Anomaly detection in car-booking graphs

Oleksandr Shchur; Aleksandar Bojchevski; Mohamed Farghal; Stephan Gunnemann; Yusuf Saber

doi:10.1109/ICDMW.2018.00093

Anomaly detection in car-booking graphs

Oleksandr Shchur
, Aleksandar Bojchevski
, Mohamed Farghal
, Stephan Gunnemann
, Yusuf Saber

Informatics 26 - Chair of Professorship of Data Analytics and Machine Learning

Technical University of Munich
Careem

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Scopus citations

Abstract

The use of car-booking services has gained massive popularity in the recent years-which led to an increasing number of fraudsters that try to game these systems. In this paper we describe a framework for fraud detection in car-booking systems. Our core idea lies in casting this problem as an instance of anomaly detection in temporal graphs. Specifically, we use unsupervised techniques, such as dense subblock discovery, to detect suspicious activity. The proposed framework is able to adapt to the variations in the data inherent to the car-booking setting, and detects fraud with high precision. This work is performed in collaboration with Careem, where the described framework is currently being deployed in production.

Original language	English
Title of host publication	Proceedings - 18th IEEE International Conference on Data Mining Workshops, ICDMW 2018
Editors	Hanghang Tong, Zhenhui Li, Feida Zhu, Jeffrey Yu
Publisher	IEEE Computer Society
Pages	604-607
Number of pages	4
ISBN (Electronic)	9781538692882
DOIs	https://doi.org/10.1109/ICDMW.2018.00093
State	Published - 2 Jul 2018
Event	18th IEEE International Conference on Data Mining Workshops, ICDMW 2018 - Singapore, Singapore Duration: 17 Nov 2018 → 20 Nov 2018

Publication series

Name	IEEE International Conference on Data Mining Workshops, ICDMW
Volume	2018-November
ISSN (Print)	2375-9232
ISSN (Electronic)	2375-9259

Conference

Conference	18th IEEE International Conference on Data Mining Workshops, ICDMW 2018
Country/Territory	Singapore
City	Singapore
Period	17/11/18 → 20/11/18

Keywords

anomaly detection
car booking systems
tensor decomposition

Access to Document

10.1109/ICDMW.2018.00093

Cite this

Shchur, O., Bojchevski, A., Farghal, M., Gunnemann, S., & Saber, Y. (2018). Anomaly detection in car-booking graphs. In H. Tong, Z. Li, F. Zhu, & J. Yu (Eds.), Proceedings - 18th IEEE International Conference on Data Mining Workshops, ICDMW 2018 (pp. 604-607). Article 8637361 (IEEE International Conference on Data Mining Workshops, ICDMW; Vol. 2018-November). IEEE Computer Society. https://doi.org/10.1109/ICDMW.2018.00093

@inproceedings{554653f842494a269b1dbd9ed447f914,

title = "Anomaly detection in car-booking graphs",

abstract = "The use of car-booking services has gained massive popularity in the recent years-which led to an increasing number of fraudsters that try to game these systems. In this paper we describe a framework for fraud detection in car-booking systems. Our core idea lies in casting this problem as an instance of anomaly detection in temporal graphs. Specifically, we use unsupervised techniques, such as dense subblock discovery, to detect suspicious activity. The proposed framework is able to adapt to the variations in the data inherent to the car-booking setting, and detects fraud with high precision. This work is performed in collaboration with Careem, where the described framework is currently being deployed in production.",

keywords = "anomaly detection, car booking systems, tensor decomposition",

author = "Oleksandr Shchur and Aleksandar Bojchevski and Mohamed Farghal and Stephan Gunnemann and Yusuf Saber",

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year = "2018",

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language = "English",

series = "IEEE International Conference on Data Mining Workshops, ICDMW",

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Shchur, O, Bojchevski, A, Farghal, M, Gunnemann, S & Saber, Y 2018, Anomaly detection in car-booking graphs. in H Tong, Z Li, F Zhu & J Yu (eds), Proceedings - 18th IEEE International Conference on Data Mining Workshops, ICDMW 2018., 8637361, IEEE International Conference on Data Mining Workshops, ICDMW, vol. 2018-November, IEEE Computer Society, pp. 604-607, 18th IEEE International Conference on Data Mining Workshops, ICDMW 2018, Singapore, Singapore, 17/11/18. https://doi.org/10.1109/ICDMW.2018.00093

Anomaly detection in car-booking graphs. / Shchur, Oleksandr; Bojchevski, Aleksandar; Farghal, Mohamed et al.
Proceedings - 18th IEEE International Conference on Data Mining Workshops, ICDMW 2018. ed. / Hanghang Tong; Zhenhui Li; Feida Zhu; Jeffrey Yu. IEEE Computer Society, 2018. p. 604-607 8637361 (IEEE International Conference on Data Mining Workshops, ICDMW; Vol. 2018-November).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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PY - 2018/7/2

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N2 - The use of car-booking services has gained massive popularity in the recent years-which led to an increasing number of fraudsters that try to game these systems. In this paper we describe a framework for fraud detection in car-booking systems. Our core idea lies in casting this problem as an instance of anomaly detection in temporal graphs. Specifically, we use unsupervised techniques, such as dense subblock discovery, to detect suspicious activity. The proposed framework is able to adapt to the variations in the data inherent to the car-booking setting, and detects fraud with high precision. This work is performed in collaboration with Careem, where the described framework is currently being deployed in production.

AB - The use of car-booking services has gained massive popularity in the recent years-which led to an increasing number of fraudsters that try to game these systems. In this paper we describe a framework for fraud detection in car-booking systems. Our core idea lies in casting this problem as an instance of anomaly detection in temporal graphs. Specifically, we use unsupervised techniques, such as dense subblock discovery, to detect suspicious activity. The proposed framework is able to adapt to the variations in the data inherent to the car-booking setting, and detects fraud with high precision. This work is performed in collaboration with Careem, where the described framework is currently being deployed in production.

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KW - tensor decomposition

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M3 - Conference contribution

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ER -

Anomaly detection in car-booking graphs

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