Determining Solvency and Insolvency of Commercial Banks in Nigeria

Yahaya Haruna U.; Abdulkarim Muhammad

doi:doi:10.11648/j.ajtas.20170604.18

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Determining Solvency and Insolvency of Commercial Banks in Nigeria

Yahaya Haruna U., Abdulkarim Muhammad

Received: 12 April 2017 Accepted: 26 April 2017 Published: 26 July 2017

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Abstract

This paper presents the application of artificial intelligence technique to develop aMulti-Layer Perceptron neural network model for determining the status (solvent or insolvent) of commercial banks in Nigeria. The common traditional classification techniques based on statistical parametric methods are constraint to fulfill certain assumptions. When those assumptions fail, the techniques do not often give sufficient descriptive accuracy in classifying the status of the banks. However, a class of feed-forward architecture of neural network known as Multi-Layer Perceptron (MLP) is not constraint by those parametric assumptions and offers good classification technique that competes well with the traditional statistical parametric techniques. In this study, data were sourced from the central bank of Nigeria and financial reports of the commercial banks in Nigeria. The banks specific variable of age, history of merger, time, total assets and total revenue are used as the input variables to the neural network. The solvency or insolvency as status are the two possible outputs of the neural network for each commercial bank in the period of 1994-2015. The developed MLP neural network model has 5 input neurons, 3 hidden neurons and 1 output neuron. Sigmoid activation function for the hidden neurons and “purelin” transfer function for the output neurons were utilized in training the MLP neural network. The results demonstrate that MLP neural networks are a viable technique for status classification of commercial banks in Nigeria.

DOI	10.11648/j.ajtas.20170604.18
Published in	American Journal of Theoretical and Applied Statistics (Volume 6, Issue 4, July 2017)
Page(s)	214-220
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

Artificial Intelligence, Multi-Layer Perceptron, Neural Network, Solvent, Insolvent, Transfer Function

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Yahaya Haruna U., Abdulkarim Muhammad. (2017). Determining Solvency and Insolvency of Commercial Banks in Nigeria. American Journal of Theoretical and Applied Statistics, 6(4), 214-220. https://doi.org/10.11648/j.ajtas.20170604.18

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ACS Style

Yahaya Haruna U.; Abdulkarim Muhammad. Determining Solvency and Insolvency of Commercial Banks in Nigeria. Am. J. Theor. Appl. Stat. 2017, 6(4), 214-220. doi: 10.11648/j.ajtas.20170604.18

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AMA Style

Yahaya Haruna U., Abdulkarim Muhammad. Determining Solvency and Insolvency of Commercial Banks in Nigeria. Am J Theor Appl Stat. 2017;6(4):214-220. doi: 10.11648/j.ajtas.20170604.18

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@article{10.11648/j.ajtas.20170604.18,
  author = {Yahaya Haruna U. and Abdulkarim Muhammad},
  title = {Determining Solvency and Insolvency of Commercial Banks in Nigeria},
  journal = {American Journal of Theoretical and Applied Statistics},
  volume = {6},
  number = {4},
  pages = {214-220},
  doi = {10.11648/j.ajtas.20170604.18},
  url = {https://doi.org/10.11648/j.ajtas.20170604.18},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20170604.18},
  abstract = {This paper presents the application of artificial intelligence technique to develop aMulti-Layer Perceptron neural network model for determining the status (solvent or insolvent) of commercial banks in Nigeria. The common traditional classification techniques based on statistical parametric methods are constraint to fulfill certain assumptions. When those assumptions fail, the techniques do not often give sufficient descriptive accuracy in classifying the status of the banks. However, a class of feed-forward architecture of neural network known as Multi-Layer Perceptron (MLP) is not constraint by those parametric assumptions and offers good classification technique that competes well with the traditional statistical parametric techniques. In this study, data were sourced from the central bank of Nigeria and financial reports of the commercial banks in Nigeria. The banks specific variable of age, history of merger, time, total assets and total revenue are used as the input variables to the neural network. The solvency or insolvency as status are the two possible outputs of the neural network for each commercial bank in the period of 1994-2015. The developed MLP neural network model has 5 input neurons, 3 hidden neurons and 1 output neuron. Sigmoid activation function for the hidden neurons and “purelin” transfer function for the output neurons were utilized in training the MLP neural network. The results demonstrate that MLP neural networks are a viable technique for status classification of commercial banks in Nigeria.},
 year = {2017}
}

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TY  - JOUR
T1  - Determining Solvency and Insolvency of Commercial Banks in Nigeria
AU  - Yahaya Haruna U.
AU  - Abdulkarim Muhammad
Y1  - 2017/07/26
PY  - 2017
N1  - https://doi.org/10.11648/j.ajtas.20170604.18
DO  - 10.11648/j.ajtas.20170604.18
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 214
EP  - 220
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20170604.18
AB  - This paper presents the application of artificial intelligence technique to develop aMulti-Layer Perceptron neural network model for determining the status (solvent or insolvent) of commercial banks in Nigeria. The common traditional classification techniques based on statistical parametric methods are constraint to fulfill certain assumptions. When those assumptions fail, the techniques do not often give sufficient descriptive accuracy in classifying the status of the banks. However, a class of feed-forward architecture of neural network known as Multi-Layer Perceptron (MLP) is not constraint by those parametric assumptions and offers good classification technique that competes well with the traditional statistical parametric techniques. In this study, data were sourced from the central bank of Nigeria and financial reports of the commercial banks in Nigeria. The banks specific variable of age, history of merger, time, total assets and total revenue are used as the input variables to the neural network. The solvency or insolvency as status are the two possible outputs of the neural network for each commercial bank in the period of 1994-2015. The developed MLP neural network model has 5 input neurons, 3 hidden neurons and 1 output neuron. Sigmoid activation function for the hidden neurons and “purelin” transfer function for the output neurons were utilized in training the MLP neural network. The results demonstrate that MLP neural networks are a viable technique for status classification of commercial banks in Nigeria.
VL  - 6
IS  - 4
ER  -

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Author Information

Yahaya Haruna U.

Department of Statistics, University of Abuja, Abuja, Nigeria
Abdulkarim Muhammad

Department of Statistics, University of Abuja, Abuja, Nigeria

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APA Style

Yahaya Haruna U., Abdulkarim Muhammad. (2017). Determining Solvency and Insolvency of Commercial Banks in Nigeria. American Journal of Theoretical and Applied Statistics, 6(4), 214-220. https://doi.org/10.11648/j.ajtas.20170604.18

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ACS Style

Yahaya Haruna U.; Abdulkarim Muhammad. Determining Solvency and Insolvency of Commercial Banks in Nigeria. Am. J. Theor. Appl. Stat. 2017, 6(4), 214-220. doi: 10.11648/j.ajtas.20170604.18

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AMA Style

Yahaya Haruna U., Abdulkarim Muhammad. Determining Solvency and Insolvency of Commercial Banks in Nigeria. Am J Theor Appl Stat. 2017;6(4):214-220. doi: 10.11648/j.ajtas.20170604.18

Copy | Download

@article{10.11648/j.ajtas.20170604.18,
  author = {Yahaya Haruna U. and Abdulkarim Muhammad},
  title = {Determining Solvency and Insolvency of Commercial Banks in Nigeria},
  journal = {American Journal of Theoretical and Applied Statistics},
  volume = {6},
  number = {4},
  pages = {214-220},
  doi = {10.11648/j.ajtas.20170604.18},
  url = {https://doi.org/10.11648/j.ajtas.20170604.18},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20170604.18},
  abstract = {This paper presents the application of artificial intelligence technique to develop aMulti-Layer Perceptron neural network model for determining the status (solvent or insolvent) of commercial banks in Nigeria. The common traditional classification techniques based on statistical parametric methods are constraint to fulfill certain assumptions. When those assumptions fail, the techniques do not often give sufficient descriptive accuracy in classifying the status of the banks. However, a class of feed-forward architecture of neural network known as Multi-Layer Perceptron (MLP) is not constraint by those parametric assumptions and offers good classification technique that competes well with the traditional statistical parametric techniques. In this study, data were sourced from the central bank of Nigeria and financial reports of the commercial banks in Nigeria. The banks specific variable of age, history of merger, time, total assets and total revenue are used as the input variables to the neural network. The solvency or insolvency as status are the two possible outputs of the neural network for each commercial bank in the period of 1994-2015. The developed MLP neural network model has 5 input neurons, 3 hidden neurons and 1 output neuron. Sigmoid activation function for the hidden neurons and “purelin” transfer function for the output neurons were utilized in training the MLP neural network. The results demonstrate that MLP neural networks are a viable technique for status classification of commercial banks in Nigeria.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - Determining Solvency and Insolvency of Commercial Banks in Nigeria
AU  - Yahaya Haruna U.
AU  - Abdulkarim Muhammad
Y1  - 2017/07/26
PY  - 2017
N1  - https://doi.org/10.11648/j.ajtas.20170604.18
DO  - 10.11648/j.ajtas.20170604.18
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 214
EP  - 220
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20170604.18
AB  - This paper presents the application of artificial intelligence technique to develop aMulti-Layer Perceptron neural network model for determining the status (solvent or insolvent) of commercial banks in Nigeria. The common traditional classification techniques based on statistical parametric methods are constraint to fulfill certain assumptions. When those assumptions fail, the techniques do not often give sufficient descriptive accuracy in classifying the status of the banks. However, a class of feed-forward architecture of neural network known as Multi-Layer Perceptron (MLP) is not constraint by those parametric assumptions and offers good classification technique that competes well with the traditional statistical parametric techniques. In this study, data were sourced from the central bank of Nigeria and financial reports of the commercial banks in Nigeria. The banks specific variable of age, history of merger, time, total assets and total revenue are used as the input variables to the neural network. The solvency or insolvency as status are the two possible outputs of the neural network for each commercial bank in the period of 1994-2015. The developed MLP neural network model has 5 input neurons, 3 hidden neurons and 1 output neuron. Sigmoid activation function for the hidden neurons and “purelin” transfer function for the output neurons were utilized in training the MLP neural network. The results demonstrate that MLP neural networks are a viable technique for status classification of commercial banks in Nigeria.
VL  - 6
IS  - 4
ER  -

Copy | Download