A Hybrid Approach for Credit Card Fraud Detection Using Naive Bayes and Voting Classifier

  • Bhagwant Jot KaurEmail author
  • Rakesh Kumar
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 49)


The prediction analysis is the approach which is applied to predict future possibilities from the current data. One need not pay cash, the cardholder just gives his card to the shopkeeper, he will swipe the card and the payment will be done in just fractions of a second. Sometimes the credit card can be stolen, and then the attacker can make false transactions. In this type of conditions, the credit card company faces a huge loss. In the existing research work, the voting-based classification approach is applied for credit card fraud detection. The voting based classification is a combination of multiple classifiers like SVM, decision tree etc. The classifier will have maximum accuracy will display its predicted result. To improve the accuracy of prediction analysis the voting based classification method will be replaced with naïve bayes classification approach. The naïve bayes classifier is the probability based classifier for credit card fraud detection. In the probability based classification method, the probabilities of the target classes are calculated and the probability of the test data is calculated. The test set which is near to the probability class is identified as the target set. The naïve bayes classification approach will improve the accuracy of credit card fraud detection. The proposed methodology will be implemented in python and results will be analyzed in terms of accuracy, precision, recall and F-measure. The naïve Bayes classifier optimizes the results in terms of accuracy, precision, recall and f-measure is optimized up to 10 to 15% for the credit card fraud detection.


Voting based classification Hybrid Ada boost Naïve Bayes Decision tree K-NN classifier SVM 


  1. 1.
    Duman, E., Ozcelik, M.H.: Detecting credit card fraud by genetic algorithm and scatter search. Expert Syst. Appl. 38(12), 13057–13063 (2011)CrossRefGoogle Scholar
  2. 2.
    Raj, S.B.E., Portia, A.A.: Analysis on credit card fraud detection methods. In: International Conference on Computer, Communication and Electrical Technology – ICCCET, vol. 19, no. 15, pp. 212–243, March 2011Google Scholar
  3. 3.
    Sahin, Y., Duman, E.: Detecting credit card fraud by decision trees and support vector machines. In: International Multi-conference of Engineers and Computer Scientists, vol. 56, no. 22, pp. 131–200, March 2011Google Scholar
  4. 4.
    Sunita, S., Chandrakanta, B.J., Chinmayee, R.: A hybrid approach of intrusion detection using ANN and FCM. Eur. J. Adv. Eng. Technol. 3(2), 6–14 (2016)Google Scholar
  5. 5.
    Singh, A., Narayan, D.: A survey on hidden Markov model for credit card fraud detection. Int. J. Eng. Adv. Technol. (IJEAT) 1(3), 2249–8958 (2012)Google Scholar
  6. 6.
    Esakkiraj, S., Chidambaram, S.: A predictive approach for fraud detection using hidden Markov model. Int. J. Eng. Res. Technol. (IJERT) 2(23), 156–166 (2013)Google Scholar
  7. 7.
    Ogwueleka, F.N.: Data mining application in credit card fraud detection system. J. Eng. Sci. Technol. 6(3), 311–322 (2011)Google Scholar
  8. 8.
    Ibrahim, L.M.: Anomaly network intrusion detection system based on distributed time-delay neural network (DTDNN). J. Eng. Sci. Technol. (JESTEC) 5(4), 457–471 (2010)MathSciNetGoogle Scholar
  9. 9.
    Ogwueleka, F.N., Inyiama, H.C.: Credit card fraud detection using artificial neural networks with a rule-based component. IUP J. Sci. Technol. 5(1), 40–47 (2009)Google Scholar
  10. 10.
    Dhankhad, S., Mohammed, E.A., Far, B.: Supervised machine learning algorithms for credit card fraudulent transaction detection: a comparative study. In: 2018 International Conference on Information Reuse and Integration for Data Science, vol. 16, no. 19, pp. 12–30, April 2018Google Scholar
  11. 11.
    Zheng, L., Liu, G., Yan, C., Jiang, C.: Transaction fraud detection based on total order relation and behavior diversity. IEEE Trans. Comput. Soc. Syst. 5(3), 56–63 (2018)CrossRefGoogle Scholar
  12. 12.
    Gahlaut, A., Tushar, Singh, P.K.: Prediction analysis of risky credit using data mining classification models. In: 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT), vol. 5, no. 76, pp. 45–50, June 2017Google Scholar
  13. 13.
    Kho, J.R.D., Vea, L.A.: Credit card fraud detection based on transaction behavior. In: Proceedings of the 2017 IEEE Region 10 Conference, TENCON, vol. 55, no. 67, pp. 25–35, March 2017Google Scholar
  14. 14.
    Kavitha, M., Suriakala, M.: Real time credit card fraud detection on huge imbalanced data using meta-classifiers. In: Proceedings of the International Conference on Inventive Computing and Informatics (ICICI), vol. 45, no. 76, pp. 34–40, April 2017Google Scholar
  15. 15.
    Carcillo, F., Le Borgne, Y.-A., Caelen, O., Bontemp, G.: An assessment of streaming active learning strategies for real-life credit card fraud detection. In: International Conference on Data Science and Advanced Analytics, vol. 55, no. 67, pp. 23–30, August 2017Google Scholar
  16. 16.
    Randhawa, K., Loo, C.K.: Credit card fraud detection using AdaBoost and majority voting. IEEE Access 6(13), 14277–14284 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.National Institute of Technical Teachers Training and Research, Panjab University, ChandigarhChandigarhIndia

Personalised recommendations