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Fraud Detection Using Sequential Patterns from Credit Card Operations

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Intelligent Computing (SAI 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1228))

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Abstract

This paper presents a novel method for detection of frauds that uses the differences in temporal dependence (sequential patterns) between valid and non-legitimate credit card operations to increase the detection performance. A two-level fusion is proposed from the results of single classifiers. The first fusion is made in low-dimension feature spaces from the card operation record and the second fusion is made to combine the results obtained in each of the low-dimension spaces. It is assumed that sequential patterns are better highlighted in low-dimension feature spaces than in the high-dimension space of all the features of the card operation record. The single classifiers implemented were linear and quadratic discriminant analyses, classification tree, and naive Bayes. Alpha integration was applied to make an optimal combination of the single classifiers. The proposed method was evaluated using a real dataset with a great disproportion between non-legitimate and valid operations. The results were evaluated using the area under the receiver operating characteristic (ROC) curve of each of the single and fused results. We demonstrated that the proposed two-level fusion combining several low-dimension feature analyses outperforms the conventional analysis using the full set of features.

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Acknowledgment

The Generalitat Valenciana supported this work under grant PROMETEO/2019/109.

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Authors and Affiliations

  1. Institute of Telecommunications and Multimedia Applications, Universitat Politècnica de València, Valencia, Spain

    Addisson Salazar, Gonzalo Safont & Luis Vergara

Authors
  1. Addisson Salazar
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  2. Gonzalo Safont
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  3. Luis Vergara
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Corresponding author

Correspondence to Addisson Salazar .

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Editors and Affiliations

  1. Saga University, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Rahul Bhatia

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Salazar, A., Safont, G., Vergara, L. (2020). Fraud Detection Using Sequential Patterns from Credit Card Operations. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Computing. SAI 2020. Advances in Intelligent Systems and Computing, vol 1228. Springer, Cham. https://doi.org/10.1007/978-3-030-52249-0_20

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