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http://dx.doi.org/10.22937/IJCSNS.2021.21.9.4

A Review of Machine Learning Algorithms for Fraud Detection in Credit Card Transaction  

Lim, Kha Shing (Quest International University)
Lee, Lam Hong (Quest International University)
Sim, Yee-Wai (Quest International University)
Publication Information
International Journal of Computer Science & Network Security / v.21, no.9, 2021 , pp. 31-40 More about this Journal
Abstract
The increasing number of credit card fraud cases has become a considerable problem since the past decades. This phenomenon is due to the expansion of new technologies, including the increased popularity and volume of online banking transactions and e-commerce. In order to address the problem of credit card fraud detection, a rule-based approach has been widely utilized to detect and guard against fraudulent activities. However, it requires huge computational power and high complexity in defining and building the rule base for pattern matching, in order to precisely identifying the fraud patterns. In addition, it does not come with intelligence and ability in predicting or analysing transaction data in looking for new fraud patterns and strategies. As such, Data Mining and Machine Learning algorithms are proposed to overcome the shortcomings in this paper. The aim of this paper is to highlight the important techniques and methodologies that are employed in fraud detection, while at the same time focusing on the existing literature. Methods such as Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), naïve Bayesian, k-Nearest Neighbour (k-NN), Decision Tree and Frequent Pattern Mining algorithms are reviewed and evaluated for their performance in detecting fraudulent transaction.
Keywords
Data Mining; Machine Learning; Credit Card; Fraud Detection;
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1 L. Oghenekaro, and C. Ugwu, "A Novel Machine Learning Approach to Credit Card Fraud Detection," International Journal of Computer Applications, vol. 140, no. 5, 2016, pp.45-50.   DOI
2 P. Craja, A. Kim, and S. Lessmann, "Deep learning for detecting financial statement fraud," Decision Support Systems, vol. 139, 2020, article 113421.
3 G. James, D. Witten, T. Hastie, and R. Tibshirani, "An Introduction to Statistical Learning," Springer, 2013, pp. 204.
4 J. Gaikwad, A. Deshmane, H. Somavanshi, S. Patil, and R. Badgujar, "Credit Card Fraud Detection using Decision Tree Induction Algorithm," International Journal of Innovative Technology and Exploring Engineering (IJITEE), vol. 4, no. 6, 2014, pp. 66-69.
5 T. Minegishi, and A. Niimi, "Proposal of Credit Card Fraudulent Use Detection by Online-type Decision Tree Construction and Verification of Generality," International Journal for Information Security Research, vol. 3, no. 1, 2013, pp. 229-235.   DOI
6 I. Monedero, F. Biscarri, C. Leon, J. Guerrero, J. Biscarri, and R. Millan, "Detection of frauds and other non-technical losses in a power utility using Pearson coefficient, Bayesian networks and decision trees," International Journal of Electrical Power & Energy Systems, vol. 34, no. 1, 2012, pp. 90-98.   DOI
7 R. Banerjee, G. Bourla, S. Chen, M. Kashyap, S. Purohit, and J. Battipaglia, "Comparative Analysis of Machine Learning Algorithms through Credit Card Fraud Detection," in Proceedings of the 2018 IEEE MIT Undergraduate Research Technology Conference, 2018, pp. 1-4.
8 W. Loh, "Classification and regression trees," Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 1, no. 1, 2011, pp. 14-23.   DOI
9 C. Paasch, Credit Card Fraud Detection using Artificial Neural Networks tuned by Genetic Algorithms, 2014, doi:10.14711/thesis-b1023238
10 R. Porkess, and S. Mason, "Looking at Debit and Credit Card Fraud," Teaching Statistics, vol. 34, no. 3, 2011, pp. 87-91.   DOI
11 S. Priyanka, "Comparative Study ID3, CART and C4.5 Decision Tree Algorithm: A Survey," International Journal of Advanced Information Science and Technology, vol. 27, no. 27, 2014, pp. 97-103.
12 V. Dheepa, and R. Dhanapal, "Behavior Based Credit Card Fraud Detection Using Support Vector Machines," Journal on Soft Computing, vol. 2, no. 4, 2012, pp. 391-397.
13 K. Hu, Y. Lu, L. Zhou, and C. Shi, "Integrating Classification and Association Rule Mining: A Concept Lattice Framework," Lecture Notes in Computer Science New Directions in Rough Sets, Data Mining, and Granular-Soft Computing, 2001, pp. 443-447.
14 B. Baesens, S. Hoppner, and T. Verdonck, "Data engineering for fraud detection," Decision Support Systems, 2021, article 113492,
15 I. Sutedja, Y. Heryadi, L. Wulandhari, and B. Abbas, "Recognizing debit card fraud transaction using CHAID and K-nearest neighbour: Indonesian Bank case," in Proceedings of the 11th International Conference on Knowledge, Information and Creativity Support Systems, 2016, pp. 1-5.
16 J. Xu, A. Sung, abd Q. Liu, "Behaviour Mining for Fraud Detection," Journal of Research and Practice in Information Technology, vol. 39, no. 1, 2007, pp. 3-18.
17 F. Thabtah, "A review of associative classification mining," The Knowledge Engineering Review, vol. 22, no. 1, 2007, pp. 37-65.   DOI
18 H. Naik, "Credit Card Fraud Detection for Online Banking Transactions," International Journal for Research in Applied Science and Engineering Technology, vol. 6, no. 4, 2018, pp. 4573-4577.   DOI
19 D. Tripathi, B. Nigam, and D. Edla, "A Novel Web Fraud Detection Technique using Association Rule Mining," Procedia Computer Science, vol. 115, 2017, pp. 274-281.   DOI
20 M. Bansal, and Suman,"Credit Card Fraud Detection Using Self Organised Map," International Journal of Information & Computation Technology, vol. 4, No. 13, 2014, pp. 1343-1348.
21 M. Franzese, and A. Iuliano, "Hidden Markov Models," Encyclopedia of Bioinformatics and Computational Biology, vol. 1, 2019, pp. 753-762.
22 M. Pietrzykowski, and W. Salabun, "Applications of Hidden Markov Model: state-of-the-art," International of Journal Computer Technology & Applications, vol. 5, no. 4, 2014, pp. 1384-1391
23 B. Liu, Y. Ma, and C. Wong, "Classification Using Association Rules: Weaknesses and Enhancements," Data Mining for Scientific and Engineering Applications Massive Computing, 2001, pp. 591-605.
24 S. Yusuf, and D. Ekrem, "Detecting Credit Card Fraud by ANN and Logistic Regression," in Proceedings of the International Symposium on Innovations in Intelligent SysTems and Applications, 2011.
25 S. Agarwal, "Data Mining: Data Mining Concepts and Techniques," in Proceedings of the 2013 International Conference on Machine Intelligence and Research Advancement, 2013, pp. 203-207.
26 C. Ordonez, and K. Zhao, "Evaluating association rules and decision trees to predict multiple target attributes," Intelligent Data Analysis, vol. 15, no. 2, 2011, pp. 173-192.   DOI
27 S. Nasreen, M. Azam, K. Shehzad, U. Naeem, and M. Ghazanfar, "Frequent Pattern Mining Algorithms for Finding Associated Frequent Patterns for Data Streams: A Survey," Procedia Computer Science, vol. 37, 2014, pp. 109-116.   DOI
28 O. Aodha, and G. Brostow, "Revisiting Example Dependent Cost-Sensitive Learning with Decision Trees," In Proceedings of the 2013 IEEE International Conference on Computer Vision, 2013, pp. 193-200.
29 R. Agrawal, and R. Srikant, "Fast algorithms for mining association rules in large databases," Research Report RJ 9839, 1994, IBM Almaden Research Center, San Jose, California.
30 D. Excell, "Bayesian inference - the future of online fraud protection," Computer Fraud & Security, vol. 2, 2012, pp. 8-11.   DOI
31 R. Sallehuddin, S. Ibrahim, A. Zain, and A. Elmi, "Detecting SIM Box Fraud by Using Support Vector Machine and Artificial Neural Network," Jurnal Teknologi, vol. 74, no. 1, 2015, pp. 137-149.
32 Y. Sahin., and E. Duman, "Detecting Credit Card Fraud by Decision Trees and Support Vector Machines," in Proceedings of the International of MultiConference of Enginners and Computer Scientists, 2011, pp. 442-447.
33 R. Schapire, "Explaining AdaBoost," Empirical Inference, 2013, pp. 37-52.
34 N. Malini, and M. Pushpa, "Investigation of Credit Card Fraud Recognition Techniques based on KNN and HMM," in Proceedings of the International Conference on Communication, Computing and Information Technology, 2018, pp. 9-13.
35 R. Marmo, Data Mining for Fraud Detection System. Encyclopedia of Data Warehousing and Mining, 2nd ed, 2013, pp. 411-416.
36 D. Excell, Bayesian Inference - the Future of Online Fraud Protection. Computer Fraud & Security, 2nd ed., 2012, pp. 8-11.
37 V. Jayasree, and R. Balan, "Money laundering regulatory risk evaluation using Bitmap Index-based Decision Tree," Journal of the Association of Arab Universities for Basic and Applied Sciences, vol. 23, no. 1, 2017, pp. 96-102.
38 C. Tyagi, P. Parwekar, P. Singh, and K. Natla, "Analysis of Credit Card Fraud Detection Techniques," Solid State Technology, vol. 63, no. 6, 2020, pp. 18057-18069.
39 C. Chee, J. Jaafar, I. Aziz, M. Hassan, and W. Yeoh, "Algorithms for frequent itemset mining: a literature review," Artificial Intelligence Review, vol. 52, 2019, pp. 2603-2621.   DOI
40 C. Ordonez, and K. Zhao, "Evaluating association rules and decision trees to predict multiple target attributes," Intelligent Data Analysis, vol. 15, no. 2, 2011, pp. 173-192.   DOI
41 J. Xu, A. Sung and Q. Liu, "Behaviour Mining for Fraud Detection," Journal of Research and Practice in Information Technology, vol. 39, no. 1, 2007, pp. 3-18.
42 T. Sweer, Autoencoding Credit Card Fraud, Radboud University, 2018, Retrieved from https://www.cs.ru.nl/bachelorstheses/2018/Tom_Sweers___458435___Autoencoding_credit_card_fraude.pdf
43 A. Desai, and D. Deshmukh, "Data mining techniques for Fraud Detection," International Journal of Computer Science and Information Technologies, vol. 3, pp. 1-4, 2013.
44 K. Seeja, and M. Zareapoor, "FraudMiner: A Novel Credit Card Fraud Detection Model Based on Frequent Itemset Mining," The Scientific World Journal, 2014, pp. 1-10.
45 S. Ong, S. Sagadevan, and N. Malim, "Credit Card Fraud Detection Using Machine Learning As Data Mining Technique," Journal of Telecommunication, Electronic and Computer Engineering, vol. 10, no. 1-4, pp. 23-27, 2014.
46 Y. Sahin, and E. Duman, "Detecting credit card fraud by decision trees & support vector machines," in Proceeding of the International Multi Conference of Engineers & Computer Scientist, vol. I, 2011.
47 D. Abdelhamid, S. Khaoula, and O. Atika, "Automatic Bank Fraud Detection Using Support Vector Machines," in Proceedings of the International conference on Computing Technology and Information Management, pp. 10-17, 2014.
48 N. Carneiro, G. Figueira, and M. Costa, "A data mining-based system for credit-card fraud detection in e-tail," Decision Support Systems 95, Elsevier B.V, 2017, pp. 91-101.
49 C. Burges, "A tutorial on support vector machines for pattern recognition," Data Mining and Knowledge Discovery, vol. 2, no. 2, 1998, pp. 121-167.   DOI
50 j2kun, "Formulating the Support Vector Machine Optimization Problem," 2017 Retrieved from https://jeremykun.com/2017/06/05/formulating-the-supportvector-machine-optimization-problem/
51 B, Hssina, A. Merbouha, H. Ezzikouri, and M. Erritali, "A comparative study of decision tree ID3 and C4.5," International Journal of Advanced Computer Science and Applications, 4(2), 2014, pp. 13-19.
52 A. Banarescu, "Detecting and Preventing Fraud with Data Analytics," Procedia Economics and Finance, vol. 32, 2015, pp. 1827-1836.   DOI
53 B. Zolfaghari, K. Bibak, T. Koshiba, H. Nemati, and P. Mitra, "Statistical trend analysis of physically unclonable functions: An approach via text mining," CRC Press, 2021, pp. 55-74.
54 J. Quinlan, "Improved Use of Continuous Attributes in C4.5," Journal of Artificial Intelligence Research, vol. 4, 1996, pp. 77-90.   DOI
55 B. Gupta, A. Rawat, A. Jain, and A. Arora, "Analysis of Various Decision Tree Algorithms for Classification in Data Mining," International Journal of Computer Applications,. Vol. 163, no. 8, 2017, pp. 0975 - 8887.
56 Y. Sahin, S. Bulkan, and E. Duman, "A cost-sensitive decision tree approach for fraud detection," Expert Systems with Applications, vol. 40, no. 15, 2013, pp. 5916-5923.   DOI
57 X. Zhang, Y. Han, W. Xu, and Q. Wang, "HOBA: A novel feature engineering methodology for credit card fraud detection with a deep learning architecture," Information Sciences, vol. 557, no. 10, 2021, pp. 302-316.   DOI
58 D. Montague, Essentials of online payment security and fraud prevention, Wiley, 2011, pp. 183-189.
59 S. Viaene, R. Derrig, and G. Dedene,"A case study of applying boosting naive bayes to claim fraud diagnosis," IEEE Transactions on Knowledge and Data Engineering, vol. 16, no. 5, 2004, pp. 612-620.   DOI
60 S. Kiran, J. Guru, R. Kumar, N. Kumar, D. Katariya, and M. Sharma, "Credit card fraud detection using Naive Bayes model based and KNN classifier," International Jounral of Advance Research, Ideas and Innovations in Technology, vol. 4, 2018, pp. 44-47.
61 I. Rajak and K. Mathai, "Intelligent Fraudulent Detection System based SVM and Optimized by Danger Theory," in Proceedings of International Conference on Computer, Communication and Control, 2015, pp. 1-4.
62 A. Serrano, J. Costa, C. Cardonha, A. Fernandes, and R. Junior, "Neural Network Predictor for Fraud Detection: A Study Case for the Federal Patrimony Department," In Proceedings of the Seventh International Conference on Forensic Computer Science, 2012, pp. 61-66.
63 V. Choudhary, and E. Divya, "Credit Card Fraud Detection using Frequent Pattern Mining using FP- Tree And Apriori Growth," International Jounral of Advance Technology and Innovation Research, vol. 09, no. 13, 2017, pp. 2370-2373.
64 CyberSource, "Annual Fraud Benchmark Report: A Balancing Act," North America Edition, 2016.
65 Q. Lu, and C. Ju, "Research on Credit Card Fraud Detection Model Based on Class Weighted Support Vector Machine," Journal of Convergence Information Technology, vol. 6, no. 1, 2011, pp. 62-68.   DOI
66 C. Milgo, "A Bayesian Classification Model for Fraud Detection over ATM Platforms," Journal of Computer Engineering, vol. 18, no. 4, pp. 26-32, 2016.
67 R. Porkess, and S. Mason, "Looking at debit and credit card fraud," Teaching Statistics, vol. 34, no. 3, 2011, pp. 87-91.   DOI
68 C. Sudha, and T. Raj, "Credit Card Fraud Detection in Internet Using K-nearest Neighbor Algorithm," International Journal of Computer Science, vol. 5, issue 11, pp. 22-30, 2017.
69 L. Mukhanov, "Using Bayesian Belief Networks for credit card fraud detection," In Proceedings of the Conference: Proceedings of the 26th International Conference on Artificial Intelligence and Applications, 2008, pp. 221-225.
70 Kevin Zakka. (n.d.), A Complete Guide to K-NearestNeighbours with Applications in Python and R, Retrieved from https://kevinzakka.github.io/2016/07/13/k-nearestneighbor
71 S. Maes, K. Tuyls, B. Vanschoenwinkel, and B. Manderick, "Credit Card Fraud Detection Using Bayesian and Neural Networks," In Proceedings of the First International NAISO Congress on NEURO FUZZY THECHNOLOGIES, 2002.