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Number 9

A Novel Method to Detect False Financial Statement using Negative Selection Algorithm

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  • International Journal of Applied Information Systems
  • Foundation of Computer Science (FCS), NY, USA
  • Volume 7 - Number 9
  • Year of Publication:
  • Authors: U. Jothi Lakshmi
  • 10.5120/ijais14-451209
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  • U. Jothi Lakshmi 2014. A Novel Method to Detect False Financial Statement using Negative Selection Algorithm. International Journal of Applied Information Systems. 7, 9 (September 2014), 1-5. DOI=http://dx.doi.org/10.5120/ijais451209 
  • @article{10.5120/ijais2017451568,
author = {U. Jothi Lakshmi},
title = {A Novel Method to Detect False Financial Statement using Negative Selection Algorithm},
journal = {International Journal of Applied Information Systems},
issue_date = {September 2014},
volume = {7},
number = {},
month = {September},
year = {2014},
issn = {},
pages = {1-5},
numpages = {},
url = {/archives/volume7/number9/678-1209},
doi = { 10.5120/ijais14-451209},
publisher = { xA9 2013 by IJAIS Journal},
address = {}
}

  • %1 451209
%A U.  Jothi Lakshmi
%T A Novel Method to Detect False Financial Statement using Negative Selection Algorithm
%J International Journal of Applied Information Systems
%@ 
%V 7
%N 
%P 1-5
%D 2014
%I  xA9 2013 by IJAIS Journal

Abstract

Financial statement fraud is one of the biggest challenges in the modern business world. It affects various sectors of people including the fraudsters, auditor and the public. Above all the economic growth of a country diminishes adversely. So the need to prevent such fraud is very important. But as the fraudsters are so adaptive to new trends it is hard to develop a preventive mechanism. And the job of auditors is very much time consuming that the chance of misinterpretation is also high in nature. Hence this paper proposes a detection mechanism - that include artificial immune algorithm - is supposed to be capable of detecting false financial statement effectively.

References

  1. www. passia. org/
  2. http://www. investorwords. com/
  3. Detection of financial statement fraud and feature selection using data mining techniques P. Ravisankar , V. Ravi , G. Raghava Rao , I. Bose , Decision Support Systems 50 (2011) 491–500,Elsievier.
  4. The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature, E. W. T. Ngai , Yong Hu ,Y. H. Wong , Yijun Chen , Xin Sun , Decision Support Systems 50 (2011) 559–569, Elsievier.
  5. Detecting evolutionary financial statement fraud Wei Zhou , Gaurav Kapoor , Decision Support Systems 50 (2011) 570–575, Elsievier.
  6. A computational model for financial reporting fraud detection Fletcher H. Glancy , Surya B. Yadav, Decision Support Systems 50 (2011) 595–601, Elsievier.
  7. An Investigation of the Negative Selection Algorithm for Fault Detection in Refrigeration Systems, Dan W Taylor1,2 and David W Corne1, ICARIS 2003, LNCS 2787, pp. 34-45 2003, Springer.
  8. Stephanie Forrest, Alan S Perelson, Lawrence Allen and Rajesh Cherukuri "Self-Nonself Discrimination in a Computer", Proceedings of the IEEE Symposium on Research in Security and Privacy, IEEE Press (1994)
  9. Revisiting Negative Selection Algorithms, Zhou Ji , AutoZone, Inc. , Memphis, TN 38103, USA. Dipankar Dasgupta dasgupta@memphis. eduDepartment of Computer Science, The University of Memphis, Memphis, TN 38152,USA

Keywords

FFS (False Financial Statement), Negative Selection Algorithm, Artificial Immune System.

Index Terms

Computer Science
Information Sciences