国家实施的移动货币服务的一个月财务日志中提取的真实交易样本模拟移动货币交易。原始日志由一家跨国公司提供，该公司是移动金融服务的提供商，目前在全球14多个国家运营。

该合成数据集的比例缩小为原始数据集的1/4，并且仅为Kaggle创建。

这是一个带有标题说明的1行示例：

1，付款，1060.31，C4292141171089.0,28.69，M1591654462,0.0,0.0,0,0

step-映射真实世界中的时间单位。在这种情况下，一步是一小时的时间。总步骤744（30天模拟）。

type-现金输入、现金输出、借记、付款和转账。

amount-以当地货币表示的交易金额。

nameOrig-启动交易的客户

oldbalanceOrg-交易前的初始余额

NewBalanceOrg-交易后的新余额

nameDest-作为交易接收方的客户

oldbalanceDest-交易前的初始余额接收人。请注意，没有以M（商家）开头的客户信息。

NewBalanceTest-交易后的新余额接收人。请注意，没有以M（商家）开头的客户信息。

isFraud-这是模拟中欺诈代理进行的交易。在这个特定的数据集中，代理人的欺诈行为旨在通过控制或客户账户获利，并试图通过转移到另一个账户然后从系统中套现来清空资金。

isFlaggedFraud——该业务模式旨在控制从一个帐户到另一个帐户的大规模转账，并标记非法尝试。此数据集中的非法尝试是试图在单个事务中传输超过200.000的数据。

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