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A Credible Individual Behavior Profiling Method for Online Payment Fraud Detection

Published: 08 July 2021 Publication History

Abstract

Online payment fraud detection relies on a credible characterization of individual behaviors. Existing individual behavior profiling methods have three problems in credibility. Firstly, they solely regard users as individuals to model and fail to credibly characterize the whole behavior patterns of transactions. Secondly, they cannot make full use of the general information among similar individuals due to the heterogeneity of transaction attributes. Thirdly, they are not capable of utilizing the label information of transactions credibly. Faced with these challenges, we propose a credible method for individual behavior profiling that consists of two steps. The first step is the construction of the credible individual behavior profiling framework. To begin with, the concept of individuals is generalized and the credible transaction description is given. Afterwards, based on the co-occurrence information of transaction attributes, the credible behavior condition is defined to set a limit to individual behavior patterns. The second step is the implementation of the framework. For this purpose, the original fraud detection problem is transformed into a pseudo-recommender system problem with a co-occurrence mapping process. In order to guarantee the credible behavior constraint, a credible recommendation algorithm is designed to collaboratively restore both the ranking and rating information of the pseudo-rating matrices. Besides, an embedding based approach is adopted to parameterize the proposed model. Experiments on an online payment transaction dataset demonstrate the effectiveness of our proposed method.

References

[1]
Yang Q, Hu X, Cheng Z, Based Big Data Analysis of Fraud Detection for Online Transaction Orders[C]. international conference on cloud computing, 2014: 98-106.
[2]
Abdallah A, Maarof M A, Zainal A . Fraud detection system: A survey[J]. Journal of Network & Computer Applications, 2016, 68(jun.):90-113.
[3]
Al-Subaie M, Zulkernine M . Efficacy of Hidden Markov Models Over Neural Networks in Anomaly Intrusion Detection[C]// International Computer Software & Applications Conference. IEEE Computer Society, 2006.
[4]
Hand D J, Whitrow C, Adams N M, Performance criteria for plastic card fraud detection tools[J]. Journal of the Operational Research Society, 2008, 59(7): 956-962.
[5]
Jiang C, Song J, Liu G, Credit Card Fraud Detection: A Novel Approach Using Aggregation Strategy and Feedback Mechanism[J]. IEEE Internet of Things Journal, 2018, 5(5): 3637-3647.
[6]
Longadge R, Dongre S. Class Imbalance Problem in Data Mining Review[J]. arXiv: Learning, 2013.
[7]
Nami S, Shajari M. Cost-sensitive payment card fraud detection based on dynamic random forest and k-nearest neighbors[J]. Expert Systems With Applications, 2018: 381-392.
[8]
Chandola V, Banerjee A, Kumar V, Anomaly detection: A survey[J]. ACM Computing Surveys, 2009, 41(3).
[9]
Van Vlasselaer V, Bravo C, Caelen O, APATE: A novel approach for automated credit card transaction fraud detection using network-based extensions[C]. decision support systems, 2015: 38-48.
[10]
Shen A, Tong R, Deng Y, Application of Classification Models on Credit Card Fraud Detection[C]. international conference on service systems and service management, 2007: 1-4.
[11]
Rtayli N, Enneya N . Enhanced credit card fraud detection based on SVM-recursive feature elimination and hyper-parameters optimization[J]. Journal of Information Security and Applications, 55.
[12]
Khormuji M K, Bazrafkan M, Sharifian M, Credit Card Fraud Detection with a Cascade Artificial Neural Network and Imperialist Competitive Algorithm[J]. International Journal of Computer Applications, 2014, 96(25): 1-9.
[13]
Bian Y, Cheng M, Yang C, Financial fraud detection: A new ensemble learning approach for imbalanced data[C]. pacific asia conference on information systems, 2016.
[14]
Devi D, Biswas S K, Purkayastha B, A Cost-sensitive weighted Random Forest Technique for Credit Card Fraud Detection[C]. international conference on computing communication and networking technologies, 2019.
[15]
Fu K, Cheng D, Tu Y, Credit Card Fraud Detection Using Convolutional Neural Networks[C]. international conference on neural information processing, 2016: 483-490.
[16]
Wang S, Liu C, Gao X, Session-Based Fraud Detection in Online E-Commerce Transactions Using Recurrent Neural Networks[C]. european conference on machine learning, 2017: 241-252.
[17]
Malini N, Pushpa M. Analysis on credit card fraud identification techniques based on KNN and outlier detection[C]. international conference on advances in electrical electronics information communication and bio informatics, 2017: 255-258.
[18]
Zaslavsky V, Strizhak A. Credit Card Fraud Detection Using Self-Organizing Maps[J]. Information & Security: An International Journal, 2006: 48-63.
[19]
Serranocinca C. Self-organizing neural networks for financial diagnosis[C]. decision support systems, 1996, 17(3): 227-238.
[20]
Niu X, Wang L, Yang X, A Comparison Study of Credit Card Fraud Detection: Supervised versus Unsupervised.[J]. arXiv: Learning, 2019.
[21]
Chalapathy R, Chawla S. Deep Learning for Anomaly Detection: A Survey[J]. arXiv: Learning, 2019.
[22]
Schlegl T, Seebock P, Waldstein S M, Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery[C]. international conference information processing, 2017: 146-157.
[23]
Lepoivre M R, Avanzini C O, Bignon G, Credit Card Fraud Detection with Unsupervised Algorithms[J]. Journal of Advances in Information Technology, 2016, 7(1): 34-38.
[24]
Pumsirirat A, Yan L. Credit Card Fraud Detection using Deep Learning based on Auto-Encoder and Restricted Boltzmann Machine[J]. International Journal of Advanced Computer Science and Applications, 2018, 9(1).
[25]
Rendle S, Freudenthaler C, Gantner Z, BPR: Bayesian personalized ranking from implicit feedback[C]. uncertainty in artificial intelligence, 2009: 452-461.
[26]
Theodoridis S. Stochastic Gradient Descent[M]// Deep Learning with Python. Apress, 2017.

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  • (2024)Leveraging Adversarial Augmentation on Imbalance Data for Online Trading Fraud DetectionIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.324096811:2(1602-1614)Online publication date: Apr-2024

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DSDE '21: Proceedings of the 2021 4th International Conference on Data Storage and Data Engineering
February 2021
165 pages
ISBN:9781450389303
DOI:10.1145/3456146
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 08 July 2021

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Author Tags

  1. Credible Method
  2. Credible Recommendation
  3. Individual Behavior Profiling
  4. Online Payment Fraud Detection

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  • (2024)Leveraging Adversarial Augmentation on Imbalance Data for Online Trading Fraud DetectionIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.324096811:2(1602-1614)Online publication date: Apr-2024

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