Assessment of Mortgage Applications Using Fuzzy Logic
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.
[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
[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
@article{"International Journal of Business, Human and Social Sciences:68214", author = "Swathi Sampath and V. Kalaichelvi", title = "Assessment of Mortgage Applications Using Fuzzy Logic", 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.", volume = "8", number = "11", pages = "3502-5", }
