Published October 2, 2014 | Version 9999664

Assessment of Mortgage Applications Using Fuzzy Logic

Description

The assessment of the risk posed by a borrower to a
lender is one of the common problems that financial institutions have
to deal with. Consumers vying for a mortgage are generally
compared to each other by the use of a number called the Credit
Score, which is generated by applying a mathematical algorithm to
information in the applicant’s credit report. The higher the credit
score, the lower the risk posed by the candidate, and the better he is
to be taken on by the lender. The objective of the present work is to
use fuzzy logic and linguistic rules to create a model that generates
Credit Scores.

Files

9999664.pdf

Files (330.3 kB)

Name Size Download all
md5:c04a26829542331bed62a55a6e989b53
330.3 kB Preview Download

Additional details

References

  • C. Zopounidis, P. Pardalos and G. Baourak is, "Fuzzy sets in management, economics, and marketing", 1st ed. River Edge, N.J.: World Scientific, 2001.
  • L. Dymowa, "Soft computing in economics and finance", 1st ed. Berlin: Springer, 2011.
  • J. de Andres Sanchez, "A Triangular Approximation for Fuzzy Discounted Cash Flows Based on Financial Indicators", Journal of computer and information technology, vol 1, iss 1, 2011.
  • J. Buckley, E. Eslami and T. Feuring, "Fuzzy Mathematics in Economics and Engineering", 1st ed. Heidelberg: Physica-Verlag HD, 2002
  • T. Korol, "Fuzzy Logic in Financial Management", Fuzzy Logic – Emerging Technologies and Applications, 2012
  • Z. Bro\vz, "Fuzzy logic as a tool for solving economical problems", 2009.
  • M. Calder, "A Framework For Conducting Risk Assessment", Childcare In Practice, vol 8, iss 1, pp. 7-18, 2002.
  • K. Shang and Z. Hossen, "Applying Fuzzy Logic to Risk Assessment and Decision-Making", Casualty Actuarial Society, Canadian Institute of Actuaries, Society of Actuaries, 2013.
  • R. Ouache and A. A. J. Adham, "Quantitative Risk Assessment in Engineering System using Fuzzy Bow-tie", International Journal of Current Engineering and Technology, vol 4, iss 2, 2014. [10] H. Ishibuchi, T. Nakashima and M. Nii, "Classification and modeling with linguistic information granules", 1st ed. New York: Springer, 2005. [11] M. Matsatsinis, K. Kosmidou, M. Doumpos and C. Zopounidis, "A fuzzy decision aiding method for the assessment of corporate bankruptcy", Fuzzy economic review, vol 8, iss 1, pp. 13--23, 2003. [12] E. Su and S. Li, "A Financial Distress Prewarning Study by Fuzzy Regression Model of TSE-Listed Companies", Asian Academy of Management Journal of Accounting and Finance, vol 2, iss 2, 2006. [13] A. Lahsasna, "Evaluation of Credit Risk Using Evolutionary-Fuzzy Logic Scheme", Masters, Faculty of Computer Science and Information Technology, University of Malaya, 2009. [14] J. Jang, C. Sun and E. Mizutani, "Neuro-fuzzy and soft computing", 1st ed. Upper Saddle River, NJ: Prentice Hall, 1997 [15] S. Naaz, A. Alam and R. Biswas, "Effect of different defuzzification methods in a fuzzy based load balancing application", International Journal of Computer Science, vol 8, iss 5, 2011