Adaptive Neuro-Fuzzy Inference System for Mortgage Loan Risk Assessment

Mojisola Grace Asogbon; Olatubosun Olabode; Oluwatoyin Catherine Agbonifo; Oluwarotimi Williams Samuel; Victoria Ifeoluwa Yemi-Peters

doi:doi:10.11648/j.ijiis.20160501.13

Adaptive Neuro-Fuzzy Inference System for Mortgage Loan Risk Assessment

Mojisola Grace Asogbon, Olatubosun Olabode, Oluwatoyin Catherine Agbonifo, Oluwarotimi Williams Samuel, Victoria Ifeoluwa Yemi-Peters

Published in International Journal of Intelligent Information Systems (Volume 5, Issue 1)

Received: 23 January 2016 Accepted: 1 February 2016 Published: 19 February 2016

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Abstract

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.

Published in	International Journal of Intelligent Information Systems (Volume 5, Issue 1)
DOI	10.11648/j.ijiis.20160501.13
Page(s)	17-24
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2016. Published by Science Publishing Group

Keywords

Mortgage Loan, Mortgage Institution, Risk Assessment, Neural Network, Fuzzy Logic

References

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[8]	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.
[9]	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.
[10]	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.
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APA Style

Mojisola Grace Asogbon, Olatubosun Olabode, Oluwatoyin Catherine Agbonifo, Oluwarotimi Williams Samuel, Victoria Ifeoluwa Yemi-Peters. (2016). Adaptive Neuro-Fuzzy Inference System for Mortgage Loan Risk Assessment. International Journal of Intelligent Information Systems, 5(1), 17-24. https://doi.org/10.11648/j.ijiis.20160501.13

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ACS Style

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. Int. J. Intell. Inf. Syst. 2016, 5(1), 17-24. doi: 10.11648/j.ijiis.20160501.13

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AMA Style

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. Int J Intell Inf Syst. 2016;5(1):17-24. doi: 10.11648/j.ijiis.20160501.13

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@article{10.11648/j.ijiis.20160501.13,
  author = {Mojisola Grace Asogbon and Olatubosun Olabode and Oluwatoyin Catherine Agbonifo and Oluwarotimi Williams Samuel and Victoria Ifeoluwa Yemi-Peters},
  title = {Adaptive Neuro-Fuzzy Inference System for Mortgage Loan Risk Assessment},
  journal = {International Journal of Intelligent Information Systems},
  volume = {5},
  number = {1},
  pages = {17-24},
  doi = {10.11648/j.ijiis.20160501.13},
  url = {https://doi.org/10.11648/j.ijiis.20160501.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20160501.13},
  abstract = {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.},
 year = {2016}
}

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TY  - JOUR
T1  - Adaptive Neuro-Fuzzy Inference System for Mortgage Loan Risk Assessment
AU  - Mojisola Grace Asogbon
AU  - Olatubosun Olabode
AU  - Oluwatoyin Catherine Agbonifo
AU  - Oluwarotimi Williams Samuel
AU  - Victoria Ifeoluwa Yemi-Peters
Y1  - 2016/02/19
PY  - 2016
N1  - https://doi.org/10.11648/j.ijiis.20160501.13
DO  - 10.11648/j.ijiis.20160501.13
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 17
EP  - 24
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.20160501.13
AB  - 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.
VL  - 5
IS  - 1
ER  -

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Author Information

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
Victoria Ifeoluwa Yemi-Peters

Mathematical Sciences Department, Kogi State University, Anyigba, Nigeria

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APA Style

Mojisola Grace Asogbon, Olatubosun Olabode, Oluwatoyin Catherine Agbonifo, Oluwarotimi Williams Samuel, Victoria Ifeoluwa Yemi-Peters. (2016). Adaptive Neuro-Fuzzy Inference System for Mortgage Loan Risk Assessment. International Journal of Intelligent Information Systems, 5(1), 17-24. https://doi.org/10.11648/j.ijiis.20160501.13

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ACS Style

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. Int. J. Intell. Inf. Syst. 2016, 5(1), 17-24. doi: 10.11648/j.ijiis.20160501.13

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AMA Style

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. Int J Intell Inf Syst. 2016;5(1):17-24. doi: 10.11648/j.ijiis.20160501.13

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@article{10.11648/j.ijiis.20160501.13,
  author = {Mojisola Grace Asogbon and Olatubosun Olabode and Oluwatoyin Catherine Agbonifo and Oluwarotimi Williams Samuel and Victoria Ifeoluwa Yemi-Peters},
  title = {Adaptive Neuro-Fuzzy Inference System for Mortgage Loan Risk Assessment},
  journal = {International Journal of Intelligent Information Systems},
  volume = {5},
  number = {1},
  pages = {17-24},
  doi = {10.11648/j.ijiis.20160501.13},
  url = {https://doi.org/10.11648/j.ijiis.20160501.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20160501.13},
  abstract = {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.},
 year = {2016}
}

Copy | Download

TY  - JOUR
T1  - Adaptive Neuro-Fuzzy Inference System for Mortgage Loan Risk Assessment
AU  - Mojisola Grace Asogbon
AU  - Olatubosun Olabode
AU  - Oluwatoyin Catherine Agbonifo
AU  - Oluwarotimi Williams Samuel
AU  - Victoria Ifeoluwa Yemi-Peters
Y1  - 2016/02/19
PY  - 2016
N1  - https://doi.org/10.11648/j.ijiis.20160501.13
DO  - 10.11648/j.ijiis.20160501.13
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 17
EP  - 24
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.20160501.13
AB  - 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.
VL  - 5
IS  - 1
ER  -

Copy | Download