Open Access Open Access  Restricted Access Subscription or Fee Access

Assessment of Class Balancing Techniques for Credit Card Deception Recognition

Kaneez Zainab(1*), Namrata Dhanda(2), Qamar Abbas(3)

(1) Department of Computer Science and Engineering, Amity University, Lucknow Campus, India
(2) Department of Computer Science and Engineering, Amity University, Lucknow Campus, India
(3) Department of Computer Science and Engineering, Ambalika Institute of Technology & Management, India
(*) Corresponding author



Nowadays, rapid progress in virtual companies and the internet is hugely changing the way of trading. Since E-commerce provides international companies to trade with each other and bridges the global trading gap, both flexibility and rivalry have increased. Along with this, unlawful activities on cyber space are also growing. The readily available tools in the market make such activities easier for cyber criminals. The companies providing credit cards need to distinguish deceitful transactions so that customers can be guarded from fraudulent actions. Much work has been done in order to detect fraudulent transactions based on user profile, spend behaviour, past transactions, category wise. However, in order to detect and prevent credit card frauds in real time environment, more technical advancements and a more strategic approach considering the nature of dataset, which is used to train the model, are needed. Since the nature of credit card transaction dataset is always imbalanced, this paper intends to evaluate the efficacy of class balancing techniques for detecting credit card related deceptions. Two variations of balanced datasets are created by exploiting popular approaches like under sampling and oversampling (SMOTE). In order to evaluate the efficacy, a simple neural network with only one hidden layer has been implemented and it has been trained with balanced datasets. Our experimental results show that the model trained with dataset gives results that are more accurate.
Copyright © 2021 Praise Worthy Prize - All rights reserved.


Credit Card Frauds; Fraud Revealing; Identity Theft; Online Frauds; Payment Frauds; Unbiased Data

Full Text:



Moalosi, Motlhaleemang, Hlomani Hlomani, and Othusitse SD Phefo. Combating credit card fraud with online behavioural targeting and device fingerprinting, International Journal of Electronic Security and Digital Forensics, 11, no. 1, pp 46-69, 2019.

Wickramanayake, Bemali, et al, A Survey of Online Card Payment Fraud Detection using Data Mining-based Methods, arXiv preprint arXiv:2011.14024, 2020.

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

Xinwei Zhang, Yaoci Han, Wei Xu, Qili Wang, HOBA: A novel feature engineering methodology for credit card fraud detection with a deep learning architecture, Information Sciences, Volume 557, 2021, Pages 302-316. ISSN 0020-0255.

K. Zainab and N. Dhanda, Big Data and Predictive Analytics in Various Sectors, International Conference on System Modeling & Advancement in Research Trends (SMART), Moradabad, India, 2018, pp. 39-43.

S. Kumar, N. Dhanda and A. Pandey, Data Science - Cosmic Infoset Mining, Modeling and Visualization, "International Conference on Computational and Characterization Techniques in Engineering & Sciences (CCTES), Lucknow, India, 2018, pp. 1-4.

Ahammad, J., Hossain, N. and Alam, M.S., Credit card fraud detection using data pre-processing on imbalanced data-Both oversampling and undersampling, In Proceedings of the International Conference on Computing Advancements pp. 1-, 2020.

Tsai, Chih-Fong, Wei-Chao Lin, Ya-Han Hu, and Guan-Ting Yao, Under-sampling class imbalanced datasets by combining clustering analysis and instance selection, Information Sciences 477, pp 47-54, 2019.

Guzmán-Ponce, A., R. M. Valdovinos, and J. S. Sánchez, A Cluster-Based Under-Sampling Algorithm for Class-Imbalanced Data, International Conference on Hybrid Artificial Intelligence Systems, pp. 299-311. Springer, Cham, 2020.

Arya, Monika, and Hanumat Sastry G, DEAL-'Deep Ensemble ALgorithm'Framework for Credit Card Fraud Detection in Real-Time Data Stream with Google TensorFlow, Smart Science 8.2 pp 71-83, 2020.

Mansourifar, Hadi, and Weidong Shi, Deep Synthetic Minority Over-Sampling Technique. arXiv preprint arXiv:2003.09788 ,2020.

Rtayli, Naoufal, and Nourddine Enneya, Enhanced credit card fraud detection based on SVM-recursive feature elimination and hyper-parameters optimization, Journal of Information Security and Applications 55, 102596, 2020.

Aashlesha Bhingarde, A. K., Credit Card Fraud Detection using Hidden Markov Model, International Journal of Advanced Research in Computer and Communication Engineering, 2, 2015.

Ishu Trivedi, M. M., Credit Card Fraud Detection, International Journal of Advanced Research in Computer a Communication Engineering, 4, 2016.

Aashlesha Bhingarde, A. K., Credit Card Fraud Detection using Hidden Markov Model, International Journal of Advanced Research in Computer and Communication Engineering, 2, 2015.

Dal Pozzolo, Andrea, Giacomo Boracchi, Olivier Caelen, Cesare Alippi, and Gianluca Bontempi, Credit card fraud detection: a realistic modelling and a novel learning strategy, IEEE transactions on neural networks and learning systems 29, no. 8, pp 3784-3797, 2017.

Carcillo, Fabrizio, Yann-Aël Le Borgne, Olivier Caelen, Yacine Kessaci, Frédéric Oblé, and Gianluca Bontempi, Combining unsupervised and supervised learning in credit card fraud detection, Information Sciences, 2019.

Randhawa, Kuldeep, Chu Kiong Loo, Manjeevan Seera, Chee Peng Lim, and Asoke K. Nandi, Credit card fraud detection using AdaBoost and majority voting, IEEE access 6, pp 14277-14284, 2018.

Roy, Abhimanyu, Jingyi Sun, Robert Mahoney, Loreto Alonzi, Stephen Adams, and Peter Beling. Deep learning detecting fraud in credit card transactions, Systems and Information Engineering Design Symposium (SIEDS), pp. 129-134. IEEE, 2018.

Credit Card / Fraud Detection - dataset by vlad |

H. John and S. Naaz, Credit card fraud detection using local outlier factor and isolation forest, Int. J. Comput. Sci. Eng., vol. 7, no. 4, pp. 1060-1064, Sep. 2019.

D. Tanouz, R. R. Subramanian, D. Eswar, G. V. P. Reddy, A. R. Kumar and C. V. N. M. Praneeth, Credit Card Fraud Detection Using Machine Learning, 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS), 2021, pp. 967-972.

Goyal, Rahul & Manjhvar, Amit & M., Dr. (2020). Review on Credit Card Fraud Detection using Data Mining Classification Techniques & Machine Learning Algorithms. SSRN Electronic Journal. 7.

Mohammed, Emad, and Behrouz Far. Supervised Machine Learning Algorithms for Credit Card Fraudulent Transaction Detection: A Comparative Study. IEEE Annals of the History of Computing, IEEE, 1 July 2018.

Zainab K., Dhanda N., Abbas Q. (2021) Analysis of Various Boosting Algorithms Used for Detection of Fraudulent Credit Card Transactions. In: Kaiser M.S., Xie J., Rathore V.S. (eds) Information and Communication Technology for Competitive Strategies (ICTCS 2020).

Abidi, M., Fizazi, H., Boudali, N., Clustering of Remote Sensing Data Based on Spherical Evolution Algorithm, (2021) International Review of Aerospace Engineering (IREASE), 14 (2), pp. 72-79.

Al-Tarawneh, M., Muheilan, M., Al Tarawneh, Z., Hand Movement-Based Diabetes Detection Using Machine Learning Techniques, (2021) International Journal on Engineering Applications (IREA), 9 (4), pp. 234-242.

Shatnawi, M., Bani Yassein, M., Aljawarneh, S., Alodibat, S., Meqdadi, O., Hmeidi, I., Al Zoubi, O., An Improvement of Neural Network Algorithm for Anomaly Intrusion Detection System, (2020) International Journal on Communications Antenna and Propagation (IRECAP), 10 (2), pp. 84-93.


  • There are currently no refbacks.

Please send any question about this web site to
Copyright © 2005-2022 Praise Worthy Prize