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robROSE: a robust approach for dealing with imbalanced data in fraud detection

robROSE: a robust approach for dealing with imbalanced data in fraud detection
robROSE: a robust approach for dealing with imbalanced data in fraud detection
A major challenge when trying to detect fraud is that the fraudulent activities form a minority class which make up a very small proportion of the data set. Detecting fraud in an imbalanced data set typically leads to predictions that favor the majority group, causing fraud to remain undetected. We discuss some popular oversampling techniques that solve the problem of imbalanced data by creating synthetic samples that mimic the minority class.
A frequent problem when analyzing real data is the presence of anomalies or outliers. When these atypical observations are present in the data these oversampling techniques are prone to create synthetic samples that distort the detection algorithm and spoil the resulting analysis. A useful tool for anomaly detection is robust statistics, which aims to find the outliers by first fitting the majority of the data and then flagging data observations that deviate from it.
In this paper, we present a robust version of ROSE, called robROSE, which combines several promising approaches to cope simultaneously with the problem of imbalanced data and and the presence of outliers. The proposed method achieves to enhance the presence of the fraud cases while ignoring anomalies.
The good performance or our new sampling technique is illustrated on simulated and real data sets and it is shown that robROSE can provide better insight in the structure of the data. The source code of the robROSE algorithm is made freely available.
1618-2510
841–861
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Hoppner, Sebastiaan
eea1c2cb-4cf0-465c-8dd2-a05944dc2fd3
Ortner, Irene
ce055eb4-ea57-47ba-a02d-c90b2a780fea
Verdonck, Tim
cb5e5679-a267-49c2-b8a0-55a74d926b1d
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Hoppner, Sebastiaan
eea1c2cb-4cf0-465c-8dd2-a05944dc2fd3
Ortner, Irene
ce055eb4-ea57-47ba-a02d-c90b2a780fea
Verdonck, Tim
cb5e5679-a267-49c2-b8a0-55a74d926b1d

Baesens, Bart, Hoppner, Sebastiaan, Ortner, Irene and Verdonck, Tim (2021) robROSE: a robust approach for dealing with imbalanced data in fraud detection. Statistical Methods & Applications, 30, 841–861. (doi:10.1007/s10260-021-00573-7).

Record type: Article

Abstract

A major challenge when trying to detect fraud is that the fraudulent activities form a minority class which make up a very small proportion of the data set. Detecting fraud in an imbalanced data set typically leads to predictions that favor the majority group, causing fraud to remain undetected. We discuss some popular oversampling techniques that solve the problem of imbalanced data by creating synthetic samples that mimic the minority class.
A frequent problem when analyzing real data is the presence of anomalies or outliers. When these atypical observations are present in the data these oversampling techniques are prone to create synthetic samples that distort the detection algorithm and spoil the resulting analysis. A useful tool for anomaly detection is robust statistics, which aims to find the outliers by first fitting the majority of the data and then flagging data observations that deviate from it.
In this paper, we present a robust version of ROSE, called robROSE, which combines several promising approaches to cope simultaneously with the problem of imbalanced data and and the presence of outliers. The proposed method achieves to enhance the presence of the fraud cases while ignoring anomalies.
The good performance or our new sampling technique is illustrated on simulated and real data sets and it is shown that robROSE can provide better insight in the structure of the data. The source code of the robROSE algorithm is made freely available.

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robROSE - Accepted Manuscript
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More information

Accepted/In Press date: 24 May 2021
e-pub ahead of print date: 7 June 2021
Published date: 1 September 2021

Identifiers

Local EPrints ID: 449492
URI: http://eprints.soton.ac.uk/id/eprint/449492
ISSN: 1618-2510
PURE UUID: 7bf058ab-8ed3-4743-8fe3-54b5ead2f26e
ORCID for Bart Baesens: ORCID iD orcid.org/0000-0002-5831-5668

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Date deposited: 03 Jun 2021 16:30
Last modified: 15 Jun 2022 04:02

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Contributors

Author: Bart Baesens ORCID iD
Author: Sebastiaan Hoppner
Author: Irene Ortner
Author: Tim Verdonck

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