Supervised Machine Learning Algorithms for Credit Card Fraudulent Transaction Detection

Authors

  • Karthik R  Computer Science and Engineering, Sri Krishna College of Engineering & Technology, Coimbatore, Tamil Nadu, India
  • Navinkumar R  Computer Science and Engineering, Sri Krishna College of Engineering & Technology, Coimbatore, Tamil Nadu, India
  • Rammkumar U  Computer Science and Engineering, Sri Krishna College of Engineering & Technology, Coimbatore, Tamil Nadu, India
  • Mothilal K. C.  Computer Science and Engineering, Sri Krishna College of Engineering & Technology, Coimbatore, Tamil Nadu, India

DOI:

https://doi.org//10.32628/CSEIT195274

Keywords:

Credit Card Fraud, Online Fraud, Cashless Transactions, Neural Network

Abstract

Cashless transactions such as online transactions, credit card transactions, and mobile wallet are becoming more popular in financial transactions nowadays. With increased number of such cashless transaction, number of fraudulent transactions is also increasing. Fraud can be distinguished by analyzing spending behavior of customers (users) from previous transaction data. Credit card fraud has highly imbalanced publicly available datasets. In this paper, we apply many supervised machine learning algorithms to detect credit card fraudulent transactions using a real-world dataset. Furthermore, we employ these algorithms to implement a super classifier using ensemble learning methods. We identify the most important variables that may lead to higher accuracy in credit card fraudulent transaction detection. Additionally, we compare and discuss the performance of various supervised machine learning algorithms that exist in literature against the super classifier that we implemented in this paper.

References

  1. Statista the statistic portal (2017, March 14) available https://www.statista.com/topics/871/online-shopping/
  2. Tanmay Kumar Behera, Suvasini Panigrahi, “Credit Card Fraud Detection: A Hybrid Approach Using Fuzzy Clustering & Neural Network”, IEEE Computer Society, 2015
  3. Kang Fu, Dawei Cheng, Yi Tu, and Liqing Zhang, “Credit Card Fraud Detection Using Convolutional Neural Networks”, Springer International Publishing AG 2016.
  4. Smt.S.Rajani, Prof.M. Padmavathamma, “A Model for Rule Based Fraud Detection in telecommunications”, International Journal of Engineering Research & Technology (IJERT), Vol. 1 Issue 5, July –2012.
  5. Hidden Markov model (2017, March 15) available https://en.wikipedia.org/wiki/Hidden_Markov_model [6] Y. Sahin and E. Duman, “Detecting Credit Card Fraud by Decision Trees and Support Vector Machines”, IMECS vol 1, 2011.
  6. Abhinav Srivastava, Amlan Kundu, Shamik Sural, Senior Member, IEEE, and Arun K. Majumdar, Senior Member, IEEE , “Credit Card Fraud Detection Using Hidden Markov Model” , IEEE transactions on dependable and secure computing, vol. 5, no. 1, january-march 2008.
  7. Michael Nielsen (2017, March 15), Deep learning available http://neuralnetworksanddeeplearning.com/chap6.html [9] Ghosh, S., Reilly, D.L.: Credit card fraud detection with a neuralnetwork. In: Proceedings of the Twenty-Seventh Hawaii International Conference on System Sciences, 1994, vol. 3, pp. 621–630. IEEE (1994)
  8. Emin Aleskerov, Bernd fieisleben and Bharat Rao, “CARDWATCH: A Neural Ntwork based database Mining System for Credit Card Fraud Detection”
  9. Sam Maes, Karl Tuyls, Bram Vanschoenwinkel, Bernard Manderick, “Credit card fraud detection using bayesian and neural networks”, International Naiso Congress on Neuro Fuzzy Technology, 2002.
  10. Minewiskan, Microsoft Neural Network Algorithm Technical Reference (2017, March 14) available at https://docs.microsoft.com/enus/ sql/analysis-services/data-mining/microsoft-neural-networkalgorithm- technical-reference
  11. Krishna Modi, Bhavesh Oza, “Outlier Analysis Approaches in Data Mining”, IJIRT vol 3 issue 7. [14] Raghavendra Patidar, Lokesh Sharma, “Credit card fraud detection using Neural Network”, IJSCE Volume-1, Issue-NCAI2011, June 2011.
  12. Alejandro Correa Bahnsen, Djamila Aouada, Aleksandar Stojanovic, Björn Ottersten, “Feature engineering strategies for credit card fraud detection”, 0957-4174/ 2016 Elsevier.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2019-04-30

Issue

Volume 5, Issue 2, March-April-2019

Section

Research Articles

License

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

How to Cite

[1]
Karthik R, Navinkumar R, Rammkumar U, Mothilal K. C., " Supervised Machine Learning Algorithms for Credit Card Fraudulent Transaction Detection, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 2, pp.394-401, March-April-2019. Available at doi : https://doi.org/10.32628/CSEIT195274