Classification of Credit Card Frauds Detection using machine learning techniques

DOI: https://doi.org/10.33650/jeecom.v5i2.6602

Authors (s)


(1) * Rasha Rokan Ismail   (University Diyala)  
        Iraq
(2)  Farah Hatem Khorsheed   (University Diyala)  
        Iraq
(*) Corresponding Author

Abstract


Credit card fraud refers to the illegal activities carried out by criminals. In this research paper, we delve into the topic by exploring four different approaches to analyze fraud, namely decision trees, logistic regression, support vector machines, and Random Forests. Our proposed technique encompasses four stages: inputting the dataset, balancing the data through sampling, training classifier models, and detecting fraud. To analyze the data, we utilized two methods: forward stepwise logistic regression analysis (LR) and decision tree analysis (DT), in addition to Random Forest and support vector machine. Based on the outcomes of our analysis, the decision tree algorithm produced the highest AUC and accuracy value, achieving a perfect score of 1. On the other hand, logistic regression yielded the lowest values of 0.33 and 0.2933 for AUC and accuracy, respectively. Moreover, the implementation of forest algorithms resulted in an impressive accuracy rate of 99.5%, which signifies a significant advancement in automating the detection of credit card fraud.



Keywords

Fraud Credit, SVM, TREE, Regression.



Full Text: PDF



References


PUMSIRIRAT, Apapan; LIU, Yan. Credit card fraud detection using deep learning based on auto-encoder and restricted boltzmann machine. International Journal of advanced computer science and applications, 2018, 9.1.‏

Euromonitor International, 2006. Financial cards in Germany Available at: http://www.euromonitor.com/Financial_Cards_in_Germany (Accessed: November 2006).

CLIFTON PHUA1, VINCENT LEE1, KATE SMITH1 & ROSS GAYLER2 “ A Comprehensive Survey of Data Mining-based Fraud Detection Research” published by School of Business Systems, Faculty of Information Technology, Monash University, Wellington Road, Clayton, Victoria 3800, Australia

“Survey Paper on Credit Card Fraud Detection by Suman” , Research Scholar, GJUS&T Hisar HCE, Sonepat published by International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 3 Issue 3, March 2014

“Research on Credit Card Fraud Detection Model Based on Distance Sum – by Wen-Fang YU and Na Wang” published by 2009 International Joint Conference on Artificial Intelligence

A. A. Aljumah, M. K. Siddiqui, and M. G. Ahamad, "Application of classification based data mining technique in diabetes care," Journal of applied Sciences, vol. 13, no. 3, 2013

E. I. Georga, D. I. Fotiadis, and V. C. Protopappas, Glucose prediction in type 1 and type 2 diabetic patients using data driven techniques. INTECH Open Access Publisher, 2011

Piryonesi S. Madeh; El-Diraby Tamer E. (2020-06-01). "Role of Data Analytics in Infrastructure Asset Management: Overcoming Data Size and Quality Problems". Journal of Transportation Engineering, Part B: Pavements. 146 (2): 04020022. doi:10.1061/JPEODX.0000175.

Piryonesi, S. Madeh; El-Diraby, Tamer E. (2021-02-01). "Using Machine Learning to Examine Impact of Type of Performance Indicator on Flexible Pavement Deterioration Modeling". Journal of Infrastructure Systems. 27 (2): 04021005.

Dal Pozzolo A, Caelen O, Le Borgne YA, Waterschoot S and Bontempi G 2014 Learned lessons in credit card fraud detection from a practitioner perspective Expert systems with applications 41 pp 4915-28.

Najadat H, Altiti O, Aqouleh AA and Younes M 2020 Credit card fraud detection based on machine and deep learning 11th Int. Conf. Information and Communication Systems (IEEE) pp 204-08

[12] Drummond C and Holte RC 2003 C4.5, class imbalance, and cost sensitivity: why undersampling beats over-sampling Workshop on learning from imbalanced datasets II (Washington DC: Citeseer) 11 pp 1-8

Kumar MS, Soundarya V, Kavitha S, Keerthika ES and Aswini E 2019 Credit card fraud detection using random forest algorithm 3 rd Int. Conf. Computing and Communications Technologies (IEEE) pp 149-53

Sadineni PK 2020 Detection of fraudulent transactions in credit card using machine learning algorithms 4 th Int. Conf. IoT in Social, Mobile, Analytics and Cloud (I-SMAC, IEEE) pp 659-60

Sailusha R, Gnaneswar V, Ramesh R and Rao GR 2020 Credit card fraud detection using machine learning 4 th Int. Conf. Intelligent Computing and Control Systems (IEEE) pp 1264- 70.

Jiang, C.; Song, J.; Liu, G.; Zheng, L.; Luan, W. Credit card fraud detection: A novel approach using aggregation strategy and feedback mechanism. IEEE Internet Things J. 2018, 5, 3637–3647.

Kumar, P.; Iqbal, F. Credit card fraud identification using machine learning approaches. In Proceedings of the 2019 1st Inter national conference on innovations in information and communication technology (ICIICT), Chennai, India, 25–26 April 2019; pp. 1–4.

Lamba, H. Credit Card Fraud Detection in Real Time. Ph.D. Thesis, California State University San Marcos, San Marcos, CA, USA, 2020.

Comparative Study on Classic Machine learning Algorithmshttps://towardsdatascience.com/comparative-study-on-classic-machine-learningalgorithms-24f9ff6ab222.

MAHESH, Konduri Praveen; AFROUZ, Shaik Ashar; AREECKAL, Anu Shaju. Detection of fraudulent credit card transactions: A comparative analysis of data sampling and classification techniques. In: Journal of Physics: Conference Series. IOP Publishing, 2022. p. 012072.‏

Sailusha R, Gnaneswar V, Ramesh R and Rao GR 2020 Credit card fraud detection using machine learning 4 th Int. Conf. Intelligent Computing and Control Systems (IEEE) pp 1264- 70.

HASAN, Fahim, et al. E-commerce Merchant Fraud Detection using Machine Learning Approach. In: 2022 7th International Conference on Communication and Electronics Systems (ICCES). IEEE, 2022. p. 1123-1127.‏

Xue, W.; Zhang, J. Dealing with imbalanced dataset: A re sampling method based on the improved SMOTE algorithm. Commun. Stat. Simul. Comput. 2013, 45, 1160–1172.

Chen, R., Chiu, M., Huang, Y. and Chen, L. 2004. Detecting credit card fraud by using questionnaire-responded transaction model based on SVMs. In Proceedings of IDEAL2004.

Brause, R., Langsdorf, T. and Hepp, M. 1999. Neural data mining for credit card fraud detection. In Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence.

Stolfo, S. J., Fan, D. W., Lee, W., Prodromidis, A. L. and Chan, P. K. 1997. Credit card fraud detection using meta-learning: Issues and initial results. In AAAI Workshop on AI Approaches to Fraud Detection and Risk Management. AAAI Press, Menlo Park, CA.

Stolfo, S., Fan, W., Lee, W., Prodromidis, A. L. and Chan, P. 1999. Cost-based modeling for fraud and intrusion detection: Results from the JAM Project. In Proceedings of the DARPA Information Survivability Conference and Exposition. IEEE Computer Press, New York.

Prodromidis, A. L., Chan P. and Stolfo S. J. 2000. Meta-learning in distributed data mining systems: issues and approaches. Advances of Distributed Data Mining, Editors Kargupta H. and Chan, P. AAAI Press.

Chen, R.-C., Luo, S.-T., Liang, X. and Lee, V. C. S. 2005. Personalized approach based on SVM and ANN for detecting credit card fraud. In Proceedings of the IEEE International Conference on Neural Networks and Brain, Beijing, China


Article View

Abstract views : 163 times | PDF files viewed : 73 times

Dimensions, PlumX, and Google Scholar Metrics

10.33650/jeecom.v5i2.6602


Refbacks

  • There are currently no refbacks.


Copyright (c) 2023 Rasha Rokan Ismail, Farah Hatem Khorsheed

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Creative Commons License
 
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Journal of Electrical Engineering and Computer (JEECOM)
Published by LP3M Nurul Jadid University, Indonesia, Probolinggo, East Java, Indonesia.