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.
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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.