Assessment of Mortgage Applications Using Fuzzy Logic
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32807
Assessment of Mortgage Applications Using Fuzzy Logic

Authors: Swathi Sampath, V. Kalaichelvi

Abstract:

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.

Keywords: Credit scoring, fuzzy logic, mortgage, risk assessment.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1096713

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2691

References:


[1] C. Zopounidis, P. Pardalos and G. Baourak is, “Fuzzy sets in management, economics, and marketing”, 1st ed. River Edge, N.J.: World Scientific, 2001.
[2] L. Dymowa, “Soft computing in economics and finance”, 1st ed. Berlin: Springer, 2011.
[3] 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.
[4] J. Buckley, E. Eslami and T. Feuring, “Fuzzy Mathematics in Economics and Engineering”, 1st ed. Heidelberg: Physica-Verlag HD, 2002
[5] T. Korol, “Fuzzy Logic in Financial Management”, Fuzzy Logic – Emerging Technologies and Applications, 2012
[6] Z. Bro\vz, “Fuzzy logic as a tool for solving economical problems”, 2009.
[7] M. Calder, “A Framework For Conducting Risk Assessment”, Childcare In Practice, vol 8, iss 1, pp. 7-18, 2002.
[8] 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.
[9] 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