Analysis of-credit-card-fault-detection
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This document analyzes various methods for credit card fraud detection. It discusses techniques like Dempster-Shafer theory, BLAST-SSAHA hybridization, hidden Markov models, evolutionary-fuzzy systems, and using Bayesian and neural networks. The document also compares the different fraud detection systems based on parameters like accuracy, method, true positive rate, false positive rate, and training data needed. In conclusion, the document states that efficient fraud detection is required, and techniques like fuzzy Darwinian systems and neural networks show good accuracy, while hidden Markov models have a low fraud detection rate.
![References
[1]Amlan Kundu, Suvasini Panigrahi, Shamik Sural and Arun K. Majumdar, “BLAST-SSAHA
Hybridization for Credit Card Fraud Detection,” IEEE Transactions On Dependable And
Secure Computing, vol. 6, Issue no. 4, pp.309-315, October-December 2009.
[2] Amlan Kundu, Suvasini Panigrahi, Shamik Sural and Arun K. Majumdar, “Credit card fraud
detection: A fusion approach using Dempster–Shafer theory and Bayesian learning,” Special
Issue on Information Fusion in Computer Security, Vol. 10, Issue no 4, pp.354- 363, October
2009.
[3] Abhinav Srivastava, Amlan Kundu, Shamik Sural, Arun K.Majumdar, “Credit Card Fraud
Detection using Hidden Markov Model,”IEEE Transactions On Dependable And Secure
Computing, vol. 5, Issue no. 1, pp.37-48, January-March 2008.
[4] Peter J. Bentley, Jungwon Kim, Gil-Ho Jung and Jong-Uk Choi, “Fuzzy Darwinian Detection
of Credit Card Fraud,” In the 14th Annual Fall Symposium of the Korean Information
Processing Society, 14th October 2000.
[5] Sam Maes, Karl Tuyls, Bram Vanschoenwinkel, Bernard Manderick, “Credit card fraud
detection using Bayesian and neural networks,” Interactive image-guided neurosurgery,
pp.261-270, 1993.
[6] Amlan Kundu, S. Sural, A.K. Majumdar, “Two-Stage Credit Card Fraud Detection Using
Sequence Alignment,” Lecture Notes in Computer Science, Springer Verlag, Proceedings of
the International Conference on Information Systems Security, Vol. 4332/2006, pp.260- 275,
2006.
[7] Simon Haykin, “Neural Networks: A Comprehensive Foundation,”2nd Edition, pp.842, 1999.](https://image.slidesharecdn.com/analysis-of-credit-card-fault-detection-131218225420-phpapp02/85/Analysis-of-credit-card-fault-detection-13-320.jpg)
Analysis of-credit-card-fault-detection
- 1. ANALYSIS ON CREDIT CARD FRAUD DETECTION METHODS Presentation By ASHWINI G T 1AR08CS004 AIeMS Under the guidance of Ms. Sharmila Chidaravalli Assistant Professor, Department of CSE, AIeMS
- 3. Contents Introduction Fraud Detection Techniques Dempster–Shafer Theory BLAST-SSAHA Hybridization Hidden Markov Model Evolutionary-fuzzy System Using Bayesian and Neural Networks Conclusion References
- 4. Introduction The Credit Card is a small plastic card issued to users as a System of Payment. Credit Card Security relies on the Physical Security of the plastic card as well as the privacy of the Credit Card Number. Globalization and increased use of the Internet for Online Shopping has resulted in a considerable proliferation of Credit Card Transactions throughout the world. Credit Card Fraud is a wide-ranging term for theft and fraud committed using a Credit Card as a fraudulent source of funds.
- 5. Fraud Detection Techniques Dempster–Shafer Theory and Bayesian learning BLAST-SSAHA Hybridization Hidden Markov Model Fuzzy Darwinian Detection Bayesian and Neural Networks
- 10. Bayesian and Neural Networks It consists of tree layers namely input hidden and output layers. Bayesian networks also called as Belief networks.
- 11. Comparison of Various Fraud Detection Systems Parameters Used For Comparison Accuracy Method True Positive (TP) False Positive(FP) Training Data
- 12. Conclusion Efficient credit card fraud detection system is an utmost requirement for any card issuing bank. The Fuzzy Darwinian fraud detection systems improve the system accuracy. The Neural Network based CARDWATCH shows good accuracy in fraud detection and processing Speed. The fraud detection rate of Hidden Markov model is very low compare to other methods. The processing speed of BLAST-SSAHA is fast enough to enable on-line detection of credit card fraud. BLAH-FDS can be effectively used to counter frauds in other domains such as telecommunication and banking fraud detection.
- 13. References [1]Amlan Kundu, Suvasini Panigrahi, Shamik Sural and Arun K. Majumdar, “BLAST-SSAHA Hybridization for Credit Card Fraud Detection,” IEEE Transactions On Dependable And Secure Computing, vol. 6, Issue no. 4, pp.309-315, October-December 2009. [2] Amlan Kundu, Suvasini Panigrahi, Shamik Sural and Arun K. Majumdar, “Credit card fraud detection: A fusion approach using Dempster–Shafer theory and Bayesian learning,” Special Issue on Information Fusion in Computer Security, Vol. 10, Issue no 4, pp.354- 363, October 2009. [3] Abhinav Srivastava, Amlan Kundu, Shamik Sural, Arun K.Majumdar, “Credit Card Fraud Detection using Hidden Markov Model,”IEEE Transactions On Dependable And Secure Computing, vol. 5, Issue no. 1, pp.37-48, January-March 2008. [4] Peter J. Bentley, Jungwon Kim, Gil-Ho Jung and Jong-Uk Choi, “Fuzzy Darwinian Detection of Credit Card Fraud,” In the 14th Annual Fall Symposium of the Korean Information Processing Society, 14th October 2000. [5] Sam Maes, Karl Tuyls, Bram Vanschoenwinkel, Bernard Manderick, “Credit card fraud detection using Bayesian and neural networks,” Interactive image-guided neurosurgery, pp.261-270, 1993. [6] Amlan Kundu, S. Sural, A.K. Majumdar, “Two-Stage Credit Card Fraud Detection Using Sequence Alignment,” Lecture Notes in Computer Science, Springer Verlag, Proceedings of the International Conference on Information Systems Security, Vol. 4332/2006, pp.260- 275, 2006. [7] Simon Haykin, “Neural Networks: A Comprehensive Foundation,”2nd Edition, pp.842, 1999.
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- 15. THANK YOU