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Deep Digging of Anomalous Transactions in Financial Networks with Imbalanced Data

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Deep Learning for Social Media Data Analytics

Part of the book series: Studies in Big Data ((SBD,volume 113))

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Abstract

Anomaly (user or transaction) detection is a major concern and has intrigued a wide spectrum of disciplines, for example, finance, security, etc. Anomalous instances in a dataset are very rare in general, which leads to imbalanced distribution of classes in the dataset, and poses a great challenge to anomaly detection. In the case of credit card transactions, it is of utmost importance to identify fraudulent transactions in order to save financial organizations from giving credit to users that are not likely to be repaid in the future. Most of the prevailing techniques to address the class imbalance problem aim to balance the dataset by either producing synthetic data samples of a minority class or eliminating some samples of the majority class. In this chapter, we provide a brief overview of the challenges faced due to class imbalance while learning deep learning-based prediction models and discuss the sampling strategies that deal with the class imbalance data problem, for example, oversampling, SMOTE, and its variants, and data ensembles. Data ensembles divide the dataset into small subsets that are in itself balanced. Then, corresponding to each balanced sub-dataset, a classifier is trained, and voting is used for the first level of prediction. As deep learning approaches have the ability to map complex non-linear relations within high-dimensional data, they can be leveraged to anomaly detection problems. To improve the classification task’s performance and avoid overfitting, Deep Multilayer Perceptron (DMLP) and Deep Convolutional Neural Network (DCNN) are trained for each balanced sub-dataset, and second level voting is performed. Also, a detailed numerical analysis of the proposed architecture is provided.

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Notes

  1. 1.

    https://www.kaggle.com/mlg-ulb/creditcardfraud.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology, Roorkee, India

    Vaishali Kansal & Pradumn Pandey

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  1. Vaishali Kansal
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Correspondence to Vaishali Kansal .

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Editors and Affiliations

  1. Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

  2. Urban Design and Regional Planning Unit, University of Alicante, Alicante, Spain

    Leticia Serrano-Estrada

  3. Dept of Mathematics & Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands

    Akrati Saxena

  4. Department of Computer Science and Engineering, National Institute Of Technology Silchar, Cachar, Assam, India

    Anupam Biswas

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Kansal, V., Pandey, P. (2022). Deep Digging of Anomalous Transactions in Financial Networks with Imbalanced Data. In: Hong, TP., Serrano-Estrada, L., Saxena, A., Biswas, A. (eds) Deep Learning for Social Media Data Analytics. Studies in Big Data, vol 113. Springer, Cham. https://doi.org/10.1007/978-3-031-10869-3_15

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