Credit Card Fraud Detection Using Random Forest Algorithm

Authors(3) :-G. Niveditha, K. Abarna, G. V. Akshaya

Credit card fraudulent happens through the account holder's card number, card details and personal information. E-commerce payment system is providing the payment for online transaction. The model is used to identify whether a new transaction is fraudulent or not. Aim is to detect 100% of the fraudulent transactions while minimizing the incorrect fraud classifications. A standard scalar model is initially trained with the normal behavior of a card holder. If an incoming credit card transaction is not accepted by the trained standard scalar model with sufficiently high probability, it is considered to be fraudulent, which defines a plot of test perception as the y coordinate versus its 1-specificity or false positive rate (FPR) as the x coordinate, is an effective method of estimate the quality or performance of diagnostic tests. The significance of the application technique reviewed in the minimization of credit card fraud. Still some issues when genuine credit card customers are misclassified as fraudulent. SMOTE is a statistical technique for increasing the number of cases in your dataset in a balanced way. Random forest builds multiple decision trees and integrate them together to get stable prediction and accuracy of about 98.6%.

Authors and Affiliations

G. Niveditha
Department of Computer Science and engineering, Sri Krishna College of Technology, Coimbatore, Tamilnadu, India
K. Abarna
Department of Computer Science and engineering, Sri Krishna College of Technology, Coimbatore, Tamilnadu, India
G. V. Akshaya
Department of Computer Science and engineering, Sri Krishna College of Technology, Coimbatore, Tamilnadu, India

SMOTE, ROC, FPR, Information Security, Data mining Techniques

  1. “Credit card Fraud Detection System using Hidden Markov Model and Adaptive Communal Detection”, International Journal of Computer Science and Information Technologies, vol 6 (2), 2016
  2. “Cost sensitive Modeling of Credit Card Fraud Using Neural Network strategy”, ICSPIS 2016, 14-15 Dec 2018, Amirkabir University of Technology Tehran, Iran.
  3. Analysis on Credit Card Fraud Detection Methods”International Journal of Computer Trends and Technology (IJCTT) – volume 8 number 1– Feb 2017 Nterchange-newline"
  4. Accenture Security (2017) Cost of cyber crime study. https://www.accenture.com/us-en/insight-cost-of-cybercrime-2017. Accessed 5 Jan 2018 Aha D, Kibler D (1991) Instance-based learning algorithms. Mach Learn 6:37–66 Al-Jarrah OY, Yoo PD, Muhaidat S, Karagiannidis GK Taha (2015) Efficient machine learning for big data: A review. Big DataRes2(3):87–93. https://doi.org/10.1016/j.bdr.2015.04.001
  5. Almukaynizi M, Nunes E, Dharaiya K, Senguttuva(2017) Proactive identification of exploits in the wild through vulnerability mentions online. In: Proceedings of the 2017 International Conference on Cyber Conflict (CyCon U.S.) pp 82–88
  6. Babko-Malaya O, Cathey R, Hinton S, Maimon D, Gladkova T (2017) Detection of hacking behaviors and communication patterns on social media. In: Proceedings of the 2017 IEEE International Conference on Big Data.pp 4636–4641
  7. Baumeister RF, Vohs KD, DeWall CN, Zhang L (2007) How emotion shapes behavior: Feedback, anticipation, and reflection, rather than direc tcausation. Personal Soc Psychol Rev 11(2):167–203 Bilge L, Han Y, Dell’Amico M (2017) Riskteller: Predicting the risk of cyber incidents. In: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security (CCS). ACM, New York.pp 1299– 1311. https://doi.org/10.1145/3133956.3134022
  8. Branco P, Torgo L, Ribeiro RP (2015) A survey of predictive modelling under imbalanced distributions. CoRR abs/1505.01658.http://arxiv.org/abs/1505.01658, 1505.01658
  9. Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) Smote: Synthetic minority over-sampling technique. J Artif Intell Res 16(1):321–357 Cooper GF, Herskovits E (1992) A bayesian method for the induction of probabilistic networks from data. Mach Learn 9(4):309–347
  10. Ayal B, MacGregor JF (1997) Recursive exponentially weighted PLS and its applications to adaptive control and prediction. J Process Control.

Publication Details

Published in : Volume 5 | Issue 2 | March-April 2019
Date of Publication : 2019-04-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 301-306
Manuscript Number : CSEIT195261
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

G. Niveditha, K. Abarna, G. V. Akshaya, "Credit Card Fraud Detection Using Random Forest Algorithm", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 2, pp.301-306, March-April-2019.
Journal URL : http://ijsrcseit.com/CSEIT195261

Article Preview