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Reseach Article

Fraud Detection in Current Scenario, Sophistications and Directions: A Comprehensive Survey

by M Kavitha, M. Suriakala
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 111 - Number 5
Year of Publication: 2015
Authors: M Kavitha, M. Suriakala
10.5120/19538-1194

M Kavitha, M. Suriakala . Fraud Detection in Current Scenario, Sophistications and Directions: A Comprehensive Survey. International Journal of Computer Applications. 111, 5 ( February 2015), 35-40. DOI=10.5120/19538-1194

@article{ 10.5120/19538-1194,
author = { M Kavitha, M. Suriakala },
title = { Fraud Detection in Current Scenario, Sophistications and Directions: A Comprehensive Survey },
journal = { International Journal of Computer Applications },
issue_date = { February 2015 },
volume = { 111 },
number = { 5 },
month = { February },
year = { 2015 },
issn = { 0975-8887 },
pages = { 35-40 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume111/number5/19538-1194/ },
doi = { 10.5120/19538-1194 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:47:06.516439+05:30
%A M Kavitha
%A M. Suriakala
%T Fraud Detection in Current Scenario, Sophistications and Directions: A Comprehensive Survey
%J International Journal of Computer Applications
%@ 0975-8887
%V 111
%N 5
%P 35-40
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Fraud Detection is one of the oldest areas of research. The requirement of an effective system that detects frauds effectively with zero loss exists until now. This is due to the increase in the technology, that influences both the ends; the user and the fraudster. Hence it becomes mandatory that the users need to stay a step ahead in this scenario. This paper discusses the changes that had taken place in the area of fraud detection. The flow of research from data mining approaches to machine learning approaches that were developed to defy the attacks are discussed here. It discusses the evolution of heuristic based mechanisms and graph based technologies that emerged in recent years. Further, it also discusses the need for Big Data based analysis in this domain. A few case studies are also discussed here to enable better understanding. Research challenges that exist in this domain in the current scenario are discussed along with the research directions.

References
  1. Rahm, Erhard, and Hong Hai Do. 2000. Data cleaning: Problems and current approaches. IEEE Data Eng. Bull. 23. 4: 3-13.
  2. Liu, Ou, et al. 2009. On an ant colony-based approach for business fraud detection. Emerging Intelligent Computing Technology and Applications. Springer Berlin Heidelberg, 1104-1111.
  3. Jia-jie, Shen, 2012. Electronic transaction fraud detection based on improved PSO algorithm. Computer Science and Network Technology (ICCSNT), 2012 2nd International Conference on. IEEE.
  4. Elías, Arturo, et al. 2011. Outlier analysis for plastic card fraud detection a hybridized and multi-objective approach. Hybrid Artificial Intelligent Systems. Springer Berlin Heidelberg, 1-9.
  5. Alowais, Mohammed Ibrahim, and Lay-Ki Soon. 2012. Credit Card Fraud Detection: Personalized or Aggregated Model. Mobile, Ubiquitous, and Intelligent Computing (MUSIC), 2012 Third FTRA International Conference on. IEEE.
  6. Rong-Chang Chen; Shu-Ting Luo; Xun Liang, Lee, V. C. S. 2005. Personalized Approach Based on SVM and ANN for Detecting Credit Card Fraud. Neural Networks and Brain, ICNN&B '05. International Conference on , vol. 2,no. , pp. 810-815, 13-15.
  7. Hormozi, Elham, et al. 2013. Accuracy evaluation of a credit card fraud detection system on Hadoop MapReduce. Information and Knowledge Technology (IKT), 2013 5th Conference on. IEEE.
  8. Hormozi, Hadi, et al. 2013. Credit cards fraud detection by negative selection algorithm on hadoop (To reduce the training time). Information and Knowledge Technology (IKT), 2013 5th Conference on. IEEE.
  9. Duman, Ekrem, AyseBuyukkaya, and IlkerElikucuk. 2013. A Novel and Successful Credit Card Fraud Detection System Implemented in a Turkish Bank. Data Mining Workshops (ICDMW), 2013 IEEE 13th International Conference on. IEEE.
  10. Bahnsen, Alejandro Correa, et al. 2013. Cost sensitive credit card fraud detection using Bayes minimum risk. Machine Learning and Applications (ICMLA), 2013 12th International Conference on. Vol. 1. IEEE.
  11. Gurjar, Ram Niwas, Neeraj Sharma, and ManojWadhwa. 2014. Finding outliers using mutual nearness based ranks detection algorithm. Optimization, Reliabilty, and Information Technology (ICROIT), 2014 International Conference on. IEEE.
  12. Cao, Lei, et al. 2014. Scalable distance-based outlier detection over high-volume data streams. Data Engineering (ICDE), 2014 IEEE 30th International Conference on. IEEE.
  13. Sahin, Yusuf, SerolBulkan, and EkremDuman, 2013. A cost-sensitive decision tree approach for fraud detection. Expert Systems with Applications 40. 15: 5916-5923.
  14. Duman, Ekrem, and M. HamdiOzcelik. 2011. Detecting credit card fraud by genetic algorithm and scatter search. Expert Systems with Applications 38. 10: 13057-13063.
  15. Wei, Wei, et al. 2013. Effective detection of sophisticated online banking fraud on extremely imbalanced data. World Wide Web 16. 4: 449-475.
  16. Kim, Ae Chan, et al. 2014. Fraud and financial crime detection model using malware forensics. Multimedia Tools and Applications 68. 2: 479-496.
  17. http://neo4j. com/
  18. http://www. cms. gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/Medicare-Provider-Charge-Data/Inpatient. html
  19. http://www. cms. gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/Medicare-Provider-Charge-Data/Outpatient. html
  20. https://www. cs. cmu. edu/~. /enron/
  21. http://weka. 8497. n7. nabble. com/file/n23121/credit_fruad. arff
  22. http://archive. ics. uci. edu/ml/datasets/Statlog+%28Australian+Credit+Approval%29.
  23. http://archive. ics. uci. edu/ml/datasets/Statlog+%28German+Credit+Data%29
Index Terms

Computer Science
Information Sciences

Keywords

Fraud detection Challenges Graph databases Graph based fraud detection Big Data in fraud