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Adaptive Neuro-Fuzzy Inference System for Mortgage Loan Risk Assessment
International Journal of Intelligent Information Systems
Volume 5, Issue 1, February 2016, Pages: 17-24
Received: Jan. 23, 2016; Accepted: Feb. 1, 2016; Published: Feb. 19, 2016
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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
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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
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