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International Journal of Computer Science Engineering Techniques(ijcse)

Title : Real-Time Credit Card Fraud Detection Using Machine Learning

ISSN : 2455-135X

Year of Publication : 2020

10.29126/2455135X/IJCSE-V5I2P6

- Authors :

Dr.E.Punarselvam, G.Nivedhitha, B.Ilavarasan, R.Naveen Kishore,C.S.PranavAdhithya,P.Prithivi Raj





MLA Style: Dr.E.Punarselvam, G.Nivedhitha, B.Ilavarasan, R.Naveen Kishore,C.S.PranavAdhithya,P.Prithivi Raj, "Real-Time Credit Card Fraud Detection Using Machine Learning" Volume 5 - Issue 2 March - April,2020 International Journal of Computer Science Engineering Techniques (IJCSE), ISSN:2455-135X, www.ijcsejournal.org

APA Style:

Dr.E.Punarselvam, G.Nivedhitha, B.Ilavarasan, R.Naveen Kishore,C.S.PranavAdhithya,P.Prithivi Raj, "Real-Time Credit Card Fraud Detection Using Machine Learning" Volume 5 - Issue 2 March - April,2020 International Journal of Computer Science Engineering Techniques (IJCSE), ISSN:2455-135X, www.ijcsejournal.org



Abstract

Credit card fraud events take place frequently and then result in huge financial losses. The number of online transactions has grown in large quantities and online credit card transactions hold a huge share of these transactions. Online transactions have become an important and necessary part of our lives. As frequency of transactions is increasing, number of fraudulent transactions is also increasing rapidly. As credit card becomes the most popular mode of payment for both online as well as regular purchase, cases of fraud associated with it are also rising. Implementation of efficient fraud detection systems has thus become imperative for all credit card issuing banks to minimize their losses. Many modern techniques based on Artificial Intelligence, Data mining, Fuzzy logic, Machine learning, Sequence Alignment, Genetic Programming etc., has evolved in detecting various credit card fraudulent transactions. A clear understanding on all these approaches will certainly lead to an efficient credit card fraud detection system. The most commonly used fraud detection methods are Neural Network (NN), rule-induction techniques, fuzzy system, decision trees, Support Vector Machines (SVM), Artificial Immune System (AIS), genetic algorithms, K-Nearest Neighbor algorithms. These techniques can be used alone or in collaboration using ensemble or meta-learning techniques to build classifiers. This thesis presents a survey of various techniques used in credit card fraud detection and evaluates each methodology based on certain design criteria.


Reference

[1] V. Bhusari and S. Patil.(2011). Use of concealed markov show in Visa misrepresentation discovery.Worldwide Journal of Distributed and Parallel Systems (IJDPS) Vol.2, No.6. [2] E.Punarselvam,“Robust Facial Expression Recognition using Local Directional Number Version”, International Journal of Innovative Research in Science, Engineering and Technology, ISSN(Online) : 2319 – 8753,ISSN (Print) : 2347-6710 Vol. 4, Special Issue 6,May 2015,pp 182- 186 [3] Sen, Sanjay Kumar., and Dash, Sujatha. (2013). Meta learning calculations for charge card extortion location. Universal Journal of Engineering Research and Development Volume 6, Issue 6, pp. 16-20. [4] Dr.E.Punarselvam,“Supervised and Semi Supervised Machine Learning Clustering Algorithm based on feature selection”, International Journal on Applications in Information and Communication Engineering, Volume 5 : Issue 2: November 2019, PP 19 –24, ISSN (Online) : 2394 – 6237 [5] N.Malini and Dr.M.Pushpa , "Analysis on Credit Card Fraud Identification Techniques based on KNN and Outlier Detection" , 3rd International Conference on Advances in Electrical, Electronics, Information, Communication and BioInformatics (AEEEICB17) , 2017 [6] Dr.E.Punarselvam,“Effective and Efficient Traffic Scrutiny in Sweet Server with Data Privacy”, International Journal on Applications in Information and Communication Engineering Volume 5 : Issue 2: November 2019, PP 1 – 5. [7] John Richard D. Kho and Larry A. Vea, “Credit card Fraud detection based on transaction Behavior”, IEEE Region 10 Conference (TENCON), Malaysia, pp 1880 – 1884 , November 2017 [8] Fahimeh Ghobadi and Mohsen Rohani, "Cost Sensitive Modeling of Credit CardFraud Using Neural Network Strategy" , IEEE ICSPIS 2016, Dec 2016 [9] Dr.E.Punarselvam,“Effective and Efficient Traffic Scrutiny in Sweet Server with Data Privacy”, International Journal on Applications in Information and Communication Engineering Volume 5 : Issue 2: November 2019, PP 1 – 5 [10] Sarween Zaza and Mostafa Al-Emran, "Mining and Exploration of Credit Cards Data in UAE", Fifth International Conference on e-Learning , pp 275-79 , 2015 [11] E.Punarselvam, “Big Data using Hadoop Database using python Language to implement Real Time Applications”, International Journal of Engineering Research and development,Vol.8 Issue No.12 Oct 2013 PP(19-22) e-ISSN:2278- 067X,p-ISSN:2278-800X. [12] Rajeshwari U and Dr B Sathish Babu, “Real-time credit card fraud detection using Streaming Analytics”, 2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), pp 439 – 444, 2016


Keywords

Data mining, Fuzzy logic, Machine learning, NN, SVM, AIS, K-Nearest Neighbor Algorithm.