A Review - ATM Card Fraud Detection Using Hidden Markov Model

Authors

  • Gupta Prashant  Al-Ameen College of Engineering,Koregaon Bhima, Savitribai Phule Pune University, Pune, India
  • Farhan Saudagar  Al-Ameen College of Engineering,Koregaon Bhima, Savitribai Phule Pune University, Pune, India
  • Afsari Raut  Al-Ameen College of Engineering,Koregaon Bhima, Savitribai Phule Pune University, Pune, India
  • Prof. Sonawane V. D  Al-Ameen College of Engineering,Koregaon Bhima, Savitribai Phule Pune University, Pune, India
  • Prof. Sultana Sayyed  Al-Ameen College of Engineering,Koregaon Bhima, Savitribai Phule Pune University, Pune, India

Keywords:

HSAP, HMM, FDS, Patterns, Transaction, Location, Fraud.

Abstract

ATM card fraud is causing billions of dollars in losses for the card payment industry. In today’s world the most accepted payment mode is ATM card for both online and also for regular purchasing; hence frauds related with it are also growing. To find the fraudulent transaction, we implement an Advanced Security Model for ATM payment using Hidden Markov Model (HMM), which detects the fraud by using customers spending behavior. This Security Model is primarily focusing on the normal spending behavior of a cardholder and some advanced securities such as Location, Amount, Time and Sequence of transactions. If the trained Security model identifies any misbehavior in upcoming transaction, then that transaction is permanently blocked until the user enter High Security Alert Password (HSAP). This paper provides an overview of frauds and begins with ATM card statistics and the definition of ATM card fraud. The main outcome of the paper is to find the fraudulent transaction and avoids the fraud before it happens.

References

  1. Abhinav Shrivastava , “Credit Card Fraud Detection Using Hidden Markov Model”, IEEE Transaction, VOL 5 No.1, March 2008.
  2. Ashish Gupta,Jagdish Raikwal, “Fraud Detection in credit Card Transaction Using Hybrid Model” ,International Journal of engineering and computer science ISSN-2319-7242 volume 3 issue,1 Jan, 2014.
  3. Fabio Cuzzolin and Michael Spanienza, “Learning Pullback HMM Distances”, IEEE Trans, August 2013.
  4. Khyati Chaudhary, Jyoti Yadav Bhawna Mallick, “A review of Fraud Detection Techniques: Credit Card”, International Journal of Computer Applications, May 2012.
  5. 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.
  6. Mubeena Syeda, Yan-Qing Zhang and Yi Pan, “Parallel Granular Neural Networks Detection for Fast Credit Card Fraud Detection”, IEEE, 2002.
  7. Purval kharat, Ankush bhat, Rohitwattal, Yogesh kamble.“Securing Financial Transactions Using Hmm And Location Based Service”13-Apr-2015.
  8. Renu and Suman, “Analysis on Credit Card Fraud Detection Methods”, International Journal of Computer Trends and Technology, 1 Feb 2014.
  9. amruddhi Belan, Sujata Mane, Tejas, “Fraud Detection in Online Banking Using Hidden Markov MODEL” 1 February 2014.
  10. V. Bhusari. , S Patil, “Study of Hidden Markov Model in Credit Card Fraudulent Detection”, International Journal of Computer Applications (0975 – 8887) Volume 20– No.5, April, 2011

Downloads

Published

2016-02-28

Issue

Section

Research Articles

How to Cite

[1]
Gupta Prashant, Farhan Saudagar, Afsari Raut, Prof. Sonawane V. D, Prof. Sultana Sayyed, " A Review - ATM Card Fraud Detection Using Hidden Markov Model, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 2, Issue 1, pp.592-596, January-February-2016.