Identity Theft Mitigation in Kenyan Financial Sectors (SACCOs): Handwritten Signature Verification

Mwangi Caroline Wambui, Abade Elisha

Abstract


The existence of identity theft in society has become a major concern due to the effects it causes to those that are affected by it, more especially in the financial sector. Thus this thesis establishes the existence of identity theft issues in the financial sector loan sections and proposes an algorithm that addresses the mitigation processes of identity theft by having the signatures on the loan forms verified using the implementation of the proposed algorithm, then the results are compared with the human experts verification that are done on a daily basis. From the qualitative data collected from the four SACCOs presented indicate the 93% of the respondents knew that forgery of one’s signature in the SACCO exists and from the 93%, 95% of them had been victims of identity theft and 50% of them knew it after deductions were been made from their accounts. The algorithm was implemented in a prototype that was used to test the signatures that were corrected from various individuals that belonged to various SACCOs. The prototype had successfully verified 80.1% of the test signatures and as expected the highest results from the four Human experts verification of forged signature was 8.3% indicating that they had indicated more signatures as originals. The prototype thus recorded an accuracy of 91.4% and a precision of 60.0%. 


Keywords


Algorithm; Identity Theft; Mitigation; Handwritten Signature; Signature Verification.

Full Text:

PDF

References


. Zimmerman Thomas G. , Russell Gregory F. , Heilper Andre , Smith Barton A. , Hu Jianying , Markman Dmitry , Graham Jon E. , Drews Clemen, “Retail Applications of Signature Verification, Almaden: IBM Research”, IBM Almaden Research Center, 2003.

. Koppenhaver, K. M., “History of Forgery, Forensic Document examination”, Humana Press, 2007.

. Karounia, A., Dayab, B. & Bahlakb, S., “Offline signature recognition using neural networks approach.” Elsevier Ltd, Procedia Computer Science, 2011, Volume 3, pp. 155-161.

. CIPPIC, “Identity Theft: Introduction and Background”, Ottawa: CIPPIC Working Paper , 2007, No. 1.

. CIPPIC, “Techniques of Identity Theft”, Ottawa: CIPPIC Working Paper, , 2007, No.2.

. Identity-Theft-Scenarios.com, “History of identity Theft”, 2015, Available online: http://www.identity-theft-scenarios.com/identity-theft-facts/history/

. Barske, D., Stander, A. & Jordaan, J., “A Digital Forensic Readiness framework for South African SME's”, Information Security for South Africa (ISSA), 2010 , (DOI) 10.1109(ISSA.2010.5588281).

. Graeme, N. R. & McNally, M. M., “Identity theft Literature Review” U.S. Department of Justice, Report 2005.

. Hoar, S. B., “Identity Theft: The Crime of the new Millennium”, HeinOnline journals, 2001, pp-1423(80).

. Ogla, A., “ID Theft: A Computer Forensics’ Investigation Framework. Australia”, 5th Australian Digital Forensics Conference Paper, 2007.

. Maxwell, M., “Police warn over card skimming syndicate, Nairobi” Star newspaper, 2012.

. Oppliger, Rolf and Gajek, Sebastian. “Effective Protection Against Phishing and Web Spoofing”, IFIP International Federation for Information Processing, 2005, pp. 32-41.

. Vishesh, T., “Phishing and Pharming- The Deadly Duo” SANS Institute, 2007.

. Post, A., “The Dangers of Spyware”, Symantec Security Response, 2003.

. Billig, Justin, Danilchenko, Yuri and Frank, Charles E., “Evaluation of Google Hacking” Kennesaw InfoSecCD Conference’08, 2008.

. Shao-Bo, J., Shawn, S.-C. & Quing-Fei, M., “Systems Plan for Combating Identity Theft- A Theoretical Framework”, Journal of Service Science and management, 2008, Volume 1, pp. 143-152.

. SecurityFocus, “Discarded computer hard drives prove a trove of personal info”, Security Focus, 2003. Available online: http://www.securityfocus.com/news/2055

. 22nd September Monday, DailyNation, 2014. Public Notice Adverts. Nairobi: Monday Daily Nation.

. Mugenda, O. M. & Mugenda, A. G., “Research Methods: Quantitative and Qualitative Approaches”, Second edition Acts Press, 1999.

. Bacchus, F., Computer Lecture notes, 2010, [Online]

Available at: http://www.cs.toronto.edu/~fbacchus/Presentations/CSP-BasicIntro.pdf

[Accessed 16 July 2015].

. Wagner, M. & Urli, T., 2013. Computer Lecture notes. [Online]

[Accessed 16 July 2015].

. Battagilia, M. P., “Nonprobability Smapling. In: Encyclopedia of survey Research methods”, SAGE Publications, 2008, pp. 523-526.

. Ross, S. M. & Morrison, G. R., “Experimental Research Methods”, Association Educational Communication and technology, 2001.

. Witten, I. H., Frank, E. & Hall, M. A., “Data Mining Practical machine Learning Tools and Techniques” 2011, Morgan Kaufmann Publishers, 3rd ed.

. Li, G., Zhiping, L., Bin, Z. & Yaoge, W., “The study for Matching Algorithms and Matching Tactics about area Vector Data Based on Spatial Directional Similarity” The Joint International Conference on Theory, Data Handling and Modelling in GeoSpatial Information Science, 2010, 38(II), p. 395.

. Vatsa M., S. R. M. P. N. A., “Signature Verification Using static and Dynamic Features” Berlin Heidelberg: Springer-Verlag, 2004, pp. 350-355.

. Thakkar, D., “Bayometric: False Acceptance Rate (FAR) and False Recognition Rate (FRR)”, 2016, Available online: https://www.bayometric.com/false-acceptance-rate-far-false-recognition-rate-frr/

. Patil, P. G. & Hegadi, R. S., “Classification of offline Handwritten Signatuires using Wavelets and a Pattern Recognition Neural Network” International journal of Computer Applications, Recent Advances in Information Technology, 2014, pp. 0975-8887.

. Anand, H. & D.L, B., “Enhanced signature verification and recognition using Matlab” International Journal of Innovative research in Advanced Engineering (IJIRAE), 2014, 1(4), pp. 2349-2163.

. Jarad, M., Al-Najdawi, N. & Tedmori, S., “Offline handwritten signature Verification System using a Supervised Neural Network approach”, IEEE Computer Society, 2014, pp. 189-195, 6(ISBN:987-1-4799-3999-2)

. Dash, T., Nayak, T. & Chattopadhyay, S., “Offline Handwritten Signature Verification using Associative Memory Net”, International Journal of Advanced Research in computer Engineering & Technology, 2012, vol. 1(4).

. Pourshahabi Muhammad R., Sigari Mohamad H. and Pourreza Hamid R., “Offline Handwritten signature identification and verification using contourlet transform”, IEEE Computer Society, 2009, Vol. 2, pp. 670-673.


Refbacks

  • There are currently no refbacks.


 

 
  

 

  


About IJC | Privacy PolicyTerms & Conditions | Contact Us | DisclaimerFAQs 

IJC is published by (GSSRR).