A Comparative Study of Financial Transaction Cards - Credit & Debit Cards

Authors

  • Sivakumar Nadarajan  Research Scholar, PG and Research Department of Computer Science, J. J. College of Arts and Science, Bharathidasan University, Tiruchirappalli, Tamil Nadu, India

Keywords:

Debit Card, Credit Card, Cashless, E-Commerce, Fraudsters.

Abstract

In today's world the use of debit and credit card have become so widespread that their volume has overtaken or entirely replaced cheques and, in some instances, cash transactions. Since the transactions are cashless and are performed on-line, it becomes the most popular mode of payment. Increase in e-commerce and the ease of online transactions and payments has led to an exponential increase in the number of people opting for online purchases. This has automatically led to an increase in the number of fraudsters trying to exploit the transparence involved in online transactions. This article defines the most common things of debit card and credit card transactions and compares both credit and debit card nature.

References

  1. Fabio Cuzzolin and Michael Spanienza, “Learning Pullback HMM Distances”, IEEE Trans, August 2013.
  2. KhyatiChaudhary, JyotiYadavBhawnaMallick, “A review of Fraud Detection Techniques: Credit Card”, International Journal of Computer Applications,May 2012.
  3. Lean Yu Shouyang Wang, “Kin Keung Lai “Credit risk assessment with a multistage neural network ensemble learning approach”, Expert Systems with Applications, Elsevier-Feb 1, 2008.
  4. Bolton, R. & Hand, D. 2002. ‘Statistical Fraud Detection: A Review’. Statistical Science, 17; 235-249.
  5. Bolton, R. & Hand, D. 2001. Unsupervised Profiling Methods for Fraud Detection, Credit Scoring and Credit Control VII.
  6. Sivakumar Nadarajan, Dr.Balasubramanian Ramanujam, “Article: Enhanced Anomaly Detection in Imbalanced Credit Card Transactions using Hybrid PSO”.International Journal of Computer Applications 135(10):28-32, 2016.
  7. Brause R., Langsdorf T. & M Hepp. 1999a. Credit card fraud detection by adaptive neural data mining, Internal Report 7/99 (J. W. Goethe-University, Computer Science Department, Frankfurt, Germany).
  8. Brause, R., Langsdorf, T. & M Hepp. 1999b. Neural Data Mining for Credit Card Fraud Detection, Proc. of 11th IEEE International Conference on Tools with Artificial Intelligence.
  9. Caminer, B. 1985. ‘Credit card Fraud: The Neglected Crime’. The Journal of Criminal Law and Criminology, 76;746-763.
  10. Chan, P., Fan, W. Prodromidis, A. & S Stolfo. 1999. ‘Distributed Data Mining in Credit Card Fraud Detection’. IEEE Intelligent Systems, 14; 67-74.
  11. Sivakumar Nadarajan, Dr.Balasubramanian Ramanujam, “Encountering Imbalance in Credit Card Fraud Detection with Metaheuristics” Advances in Natural and Applied Sciences. 10(8); Pages: 33-41,2016.
  12. Sivakumar Nadarajan, Dr.Balasubramanian Ramanujam, “Fast and Effective Credit Card Fraud Detection in Imbalanced Data using Parallel Hybrid PSO” International Journal of Advanced Research in Science, Engineering and Technology, Vol. 3, Issue 9 , September 2016
  13. Ding Wang, Ping Wang, Chun-guang Ma and Zhong Chen “Robust Smart Card based Password Authentication Scheme against Smart Card Security Breach”
  14. Sudarshan K. Valluru “Design and Assemble of Low Cost Prepaid Smart Card Energy Meter – A Novel Design” International Journal on Electrical Engineering and Informatics ‐ Volume 6, Number 1, March 2014

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Published

2017-12-31

Issue

Section

Research Articles

How to Cite

[1]
Sivakumar Nadarajan, " A Comparative Study of Financial Transaction Cards - Credit & Debit Cards, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 6, pp.694-698, November-December-2017.