Credit Card Fraud Detection

Context
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It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase.

Content
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The dataset contains transactions made by credit cards in September 2013 by European cardholders.
This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions.

It contains only numerical input variables which are the result of a PCA transformation. Unfortunately, due to confidentiality issues, we cannot provide the original features and more background information about the data. Features V1, V2, ... V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA are 'Time' and 'Amount'. Feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-sensitive learning. Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise.

Given the class imbalance ratio, we recommend measuring the accuracy using the Area Under the Precision-Recall Curve (AUPRC). Confusion matrix accuracy is not meaningful for unbalanced classification.

Update (03/05/2021)
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A simulator for transaction data has been released as part of the practical handbook on Machine Learning for Credit Card Fraud Detection - https://fraud-detection-handbook.github.io/fraud-detection-handbook/Chapter_3_GettingStarted/SimulatedDataset.html. We invite all practitioners interested in fraud detection datasets to also check out this data simulator, and the methodologies for credit card fraud detection presented in the book.

Acknowledgements
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The dataset has been collected and analysed during a research collaboration of Worldline and the Machine Learning Group (http://mlg.ulb.ac.be) of ULB (Université Libre de Bruxelles) on big data mining and fraud detection.
More details on current and past projects on related topics are available on [https://www.researchgate.net/project/Fraud-detection-5][1] and the page of the [DefeatFraud][2] project

Please cite the following works:

Andrea Dal Pozzolo, Olivier Caelen, Reid A. Johnson and Gianluca Bontempi. [Calibrating Probability with Undersampling for Unbalanced Classification.][3] In Symposium on Computational Intelligence and Data Mining (CIDM), IEEE, 2015

Dal Pozzolo, Andrea; Caelen, Olivier; Le Borgne, Yann-Ael; Waterschoot, Serge; Bontempi, Gianluca. [Learned lessons in credit card fraud detection from a practitioner perspective][4], Expert systems with applications,41,10,4915-4928,2014, Pergamon

Dal Pozzolo, Andrea; Boracchi, Giacomo; Caelen, Olivier; Alippi, Cesare; Bontempi, Gianluca. [Credit card fraud detection: a realistic modeling and a novel learning strategy,][5] IEEE transactions on neural networks and learning systems,29,8,3784-3797,2018,IEEE

Dal Pozzolo, Andrea [Adaptive Machine learning for credit card fraud detection][6] ULB MLG PhD thesis (supervised by G. Bontempi)

Carcillo, Fabrizio; Dal Pozzolo, Andrea; Le Borgne, Yann-Aël; Caelen, Olivier; Mazzer, Yannis; Bontempi, Gianluca. [Scarff: a scalable framework for streaming credit card fraud detection with Spark][7], Information fusion,41, 182-194,2018,Elsevier

Carcillo, Fabrizio; Le Borgne, Yann-Aël; Caelen, Olivier; Bontempi, Gianluca. [Streaming active learning strategies for real-life credit card fraud detection: assessment and visualization,][8] International Journal of Data Science and Analytics, 5,4,285-300,2018,Springer International Publishing

Bertrand Lebichot, Yann-Aël Le Borgne, Liyun He, Frederic Oblé, Gianluca Bontempi [Deep-Learning Domain Adaptation Techniques for Credit Cards Fraud Detection](https://www.researchgate.net/publication/332180999_Deep-Learning_Domain_Adaptation_Techniques_for_Credit_Cards_Fraud_Detection), INNSBDDL 2019: Recent Advances in Big Data and Deep Learning, pp 78-88, 2019

Fabrizio Carcillo, Yann-Aël Le Borgne, Olivier Caelen, Frederic Oblé, Gianluca Bontempi [Combining Unsupervised and Supervised Learning in Credit Card Fraud Detection ](https://www.researchgate.net/publication/333143698_Combining_Unsupervised_and_Supervised_Learning_in_Credit_Card_Fraud_Detection) Information Sciences, 2019

Yann-Aël Le Borgne, Gianluca Bontempi [Reproducible machine Learning for Credit Card Fraud Detection - Practical Handbook ](https://www.researchgate.net/publication/351283764_Machine_Learning_for_Credit_Card_Fraud_Detection_-_Practical_Handbook)

Bertrand Lebichot, Gianmarco Paldino, Wissam Siblini, Liyun He, Frederic Oblé, Gianluca Bontempi [Incremental learning strategies for credit cards fraud detection](https://www.researchgate.net/publication/352275169_Incremental_learning_strategies_for_credit_cards_fraud_detection), IInternational Journal of Data Science and Analytics

[1]: https://www.researchgate.net/project/Fraud-detection-5
[2]: https://mlg.ulb.ac.be/wordpress/portfolio_page/defeatfraud-assessment-and-validation-of-deep-feature-engineering-and-learning-solutions-for-fraud-detection/
[3]: https://www.researchgate.net/publication/283349138_Calibrating_Probability_with_Undersampling_for_Unbalanced_Classification
[4]: https://www.researchgate.net/publication/260837261_Learned_lessons_in_credit_card_fraud_detection_from_a_practitioner_perspective
[5]: https://www.researchgate.net/publication/319867396_Credit_Card_Fraud_Detection_A_Realistic_Modeling_and_a_Novel_Learning_Strategy
[6]: http://di.ulb.ac.be/map/adalpozz/pdf/Dalpozzolo2015PhD.pdf
[7]: https://www.researchgate.net/publication/319616537_SCARFF_a_Scalable_Framework_for_Streaming_Credit_Card_Fraud_Detection_with_Spark

[8]: https://www.researchgate.net/publication/332180999_Deep-Learning_Domain_Adaptation_Techniques_for_Credit_Cards_Fraud_Detection

在当前语境下，信用卡公司识别欺诈性信用卡交易的能力至关重要，以确保客户不会为其未购买的物品支付费用。

数据集内容

本数据集收录了2013年9月欧洲信用卡持卡人的信用卡交易记录。该数据集展示了两天内发生的交易，其中包含492起欺诈交易，占总交易量的284,807笔中的0.172%。数据集呈现出高度的不平衡性，其中正类（欺诈）仅占所有交易的0.172%。

数据集仅包含经过主成分分析（PCA）转换的数值型输入变量。遗憾的是，由于保密问题，我们无法提供原始特征和更多关于数据的背景信息。特征V1、V2、...、V28是通过PCA获得的特征，唯一未经过PCA转换的特征为'Time'和'Amount'。特征'Time'表示每次交易与数据集中第一次交易之间的秒数。特征'Amount'代表交易金额，该特征可用于示例依赖的成本敏感学习。特征'Class'为响应变量，欺诈事件时取值为1，否则为0。

鉴于类别不平衡比率，我们建议使用精确率-召回率曲线下面积（AUPRC）来衡量准确性。对于不平衡分类，混淆矩阵的准确性不具有实际意义。

更新（2021年3月5日）

作为《信用卡欺诈检测机器学习实用手册》的一部分，已发布一个交易数据模拟器 - https://fraud-detection-handbook.github.io/fraud-detection-handbook/Chapter_3_GettingStarted/SimulatedDataset.html。我们诚挚邀请所有对欺诈检测数据集感兴趣的从业者查看此数据模拟器，以及书中介绍的信用卡欺诈检测方法。

致谢

该数据集由Worldline公司与布鲁塞尔自由大学（ULB）机器学习小组（http://mlg.ulb.ac.be）在大数据挖掘和欺诈检测方面的研究合作期间收集和分析。

有关当前和过去相关项目的更多详细信息，请参阅[https://www.researchgate.net/project/Fraud-detection-5][1]和[DefeatFraud][2]项目页面。

请引用以下著作：

Andrea Dal Pozzolo, Olivier Caelen, Reid A. Johnson 和 Gianluca Bontempi. [Calibrating Probability with Undersampling for Unbalanced Classification.][3] 在计算智能与数据挖掘研讨会（CIDM）上，IEEE，2015

Dal Pozzolo, Andrea; Caelen, Olivier; Le Borgne, Yann-Ael; Waterschoot, Serge; Bontempi, Gianluca. [Learned lessons in credit card fraud detection from a practitioner perspective][4], Expert systems with applications, 41, 10, 4915-4928, 2014, Pergamon

Dal Pozzolo, Andrea; Boracchi, Giacomo; Caelen, Olivier; Alippi, Cesare; Bontempi, Gianluca. [Credit card fraud detection: a realistic modeling and a novel learning strategy,][5] IEEE transactions on neural networks and learning systems, 29, 8, 3784-3797, 2018, IEEE

Dal Pozzolo, Andrea [Adaptive Machine learning for credit card fraud detection][6] ULB MLG PhD thesis (supervised by G. Bontempi)

Carcillo, Fabrizio; Dal Pozzolo, Andrea; Le Borgne, Yann-Aël; Caelen, Olivier; Mazzer, Yannis; Bontempi, Gianluca. [Scarff: a scalable framework for streaming credit card fraud detection with Spark][7], Information fusion, 41, 182-194, 2018, Elsevier

Carcillo, Fabrizio; Le Borgne, Yann-Aël; Caelen, Olivier; Bontempi, Gianluca. [Streaming active learning strategies for real-life credit card fraud detection: assessment and visualization,][8] International Journal of Data Science and Analytics, 5, 4, 285-300, 2018, Springer International Publishing

Bertrand Lebichot, Yann-Aël Le Borgne, Liyun He, Frederic Oblé, Gianluca Bontempi [Deep-Learning Domain Adaptation Techniques for Credit Cards Fraud Detection](https://www.researchgate.net/publication/332180999_Deep-Learning_Domain_Adaptation_Techniques_for_Credit_Cards_Fraud_Detection), INNSBDDL 2019: Recent Advances in Big Data and Deep Learning, pp 78-88, 2019

Fabrizio Carcillo, Yann-Aël Le Borgne, Olivier Caelen, Frederic Oblé, Gianluca Bontempi [Combining Unsupervised and Supervised Learning in Credit Card Fraud Detection ](https://www.researchgate.net/publication/333143698_Combining_Unsupervised_and_Supervised_Learning_in_Credit_Card_Fraud_Detection) Information Sciences, 2019

Yann-Aël Le Borgne, Gianluca Bontempi [Reproducible machine Learning for Credit Card Fraud Detection - Practical Handbook ](https://www.researchgate.net/publication/351283764_Machine_Learning_for_Credit_Card_Fraud_Detection_-_Practical_Handbook)

Bertrand Lebichot, Gianmarco Paldino, Wissam Siblini, Liyun He, Frederic Oblé, Gianluca Bontempi [Incremental learning strategies for credit cards fraud detection](https://www.researchgate.net/publication/352275169_Incremental_learning_strategies_for_credit_cards_fraud_detection), International Journal of Data Science and Analytics

[1]: https://www.researchgate.net/project/Fraud-detection-5

[2]: https://mlg.ulb.ac.be/wordpress/portfolio_page/defeatfraud-assessment-and-validation-of-deep-feature-engineering-and-learning-solutions-for-fraud-detection/

[3]: https://www.researchgate.net/publication/283349138_Calibrating_Probability_with_Undersampling_for_Unbalanced_Classification

[4]: https://www.researchgate.net/publication/260837261_Learned_lessons_in_credit_card_fraud_detection_from_a_practitioner_perspective

[5]: https://www.researchgate.net/publication/319867396_Credit_Card_Fraud_Detection_A_Realistic_Modeling_and_a_Novel_Learning_Strategy

[6]: http://di.ulb.ac.be/map/adalpozz/pdf/Dalpozzolo2015PhD.pdf

[7]: https://www.researchgate.net/publication/319616537_SCARFF_a_Scalable_Framework_for_Streaming_Credit_Card_Fraud_Detection_with_Spark

[8]: https://www.researchgate.net/publication/332180999_Deep-Learning_Domain_Adaptation_Techniques_for_Credit_Cards_Fraud_Detection