Credit Risk Assessment Utilizing Data Reduction Technique for Individual Loaning in Financial Institutes (Case Study: Tejarat Bank, Rasht, Iran)

Abbas Mahmoudabadi; Matin Mehrshad; Mohammad Reza Aminnaseri

doi:doi:10.11648/j.ajtab.20170301.12

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Credit Risk Assessment Utilizing Data Reduction Technique for Individual Loaning in Financial Institutes (Case Study: Tejarat Bank, Rasht, Iran)

Abbas Mahmoudabadi, Matin Mehrshad, Mohammad Reza Aminnaseri

Published in American Journal of Theoretical and Applied Business (Volume 3, Issue 1)

Received: 16 March 2017 Accepted: 25 April 2017 Published: 30 May 2017

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Abstract

Because of the nature of the financial and economic activities and they are practically accompanied with a degree of risk., banks are usually dealing with many risks, including operational, marketing, interest rate, etc. Since, credit risk has significant effects on financial banks activities in terms of loaning profits, the risk of repayment individual loans has been investigated in this research work. Two well-known regression models of Probit and Logistic have been developed based on nine extracted factors which have been investigated during the offering of loans according to the possibility of late or non-repayment. In order to minimize inter-correlation and extracting high-independency factors, the statistical technique of Principal Component Analysis (PCA), categorized as a data reduction technique, has been utilized and three factors out of nine have been omitted. One of Tejarat bank branches in the Iranian Northern Province of Guilan has been selected as case study to gather experimental data for assessing the credit risk of individual bank investors. The results of model validation revealed that the implementation of PCA method can improve the accuracy of models’ outputs and Probit regression model has better results rather than Logit one.

Published in	American Journal of Theoretical and Applied Business (Volume 3, Issue 1)
DOI	10.11648/j.ajtab.20170301.12
Page(s)	11-17
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), 2024. Published by Science Publishing Group

Keywords

Non-repayment Loaning, Credit Risk Evaluation, Individual Investors, Principal Component Analysis, Probit and Logit Regression Models

References

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[7]	Baon, Y., Ling, X., Hai, J., and Jing, X., An early warning system for loan risk assessment using artificial neural networks, knowledge-Based Systems, (14), 303-306, 2001.
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Abbas Mahmoudabadi, Matin Mehrshad, Mohammad Reza Aminnaseri. (2017). Credit Risk Assessment Utilizing Data Reduction Technique for Individual Loaning in Financial Institutes (Case Study: Tejarat Bank, Rasht, Iran). American Journal of Theoretical and Applied Business, 3(1), 11-17. https://doi.org/10.11648/j.ajtab.20170301.12

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ACS Style

Abbas Mahmoudabadi; Matin Mehrshad; Mohammad Reza Aminnaseri. Credit Risk Assessment Utilizing Data Reduction Technique for Individual Loaning in Financial Institutes (Case Study: Tejarat Bank, Rasht, Iran). Am. J. Theor. Appl. Bus. 2017, 3(1), 11-17. doi: 10.11648/j.ajtab.20170301.12

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AMA Style

Abbas Mahmoudabadi, Matin Mehrshad, Mohammad Reza Aminnaseri. Credit Risk Assessment Utilizing Data Reduction Technique for Individual Loaning in Financial Institutes (Case Study: Tejarat Bank, Rasht, Iran). Am J Theor Appl Bus. 2017;3(1):11-17. doi: 10.11648/j.ajtab.20170301.12

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@article{10.11648/j.ajtab.20170301.12,
  author = {Abbas Mahmoudabadi and Matin Mehrshad and Mohammad Reza Aminnaseri},
  title = {Credit Risk Assessment Utilizing Data Reduction Technique for Individual Loaning in Financial Institutes (Case Study: Tejarat Bank, Rasht, Iran)},
  journal = {American Journal of Theoretical and Applied Business},
  volume = {3},
  number = {1},
  pages = {11-17},
  doi = {10.11648/j.ajtab.20170301.12},
  url = {https://doi.org/10.11648/j.ajtab.20170301.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtab.20170301.12},
  abstract = {Because of the nature of the financial and economic activities and they are practically accompanied with a degree of risk., banks are usually dealing with many risks, including operational, marketing, interest rate, etc. Since, credit risk has significant effects on financial banks activities in terms of loaning profits, the risk of repayment individual loans has been investigated in this research work. Two well-known regression models of Probit and Logistic have been developed based on nine extracted factors which have been investigated during the offering of loans according to the possibility of late or non-repayment. In order to minimize inter-correlation and extracting high-independency factors, the statistical technique of Principal Component Analysis (PCA), categorized as a data reduction technique, has been utilized and three factors out of nine have been omitted. One of Tejarat bank branches in the Iranian Northern Province of Guilan has been selected as case study to gather experimental data for assessing the credit risk of individual bank investors. The results of model validation revealed that the implementation of PCA method can improve the accuracy of models’ outputs and Probit regression model has better results rather than Logit one.},
 year = {2017}
}

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TY  - JOUR
T1  - Credit Risk Assessment Utilizing Data Reduction Technique for Individual Loaning in Financial Institutes (Case Study: Tejarat Bank, Rasht, Iran)
AU  - Abbas Mahmoudabadi
AU  - Matin Mehrshad
AU  - Mohammad Reza Aminnaseri
Y1  - 2017/05/30
PY  - 2017
N1  - https://doi.org/10.11648/j.ajtab.20170301.12
DO  - 10.11648/j.ajtab.20170301.12
T2  - American Journal of Theoretical and Applied Business
JF  - American Journal of Theoretical and Applied Business
JO  - American Journal of Theoretical and Applied Business
SP  - 11
EP  - 17
PB  - Science Publishing Group
SN  - 2469-7842
UR  - https://doi.org/10.11648/j.ajtab.20170301.12
AB  - Because of the nature of the financial and economic activities and they are practically accompanied with a degree of risk., banks are usually dealing with many risks, including operational, marketing, interest rate, etc. Since, credit risk has significant effects on financial banks activities in terms of loaning profits, the risk of repayment individual loans has been investigated in this research work. Two well-known regression models of Probit and Logistic have been developed based on nine extracted factors which have been investigated during the offering of loans according to the possibility of late or non-repayment. In order to minimize inter-correlation and extracting high-independency factors, the statistical technique of Principal Component Analysis (PCA), categorized as a data reduction technique, has been utilized and three factors out of nine have been omitted. One of Tejarat bank branches in the Iranian Northern Province of Guilan has been selected as case study to gather experimental data for assessing the credit risk of individual bank investors. The results of model validation revealed that the implementation of PCA method can improve the accuracy of models’ outputs and Probit regression model has better results rather than Logit one.
VL  - 3
IS  - 1
ER  -

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Author Information

Abbas Mahmoudabadi

Department of Industrial Engineering, Mehr Astan University, Astaneh-Ashrafieh, Guilan, Iran
Matin Mehrshad

Department of Industrial Engineering, Mehr Astan University, Astaneh-Ashrafieh, Guilan, Iran
Mohammad Reza Aminnaseri

Department of Industrial Engineering, Tarbiat Modares University, Tehran, Iran

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APA Style

Abbas Mahmoudabadi, Matin Mehrshad, Mohammad Reza Aminnaseri. (2017). Credit Risk Assessment Utilizing Data Reduction Technique for Individual Loaning in Financial Institutes (Case Study: Tejarat Bank, Rasht, Iran). American Journal of Theoretical and Applied Business, 3(1), 11-17. https://doi.org/10.11648/j.ajtab.20170301.12

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ACS Style

Abbas Mahmoudabadi; Matin Mehrshad; Mohammad Reza Aminnaseri. Credit Risk Assessment Utilizing Data Reduction Technique for Individual Loaning in Financial Institutes (Case Study: Tejarat Bank, Rasht, Iran). Am. J. Theor. Appl. Bus. 2017, 3(1), 11-17. doi: 10.11648/j.ajtab.20170301.12

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AMA Style

Abbas Mahmoudabadi, Matin Mehrshad, Mohammad Reza Aminnaseri. Credit Risk Assessment Utilizing Data Reduction Technique for Individual Loaning in Financial Institutes (Case Study: Tejarat Bank, Rasht, Iran). Am J Theor Appl Bus. 2017;3(1):11-17. doi: 10.11648/j.ajtab.20170301.12

Copy | Download

@article{10.11648/j.ajtab.20170301.12,
  author = {Abbas Mahmoudabadi and Matin Mehrshad and Mohammad Reza Aminnaseri},
  title = {Credit Risk Assessment Utilizing Data Reduction Technique for Individual Loaning in Financial Institutes (Case Study: Tejarat Bank, Rasht, Iran)},
  journal = {American Journal of Theoretical and Applied Business},
  volume = {3},
  number = {1},
  pages = {11-17},
  doi = {10.11648/j.ajtab.20170301.12},
  url = {https://doi.org/10.11648/j.ajtab.20170301.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtab.20170301.12},
  abstract = {Because of the nature of the financial and economic activities and they are practically accompanied with a degree of risk., banks are usually dealing with many risks, including operational, marketing, interest rate, etc. Since, credit risk has significant effects on financial banks activities in terms of loaning profits, the risk of repayment individual loans has been investigated in this research work. Two well-known regression models of Probit and Logistic have been developed based on nine extracted factors which have been investigated during the offering of loans according to the possibility of late or non-repayment. In order to minimize inter-correlation and extracting high-independency factors, the statistical technique of Principal Component Analysis (PCA), categorized as a data reduction technique, has been utilized and three factors out of nine have been omitted. One of Tejarat bank branches in the Iranian Northern Province of Guilan has been selected as case study to gather experimental data for assessing the credit risk of individual bank investors. The results of model validation revealed that the implementation of PCA method can improve the accuracy of models’ outputs and Probit regression model has better results rather than Logit one.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - Credit Risk Assessment Utilizing Data Reduction Technique for Individual Loaning in Financial Institutes (Case Study: Tejarat Bank, Rasht, Iran)
AU  - Abbas Mahmoudabadi
AU  - Matin Mehrshad
AU  - Mohammad Reza Aminnaseri
Y1  - 2017/05/30
PY  - 2017
N1  - https://doi.org/10.11648/j.ajtab.20170301.12
DO  - 10.11648/j.ajtab.20170301.12
T2  - American Journal of Theoretical and Applied Business
JF  - American Journal of Theoretical and Applied Business
JO  - American Journal of Theoretical and Applied Business
SP  - 11
EP  - 17
PB  - Science Publishing Group
SN  - 2469-7842
UR  - https://doi.org/10.11648/j.ajtab.20170301.12
AB  - Because of the nature of the financial and economic activities and they are practically accompanied with a degree of risk., banks are usually dealing with many risks, including operational, marketing, interest rate, etc. Since, credit risk has significant effects on financial banks activities in terms of loaning profits, the risk of repayment individual loans has been investigated in this research work. Two well-known regression models of Probit and Logistic have been developed based on nine extracted factors which have been investigated during the offering of loans according to the possibility of late or non-repayment. In order to minimize inter-correlation and extracting high-independency factors, the statistical technique of Principal Component Analysis (PCA), categorized as a data reduction technique, has been utilized and three factors out of nine have been omitted. One of Tejarat bank branches in the Iranian Northern Province of Guilan has been selected as case study to gather experimental data for assessing the credit risk of individual bank investors. The results of model validation revealed that the implementation of PCA method can improve the accuracy of models’ outputs and Probit regression model has better results rather than Logit one.
VL  - 3
IS  - 1
ER  -

Copy | Download