Analysing Auto ML Model for Credit Card Fraud Detection
Keywords:
Auto ML, Classification, Credit card, Fraud detection, Machine learningAbstract
Fraud Detection is a major concern these days because of digitalization. We are totally dependent on online transactions these days for even very small needs. There is no doubt that online transactions have made our life very easy but it has increased risk on other hand. And this risk can be very harmful one day. Confidential data is being stolen by the different apps and it is sold in international market. Which later on comes to us in totally different and very harmful way. So why not to use technology again to stop these risks and flaws. Various ML techniques has been observed by researchers but Auto ML is yet not discovered on a wider platform. Therefore, this paper at first aims to explore the trending technology Auto ML. Then a model for evaluating Auto ML is suggested and analysed with different classification algorithms. The experimental results ascertained the accuracy of Auto ML followed by a comparative analysis of ML and Auto ML.
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References
Maniraj, S., Saini, A., Ahmed, S., & Sarkar, S., “Credit card fraud detection using machine learning and data science”. International Journal of Engineering Research and, 8(09) 2019.
Patil, S., Nemade, V., & Soni, P. K. (2018). Predictive modelling for credit card fraud detection using data analytics. Procedia computer science, 132, 385-395.
Varmedja, D., Karanovic, M., Sladojevic, S., Arsenovic, M., & Anderla, A. (2019, March). Credit card fraud detection machine learning methods. In 2019 18th International Symposium INFOTEH-JAHORINA (INFOTEH) (pp. 1-5). IEEE.
Carcillo, F., Le Borgne, Y. A., Caelen, O., Kessaci, Y., Oblé, F., & Bontempi, G. (2019). Combining unsupervised and supervised learning in credit card fraud detection. Information Sciences.
Srivastava, A., Kundu, A., Sural, S., & Majumdar, A. (2008). Credit card fraud detection using hidden Markov model. IEEE Transactions on dependable and secure computing, 5(1), 37- 48.
Quah, J. T., & Sriganesh, M. (2008). Real-time credit card fraud detection using computational intelligence. Expert systems with applications, 35(4), 1721-1732.
S. Akila and U. Srinivasulu Reddy, “Cost-sensitive Risk Induced Bayesian Inference Bagging (RIBIB) for credit card fraud detection,” Journal of Computational Science, vol. 27, pp. 247–254, Jul. 2018, doi: 10.1016/j.jocs.2018.06.009.
M. Ozbayoglu, M. U. Gudelek, and O. B. Sezer, “Deep learning for financial applications : A survey,” Applied Soft Computing, vol. 93, p. 106384, Aug. 2020, doi: 10.1016/j.asoc.2020.106384.
S. Bhattacharyya, S. Jha, K. Tharakunnel, and J. C. Westland, “Data mining for credit card fraud: A comparative study,” Decision Support Systems, vol. 50, no. 3, pp. 602–613, Feb. 2011, doi: 10.1016/j.dss.2010.08.008.
G. C. de Sá, A. C. M. Pereira, and G. L. Pappa, “A customized classification algorithm for credit card fraud detection,” Engineering Applications of Artificial Intelligence, vol. 72, pp. 21–29, Jun. 2018, doi: 10.1016/j.engappai.2018.03.011.
Carcillo, Y.-A. Le Borgne, O. Caelen, Y. Kessaci, F. Oblé, and G. Bontempi, “Combining unsupervised and supervised learning in credit card fraud detection,” Information Sciences, May 2019, doi: 10.1016/j.ins.2019.05.042.
S. M. S. Askari and M. A. Hussain, “IFDTC4.5: Intuitionistic fuzzy logic based decision tree for Etransactional fraud detection,” Journal of Information Security and Applications, vol. 52, p. 102469, Jun. 2020, doi: 10.1016/j.jisa.2020.102469.
C. S. Throckmorton, V. Mohan, J. M. William and C. Leslie, “Financial fraud detection using vocal, linguistic and financial cues,” 2018.
Y. Pandey, “Credit card fraud detection using deep learning” Int. J. Adv. Res. Comput. Sci., vol. 8, no. 5, May–Jun. 2017. [15] Malik, Sanjay Kumar, and Sarika Chaudhary. "Comparative
study of decision tree algorithms for data analysis." International Journal of research in Computer Engineering and Electronic. Page1 2 (2013).
Kaur, Sonamdeep, Sarika Chaudhary, and Neha Bishnoi. "A Survey: Clustering Algorithms in Data Mining." International Journal of Computer Applications 975 (2015): 8887.
Mandiratta, Sonam, Pooja Batra Nagpal, and Sarika Chaudhary. "A Perlustration of Various Image Segmentation Techniques." International Journal of Computer Applications 139.12 (2016).
Sarika Chaudhary, Yojna Arora, Neelam Yadav (2020). Optimization of Random Forest Algorithm for Breast Cancer Detection IJIRCST Vol-8 Issue-3 Page No-63-66.
Chaudhary,S., Nagpal.P (2019). “Live location tracker” , Global Research and Development Journal for Engineering, | Volume 4, Issue 10
