Research on improvements of fraud detection system: basing on improved machine learning algorithms

Research Article
Open access

Research on improvements of fraud detection system: basing on improved machine learning algorithms

Zhiding Zhang 1*
  • 1 Shanghai University    
  • *corresponding author zhangzhiding@shu.edu.cn
Published on 11 December 2023 | https://doi.org/10.54254/2755-2721/27/20230146
ACE Vol.27
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-199-5
ISBN (Online): 978-1-83558-200-8

Abstract

Nowadays, commercial fraud behaviors commonly occur in many industries. However, due to obstacles like concept drift, imbalanced dataset and uneven distribution of fraud entries, Fraud Detection System (FDS) fails to identify such behaviors. Among the problems mentioned above, most research focus on dealing with skewed dataset. This paper first presents common application scenarios of FDS which consist of credit card fraud, insurance fraud and supply chain fraud. Then, this study introduces five representative methods in dealing with problems mentioned above, which are K Nearest Neighbors-Synthetic Minority Oversampling Technique-Long Short-term Memory Networks (kNN-SMOTE-LSTM), Generative Adversarial Nets-AdaBoost-Decision tree (GAN-AdaBoost-DT), Wasserstein GAN-Kernel Density Estimation-Gradient Boosting DT (WGAN-KDE-GBDT), Time-LSTM (TLSTM) and Adaptive Synthetic Sampling-Sequential Forward Selection-Random Forest (ADASYN-SFS-RF). KNN-SMOTE-LSTM adopts KNN as an identifying classifier so as to only retain true samples. GAN-AdaBoost-DT generates new samples without referring to real transactions. WGAN-KDE-GBDT uses Wasserstein Distance as distance measurement instead, and thus improves training speed and guarantees successful generation. TLSTM tires to consider the weights of different time intervals and measures the similarity between the simulated behavior and the genuine behavior. ADASYN-SFS-RF employs SFS algorithm, basing on RF, to only reserve optimal subsets of features. Finally, result metrics prove that those improved algorithms do improve the overall performance of FDS, even if with limitations at some indicators.

Keywords:

fraud detection system, imbalanced dataset, machine learning

Zhang,Z. (2023). Research on improvements of fraud detection system: basing on improved machine learning algorithms. Applied and Computational Engineering,27,49-56.
Export citation

References

[1]. Abdallah A, Maarof M A, Zainal A. Fraud detection system: A survey. Journal of Network and Computer Applications, 2016, 68: 90-113.

[2]. P. Saravanan, V. Subramaniyaswamy, N. Sivaramakrishnan, M. Arun Prakash, T. Arunkumar. Data mining approach for subscription-fraud detection in telecommunication sector. Contemporary Engineering Sciences, Vol. 7, 2014, no. 11, 515-522

[3]. Benchaji, I., Douzi, S., El Ouahidi, B. Using Genetic Algorithm to Improve Classification of Imbalanced Datasets for Credit Card Fraud Detection. In: Khoukhi, F., Bahaj, M., Ezziyyani, M. (eds) Smart Data and Computational Intelligence. AIT2S. Lecture Notes in Networks and Systems, 2018, vol 66. Springer, Cham.

[4]. Chunhua Ju, Guanyu Chen, Fuguang Bao. Consumer Finance Risk Detection Model Based on kNN-SMOTE-LSTM: Taking Credit Card Fraud Detection as An Example. Chinese Journal of Systems Science, 2021, 41(02):481-498.

[5]. Yu Lin, Xun Huang, Weide Chun, Dengshi Huang. Early Warning Research on Extreme Financial Risks Based on ODR-ADASYN-SVM. Journal of Management Sciences in China, 2016, 19(05):87-101.

[6]. Zan Mo, Yanrong Gai, Guanlong Fan. Credit Card Fraud Classification Based on GAN-AdaBoost-DT Imbalance Classification Algorithm. Journal of Computer Applications, 2019, 39(02):618-622.

[7]. Luhui Cao, Fenglin Qin, Zhongmin Yan. TLSTM Based Medicare Fraud Detection. Computer Engineering and Applications, 2020, 56(21):237-241.

[8]. Rongrong Chen, Guohua Zhan, Zhihua Li. Credit Card Transaction Fraud Prediction Based on XGBoost Algorithm Model. Application Research of Computers, 2020, 37(S1):111-112+115.

[9]. Johannes Jurgovsky, Michael Granitzer, Konstantin Ziegler, Sylvie Calabretto, Pierre-Edouard Portier, Liyun He-Guelton, Olivier Caelen, Sequence classification for credit-card fraud detection, Expert Systems with Applications, Volume 100, 2018, Pages 234-245

[10]. Carneiro, E.M.; Forster, C.H.Q.; Mialaret, L.F.S.; Dias, L.A.V.; da Cunha, A.M. High-Cardinality Categorical Attributes and Credit Card Fraud Detection. Mathematics, 2022, 10, 3808.

[11]. Wenlong Wu, Xi Zhou, Yi Wang, Baoquan Wang. WKAG: A Fraud Detection Method for Unbalanced Health Care Data. Computer Engineering and Applications, 2021, 57(09):247-254.

[12]. F. Wan, XGBoost Based Supply Chain Fraud Detection Model, 2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE), 2021, pp. 355-358.

[13]. Wanmin Wang, Luping Zhi. Generalization Performance Improvement and Interpretability of Fraud Detection Model Based on ADASYN-SFS-R.F. Application Research of Computers, 2023, 1-11.

[14]. Mengting Chai,Yuanping Zhu. Research and Application Progress of Generative Adversarial Networks. Computer Engineering, 2019, 45(09):222-234..

[15]. Fenglong Ma, Radha Chitta, Jing Zhou, Quanzeng You, Tong Sun, and Jing Gao. Dipole: Diagnosis Prediction in Healthcare via Attention-based Bidirectional Recurrent Neural Networks. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '17). Association for Computing Machinery, 2017, 1903–1911.

Cite this article

Zhang,Z. (2023). Research on improvements of fraud detection system: basing on improved machine learning algorithms. Applied and Computational Engineering,27,49-56.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

Disclaimer/Publisher's Note

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

About volume

Volume title: Proceedings of the 2023 International Conference on Software Engineering and Machine Learning

ISBN：978-1-83558-199-5(Print) / 978-1-83558-200-8(Online)
Editor：Anil Fernando, Marwan Omar
Conference website: http://www.confseml.org
Conference date: 19 April 2023
Series: Applied and Computational Engineering
Volume number: Vol.27
ISSN：2755-2721(Print) / 2755-273X(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).

References

[1]. Abdallah A, Maarof M A, Zainal A. Fraud detection system: A survey. Journal of Network and Computer Applications, 2016, 68: 90-113.

[2]. P. Saravanan, V. Subramaniyaswamy, N. Sivaramakrishnan, M. Arun Prakash, T. Arunkumar. Data mining approach for subscription-fraud detection in telecommunication sector. Contemporary Engineering Sciences, Vol. 7, 2014, no. 11, 515-522

[3]. Benchaji, I., Douzi, S., El Ouahidi, B. Using Genetic Algorithm to Improve Classification of Imbalanced Datasets for Credit Card Fraud Detection. In: Khoukhi, F., Bahaj, M., Ezziyyani, M. (eds) Smart Data and Computational Intelligence. AIT2S. Lecture Notes in Networks and Systems, 2018, vol 66. Springer, Cham.

[4]. Chunhua Ju, Guanyu Chen, Fuguang Bao. Consumer Finance Risk Detection Model Based on kNN-SMOTE-LSTM: Taking Credit Card Fraud Detection as An Example. Chinese Journal of Systems Science, 2021, 41(02):481-498.

[5]. Yu Lin, Xun Huang, Weide Chun, Dengshi Huang. Early Warning Research on Extreme Financial Risks Based on ODR-ADASYN-SVM. Journal of Management Sciences in China, 2016, 19(05):87-101.

[6]. Zan Mo, Yanrong Gai, Guanlong Fan. Credit Card Fraud Classification Based on GAN-AdaBoost-DT Imbalance Classification Algorithm. Journal of Computer Applications, 2019, 39(02):618-622.

[7]. Luhui Cao, Fenglin Qin, Zhongmin Yan. TLSTM Based Medicare Fraud Detection. Computer Engineering and Applications, 2020, 56(21):237-241.

[8]. Rongrong Chen, Guohua Zhan, Zhihua Li. Credit Card Transaction Fraud Prediction Based on XGBoost Algorithm Model. Application Research of Computers, 2020, 37(S1):111-112+115.

[9]. Johannes Jurgovsky, Michael Granitzer, Konstantin Ziegler, Sylvie Calabretto, Pierre-Edouard Portier, Liyun He-Guelton, Olivier Caelen, Sequence classification for credit-card fraud detection, Expert Systems with Applications, Volume 100, 2018, Pages 234-245

[10]. Carneiro, E.M.; Forster, C.H.Q.; Mialaret, L.F.S.; Dias, L.A.V.; da Cunha, A.M. High-Cardinality Categorical Attributes and Credit Card Fraud Detection. Mathematics, 2022, 10, 3808.

[11]. Wenlong Wu, Xi Zhou, Yi Wang, Baoquan Wang. WKAG: A Fraud Detection Method for Unbalanced Health Care Data. Computer Engineering and Applications, 2021, 57(09):247-254.

[12]. F. Wan, XGBoost Based Supply Chain Fraud Detection Model, 2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE), 2021, pp. 355-358.

[13]. Wanmin Wang, Luping Zhi. Generalization Performance Improvement and Interpretability of Fraud Detection Model Based on ADASYN-SFS-R.F. Application Research of Computers, 2023, 1-11.

[14]. Mengting Chai,Yuanping Zhu. Research and Application Progress of Generative Adversarial Networks. Computer Engineering, 2019, 45(09):222-234..

[15]. Fenglong Ma, Radha Chitta, Jing Zhou, Quanzeng You, Tong Sun, and Jing Gao. Dipole: Diagnosis Prediction in Healthcare via Attention-based Bidirectional Recurrent Neural Networks. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '17). Association for Computing Machinery, 2017, 1903–1911.

For authors
Guide for authors Policies for authors Aims and scope Subscription Open access policy
For editors
Policies for editors Editorial board
For reviewers
Policies for reviewers Privacy policy

Cookies are used by this site.
Copyright © 2024 EWA Publishing, its licensors, and contributors. All rights reserved, including text and data mining, AI training, and similar technologies.