We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 November 2024
Call for Paper
December Edition
IJCA solicits high quality original research papers for the upcoming December edition of the journal. The last date of research paper submission is 20 November 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Fraud Detection in Supply Chain using Excel Sheet

by T N Varma, D A Khan
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 80 - Number 2
Year of Publication: 2013
Authors: T N Varma, D A Khan
10.5120/13833-0585

T N Varma, D A Khan . Fraud Detection in Supply Chain using Excel Sheet. International Journal of Computer Applications. 80, 2 ( October 2013), 20-25. DOI=10.5120/13833-0585

@article{ 10.5120/13833-0585,
author = { T N Varma, D A Khan },
title = { Fraud Detection in Supply Chain using Excel Sheet },
journal = { International Journal of Computer Applications },
issue_date = { October 2013 },
volume = { 80 },
number = { 2 },
month = { October },
year = { 2013 },
issn = { 0975-8887 },
pages = { 20-25 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume80/number2/13833-0585/ },
doi = { 10.5120/13833-0585 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:53:30.371138+05:30
%A T N Varma
%A D A Khan
%T Fraud Detection in Supply Chain using Excel Sheet
%J International Journal of Computer Applications
%@ 0975-8887
%V 80
%N 2
%P 20-25
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Fraud occurs due to intentionally manipulated data in complex and dynamic supply chains cyberspace being a complicated task for detecting or auditing agencies. However, prevention from vulnerable manipulation is the best way to reduce frauds. The application of Excel sheet as decision supporting tools, leads to identify abnormally mismatch or hidden pattern of data, and its depth analysis helps agencies to scientifically examine the feasibility of implementing one 'trust-but-verify' method in supply chain network using a probability distribution called Benford's distribution within a short span of time to detect and prevent fraudulent transactions. This paper demonstrates how to use Excel Sheet to perform Benford distribution statistical test as an effective tool for locating red flags in suspected data from decision-making data-set of supply chain network.

References
  1. Becker, P. (1982), ‘ Patterns in listings of failure-rate and MTTF values and listings of other data’, IEEE Transactions on Reliability, Vol.31,pp. 132-134.
  2. Benford, Frank (1938), ‘The Law of Anomalous Numbers’, The American Philosophical Society, Vol.7,No.4 ,pp. 551-72. JSTOR. Web. http://www.jstor.org (Accessed, July 15,2013)
  3. Berger, Arno, and Theodore P. Hill. (2007), ‘Newton's Method Obeys Benford's Law’, American Mathematical Monthly Vol. 114, No.7, pp.588-60I.
  4. Buck, B., Merchant, A. and S. Perez (1993), ‘ An illustration of Benford’s first digit law using alpha decay half lives’, European Journal of Physics, Vol. 14, pp. 59-63.
  5. Burke, J. and E. Kincanon (1991), ‘ Benford’s law and physical constants : the distribution of initial digits’, American Journal of Physics, Vol. 59, pp. 952.
  6. Dr. L. Kailasam (2011), ‘Benford Distribution - An Effective Audit Tool’ ,The Chartered Accountant, pp. 716-720
  7. Durtschi, Cindy, William Hillison, and Carl Pacini (2004), ‘The Effective Use of Benford's Law to Assist in Detective Fraud in Accounting Data’, UIC. Journal of Forensic Accounting, pp.17-34,Web http://www.uic.edu/ (Accessed,July 1,2013)
  8. Goutsmit, S.A. and W.H. Furry (1944). Significant figures of numbers in statistical tables. Vol. 154, pp. 800-801.
  9. Hoyle, D.C., Rattray, M., Jupp, R. and A. Brass (2002), ‘ Making sense of microarray data distributions’, Bioinformatics, Vol. 18(4), pp. 576-584.
  10. Knuth, D. (1969), The Art of Computer Programming, vol. 2, 219-229. (2nd ed.).Addison-Wesley, Reading, MA, 239-249. (3rd ed.) (1998), 254-262.
  11. Newcomb, Simon.(1881), ‘Note on the Frequency of Use of the Different Digits in Natural Numbers.’ American Journal of Mathematics, Vol.4.1, pp. 39-40, Web http://www.jstor.org/ (Accessed, July 1,2013)
  12. Miller, S.J. and M.J. Nigrini (2006), ‘ Differences between independent variables and almost Benford behaviour. Arxiv preprint math.PR/0601344 at www.arxiv.org.
  13. Nigrini, M.J., and S. J. Miller. (2007), ‘Benford's Law applied to hydrology data - results and relevance to other geophysical data’, Mathematical Geology, Vol. 39 (5),pp. 469-490.
  14. Nigrini, Mark J. Datas 2009 for Excel 2007 Program Details. Web. http://www.nigrini.com/datas_software.htm (Accessed ,,July 12,2013)
  15. Nigrini, Mark J. (1996), ‘Taxpayer compliance application of Benford's law’ , Journal of the American Taxation Association, Vol. 18(1), pp. 27-92
  16. Pietronero, L., Tosatti, E., Tosatti, V. and A. Vespignani (2001), ‘Explaining the uneven distribution of numbers in nature’, Physica A, Vol. 293,pp.297-304.
  17. Roukema, Bouewjin. (2009), ‘Benford’s Law Anomalies in the 2009 Iranian Presidential Election’, Annals of Applied StatisticsWeb.http://arxiv.org/PS_cache/arxiv/pdf/0906/0906.2789v1.pdf (Accessed, July 12,2013)
  18. Saville, AD.(2006), ‘Using Benford's Law to Detect Data Error and Fraud: An Examination of Companies Listed on the Johannesburg Stock Exchange’, SAJEMS, Vol. 9.3,pp. 341-54. Web.http://www.scribd.com/doc/47789223/Saville-Using-282006-29
  19. Sinn, Hans-Werner (2002), ‘Weber's Law and the Biological Evolution of Risk Preferences’, http://www.huebnergeneva.org/documents/Sinn.pdf (Accessed,July 10,2013)
  20. Skousen, Christopher J., Guan, Liming and Wetzel, T. Sterling (2004), ‘Anomalies and unusual patterns in reported eamings: Japanese managers round eamings’, Journal of International FinancialManagement and Accounting, Vol. 15, Issue 3, pp. 212-234
  21. Tam Cho, Wendy K. and Gaines, Brian J. (2007), ‘Breaking the (Benford's) Law: Statistical Fraud Detection in Campaign Finance’ American Statistician, Vol. 61, Issue 3, August, p. 218-223
  22. Thomas, Jacob K. (1989), ‘Unusual Patterns in Reported Earnings’, The Accounting Review, Vol. LXIV.4 pp.773-87.
  23. Varian, H.R. (1972), ‘Benford's Law’, American Statistician, Vol. 26, pp. 65-66
  24. Varma, T.N. and Khan , D.A. (2012) , ‘Fraud Detection in Supply Chain using Benford Distribution.’, International Journal of Research in Management, Vol. 5 No. 2, pp.90-96
  25. Weiss, Neil A. (1999), Introductory Statistics, Fifth Edition, Addison Wesley, Reading, MA, pp.A-16
  26. Wikipedia (2012), Weber-Fechner law, http://en.wikipedia.org/wiki/Weber-Fechner_Law (Accessed, July 12,2013)
  27. http://office.microsoft.com/en-us/excel/ (Accessed, May 8 ,2013)
Index Terms

Computer Science
Information Sciences

Keywords

Benford Distribution Excel sheet Fraud Supply Chain Management

Powered by PhDFocusTM