Volume 5, Issue 1, February 2016, Page: 17-24
Adaptive Neuro-Fuzzy Inference System for Mortgage Loan Risk Assessment
Mojisola Grace Asogbon, Department of Computer Science, Federal University of Technology Akure, Nigeria
Olatubosun Olabode, Department of Computer Science, Federal University of Technology Akure, Nigeria
Oluwatoyin Catherine Agbonifo, Department of Computer Science, Federal University of Technology Akure, Nigeria
Oluwarotimi Williams Samuel, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China; University of Chinese Academy of Sciences, Department of Computing, Beijing, China
Victoria Ifeoluwa Yemi-Peters, Mathematical Sciences Department, Kogi State University, Anyigba, Nigeria
Received: Jan. 23, 2016;       Accepted: Feb. 1, 2016;       Published: Feb. 19, 2016
DOI: 10.11648/j.ijiis.20160501.13      View  4557      Downloads  141
Mortgage lending is one of the major businesses of mortgage institutions which usually involve the granting of loan to potential customers who want to own a home but do not have sufficient capital to do so. The granting of mortgage loan to customers usually comes with a lot of risks which may eventually affect the continuity of such institution if not properly managed. In recent times, several techniques for mortgage loan risk assessment have been proposed. However, a technique that can learn and adapt at the same time incorporate current knowledge of mortgage loan practices is still lacking. Therefore, this research proposed a hybrid decision support system in which neural networks was used to build learning and adaptive capabilities into a fuzzy inference module for mortgage loan risk assessment. The performance of the proposed hybrid system was investigated based on the accuracy of loan risk prediction and the mean absolute deviation metrics. Experimental results show that the hybrid system has better performance than the non-adaptive fuzzy inference system. Our findings suggest that the proposed method would efficiently predict the risk associated with mortgage loan applicants and thereby promote mortgage lending in such institutions.
Mortgage Loan, Mortgage Institution, Risk Assessment, Neural Network, Fuzzy Logic
To cite this article
Mojisola Grace Asogbon, Olatubosun Olabode, Oluwatoyin Catherine Agbonifo, Oluwarotimi Williams Samuel, Victoria Ifeoluwa Yemi-Peters, Adaptive Neuro-Fuzzy Inference System for Mortgage Loan Risk Assessment, International Journal of Intelligent Information Systems. Vol. 5, No. 1, 2016, pp. 17-24. doi: 10.11648/j.ijiis.20160501.13
Copyright © 2016 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
A. A. Hussein, and P. John, Credit scoring, statistical techniques and evaluation criteria: A review of the literature, Intelligent Systems in Accounting, Finance & Management, vol. 18, no. (2-3), pp. 59-88, 2011.
Z. Kirori, and J. Ogutu, An application of the logit boost ensemble algorithm in loan appraisals, International Journal of Intelligent Information Systems, vol. 2, no. 2, pp. 34-39, 2013.
Y. Shachmurove, Applying artificial neural networks to business, economics and finance, International Journal of Business, vol. 6, no. 1, pp.1-22, 2002.
F. E. Shorouq, G. Y. Saad, and A. E. Ghaleb, Neuro-Based artificial intelligence model for loan decisions, American Journal of Economics and Business Administration, vol.2, no.1, pp. 27-34, 2010.
M. Khashei, M. Bijari, and G. A. Raissi, Improvement of auto-regressive integrated moving average models using fuzzy logic and artificial neural networks (ANNs), Neurocomputing vol.72, pp. 956–967, 2009.
Y. Atinc, and A. Kursat, Comparison with sugeno model and measurement of cancer risk analysis by new fuzzy logic approach, African Journal of Biotechnology, vol. 11, no. 4, pp. 979-991, 2012.
H. Matoussi, and A. Abdelmoula, Using a neural network-based methodology for credit risk evaluation of a Tunisian bank, Middle Eastern Finance and Economics, Issue 4, pp. 117-140, 2009.
J. R. Jang, Fuzzy Modeling using generalized neural networks and kalman filter algorithm, Proceedings of the 9th National Conference on Artificial Intelligence, Anaheim, CA, USA, vol. 2, pp. 762–767, July 14–19, 1991.
J. R. Jang, ANFIS: Adaptive network based fuzzy inference system, IEEE Transactions on Systems, Man and Cybernetics, vol. 23, no. 3, 1993. Doi:10.1109/21.256541.
S. Rajeev, and C. Pankaj, Credit risk assessment for mortgage lending, International Journal of Research in Business Management, vol. 3, no. 4, pp. 13-18, 2015.
A. F. Finando, P. Sergio, and M. C. Victor, An AHP based approach for credit risk evaluation of mortgage loans, International Journal of Strategic Property Management., vol. 18, no.1, pp. 38-55, 2014.
K. A. Aida, Bank credit risk analysis with k-nearest neighbor classifier: Case of Tunisian banks, Accounting and Management Information Systems, vol. 14, No. 1, pp. 79-106, 2015.
A. R. Ghatge, and P. P. Halkarnikar, Ensemble neural network strategy for predicting credit default evaluation, International Journal of Engineering and Innovative Technology (IJEIT), vol. 2, no. 7, pp. 223-225, 2013.
F. A. Umar, K. P. Joseph, and B. H. James, Fuzzy logic approach to credit scoring for micro finances in Ghana (A case study of kwiqplus money lending), International Journal of Computer Applications, vol. 94, no. 8, pp. 11-19, 2014.
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