PD-FDS: Purchase Density based Online Credit Card Fraud Detection System

Youngjoon Ki, Ji Won Yoon
Proceedings of the KDD 2017: Workshop on Anomaly Detection in Finance, PMLR 71:76-84, 2018.

Abstract

Credit card fraud detection is an endless war between fraudsters and payment service providers. Indeed, annual global financial loss by credit card frauds has increased. Fraudsters have been organized and systematized, attempting to find weak points of existing fraud detection system (FDS). State-of-the-art FDS approaches utilize already existing fraud cases, which can result in different FDS by payment service providers. Therefore, a new payment service provider may not have room for installing a FDS due to the lack of fraudulent cases. Moreover, credit card transactions contain the legitimate owner’s personal information, which can be exposed to an honest but curious fraud analyst. In this paper, we propose a purchase density based FDS (PD-FDS) that uses three features which are not related to personal information. PD-FDS does not require already existing fraudulent transactions and also shows low false positive rate (<0.01).

Cite this Paper

BibTeX
@InProceedings{pmlr-v71-ki18a, title = {PD-FDS: Purchase Density based Online Credit Card Fraud Detection System}, author = {Ki, Youngjoon and Yoon, Ji Won}, booktitle = {Proceedings of the KDD 2017: Workshop on Anomaly Detection in Finance}, pages = {76--84}, year = {2018}, editor = {Anandakrishnan, Archana and Kumar, Senthil and Statnikov, Alexander and Faruquie, Tanveer and Xu, Di}, volume = {71}, series = {Proceedings of Machine Learning Research}, month = {14 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v71/ki18a/ki18a.pdf}, url = {https://proceedings.mlr.press/v71/ki18a.html}, abstract = {Credit card fraud detection is an endless war between fraudsters and payment service providers. Indeed, annual global financial loss by credit card frauds has increased. Fraudsters have been organized and systematized, attempting to find weak points of existing fraud detection system (FDS). State-of-the-art FDS approaches utilize already existing fraud cases, which can result in different FDS by payment service providers. Therefore, a new payment service provider may not have room for installing a FDS due to the lack of fraudulent cases. Moreover, credit card transactions contain the legitimate owner’s personal information, which can be exposed to an honest but curious fraud analyst. In this paper, we propose a purchase density based FDS (PD-FDS) that uses three features which are not related to personal information. PD-FDS does not require already existing fraudulent transactions and also shows low false positive rate (<0.01).} }
Endnote
%0 Conference Paper %T PD-FDS: Purchase Density based Online Credit Card Fraud Detection System %A Youngjoon Ki %A Ji Won Yoon %B Proceedings of the KDD 2017: Workshop on Anomaly Detection in Finance %C Proceedings of Machine Learning Research %D 2018 %E Archana Anandakrishnan %E Senthil Kumar %E Alexander Statnikov %E Tanveer Faruquie %E Di Xu %F pmlr-v71-ki18a %I PMLR %P 76--84 %U https://proceedings.mlr.press/v71/ki18a.html %V 71 %X Credit card fraud detection is an endless war between fraudsters and payment service providers. Indeed, annual global financial loss by credit card frauds has increased. Fraudsters have been organized and systematized, attempting to find weak points of existing fraud detection system (FDS). State-of-the-art FDS approaches utilize already existing fraud cases, which can result in different FDS by payment service providers. Therefore, a new payment service provider may not have room for installing a FDS due to the lack of fraudulent cases. Moreover, credit card transactions contain the legitimate owner’s personal information, which can be exposed to an honest but curious fraud analyst. In this paper, we propose a purchase density based FDS (PD-FDS) that uses three features which are not related to personal information. PD-FDS does not require already existing fraudulent transactions and also shows low false positive rate (<0.01).
APA
Ki, Y. & Yoon, J.W.. (2018). PD-FDS: Purchase Density based Online Credit Card Fraud Detection System. Proceedings of the KDD 2017: Workshop on Anomaly Detection in Finance, in Proceedings of Machine Learning Research 71:76-84 Available from https://proceedings.mlr.press/v71/ki18a.html.

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