CFP last date
22 April 2024
Reseach Article

Database Implementation and Testing of Dynamic Credit Card Fraud Detection System

by Anita Jog, Anjali Chandavale
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 168 - Number 11
Year of Publication: 2017
Authors: Anita Jog, Anjali Chandavale
10.5120/ijca2017914557

Anita Jog, Anjali Chandavale . Database Implementation and Testing of Dynamic Credit Card Fraud Detection System. International Journal of Computer Applications. 168, 11 ( Jun 2017), 42-47. DOI=10.5120/ijca2017914557

@article{ 10.5120/ijca2017914557,
author = { Anita Jog, Anjali Chandavale },
title = { Database Implementation and Testing of Dynamic Credit Card Fraud Detection System },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2017 },
volume = { 168 },
number = { 11 },
month = { Jun },
year = { 2017 },
issn = { 0975-8887 },
pages = { 42-47 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume168/number11/27923-2017914557/ },
doi = { 10.5120/ijca2017914557 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:15:54.935008+05:30
%A Anita Jog
%A Anjali Chandavale
%T Database Implementation and Testing of Dynamic Credit Card Fraud Detection System
%J International Journal of Computer Applications
%@ 0975-8887
%V 168
%N 11
%P 42-47
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Credit card frauds are increasing with the increase in use of plastic money. These frauds include the transactions done either by stealing the physical card or using card data such as card number, expiry date and pin number. There is a need to recognize customer spending pattern and apply validations for incoming transaction. Suspicious transactions can go under rigorous security checks. This paper describes the database implementation of credit card fraud detection system which is adaptive to concept drift environment. The system is designed using PL-SQL stored procedures and JAVA. The validation procedure and testing results are included in this paper.

References
  1. Emanuel MinedaCarneiro, “Cluster Analysis and Artificial Neural Networks: A Case Study in Credit Card Fraud Detection,” in 2015 IEEE International Con- ference
  2. V. Mareeswari, “Prevention of Credit Card Fraud Detection based on HSVM”. 2016 IEEE International Conference On Information Communication And Em- bedded System.
  3. Carlos A. S. Assis, “A Genetic Programming Approach for Fraud Detection in Electronic Transactions” in Advances in Computing and Communication Engi- neering (ICACCE), 2015 Second International Conference
  4. Andrea Dal Pozzolo, "Credit Card Fraud Detection and Concept-Drift Adaptation with Delayed Supervised Information",
  5. Véronique Van Vlasselaer, "APATE: A novel approach for automated credit card transaction fraud detection using network-based extensions" published in Deci- sion Support Systems 2015
  6. Dhiya Al-Jumeily, "Methods and Techniques to Support the Development of Fraud Detection System", IEEE 2015
  7. Dustin Y. Harvey, "Automated Feature Design for Numeric Sequence Classifica- tion by Genetic Programming", IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, VOL. 19, NO. 4, AUGUST 2015
  8. Mukesh Kumar Mishra, "A Comparative Study of Chebyshev Functional Link Artificial Neural Network, Multi-layer Perceptron and Decision Tree for Credit Card Fraud Detection", 2014 13th International Conference on Information Technology
  9. Sahin Yusuf, BulkanSerol, DumanEkrem,”A Cost-Sensitive Decision Tree Approach for Fraud Detection”, Expert Systems with Applications, vol.40, pp.5916-5923, 2013
  10. Kang Fu, Dawei Cheng, Yi Tu, and Liqing Zhang, “Credit Card Fraud Detection Using Convolutional Neural Networks”, Neural Information Processing, Springer Andrea Dal Pozzolo, Olivier Caelen,” Learned lessons in credit card fraud detec- tion from a practitioner perspective” , Expert Systems with Applications 41,2014.
  11. How a credit card is processed https://www.creditcards.com/credit-card-news/assets/HowACreditCardIsProcessed.pdf
  12. Global Card Fraud Damages Reach $16B http://www.pymnts.com/news/2015/global-card-fraud-damages-reach-16b/
  13. Credit Card Fraud Detection https://www.kaggle.com/dalpozz/creditcardfraud
  14. Yiğit Kültür, "A Novel Cardholder Behavior Model for Detecting Credit Card Fraud”, IEEE international conference on commuting and communication engineering, 2015
Index Terms

Computer Science
Information Sciences

Keywords

Concept drift self learning credit card fraud detection.