Knowing Ahead Mathematical Determinant of Bank Customers Credit Worthiness: A Safe Strategy for Funding Loan in a Critical Economy

Margaret Ose Asika

doi:doi:10.11648/j.mcs.20210601.13

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Knowing Ahead Mathematical Determinant of Bank Customers Credit Worthiness: A Safe Strategy for Funding Loan in a Critical Economy

Margaret Ose Asika

Published in Mathematics and Computer Science (Volume 6, Issue 1)

Received: 29 January 2020 Accepted: 11 March 2020 Published: 10 March 2021

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Abstract

The study was carried out to identify relevant attributes that signals the capacity of borrower to pay back the loan and determine the fit of mathematical scoring model to evaluate credit worthiness of a potential borrower. The data was taken from primary and secondary sources which was through the use of questionnaires (primary source) while the secondary source was collection of data from all the financial statements of selected business owners in Ekpoma, Edo State credits history of these business owners as well. The descriptive research and the explanatory research designs were employed in this study. Two research questions were raised while one hypothesis was formulated to guide the study. Thirty five (35) business owners were randomly selected from Ekpoma metropolis of Edo state for this study based on loan applications and business capacity. The data collected were analyzed using Altman Z-scores, frequencies and percentages while the Pearson Product Moment Correlation Co-efficient was used to determine the relationship between Mathematical Scoring model and credits worthiness. The result showed that credit scores developed from borrower financial and non-financial records and history such as turnover, assets, previous loan repayment rate and trading capital perfectly classified them into five risk classes of A (Worthy and very able to payback), B (worthy and less able to pay back) and D (not worthy at all). The result revealed that credit score can safe award banks and creditors against credit risk default and loss of money. It was therefore recommended among others, that banks and credit facilities handlers should adopt mathematical credit scoring techniques to avoid loss of their money.

Published in	Mathematics and Computer Science (Volume 6, Issue 1) This article belongs to the Special Issue One and Two Levels of Trade Credit Based on Discounted Cashflow and Inventory Inaccuracy and Other Modelling Related Topics
DOI	10.11648/j.mcs.20210601.13
Page(s)	16-23
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), 2021. Published by Science Publishing Group

Keywords

Credits Worthiness, Credit Scoring, Credits Risk, Bank Customers

References

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[2]	Basel Committee on Banking Supervision (2001). Risk Management Practices and Regulatory Capital: Cross-Sectional Comparison (available at www.bis.org).
[3]	Afriyie, H. O. and Akotey J. O. (2011) Credit Risk Management and Profitability of Selected Rural Banks In Ghana. Faculty of Economics and Business Administration Catholic University College of Ghana. Pp 1-18.
[4]	Sudhakar M1 and Reddy C. V. K. (2016) Two Step Credit Risk Assessment Model for Retail Bank Loan Applications using Decision Tree Data Mining Technique. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), 5 (3), pp 1-14.
[5]	Bakpo F. S. and Kabari, L. G. (2009) Credit Risk Evaluation System: An Artificial Neural Network Approach. Nigerian Journal of Technology, publishing research reports.
[6]	https://en.m.wikipedia.org.
[7]	Vodová, P. (2014) Credit Risk as a Cause of Banking Crises. Silesian University, School of Business Administration, Czech Republic pp 1-25.
[8]	https://www.google.com/m?q=slaessens+and+laeven+%29+banking+crisis&clients=ms-opera-mobile&chanel=new&espv=1.
[9]	IMF (1999). Annual Report of the Executives board for the financial year ended April, 30 1999.
[10]	Bharati M. R., (2019) “Data Mining Techniques and Applications”, Indian Journal of Computer Science and Engineering Vol. 1 No. 4. pp 3-8.
[11]	Murray, J. (2011). Default on loan united states Business law and Taxes Guide.
[12]	Bakare, I. A. O, Ajibola, A. I & Samuel, K. H. (2015). To what extent does Banks’ credit stimulate Economic Growth? Evidence from Nigeria. 13 (1).
[13]	Ofonyelu, C. C., & Alimi, R. S. (2013). Perceived bank risk and Expose Default outcome. Are the Banks’ loans screening criteria Efficient? Asian Economic and Financial Review, 3 (8), 991–1002.
[14]	Ntiamuah, B & Ofeng, A. (2014). Loan default rate and its impact on profitability in financial institution. Research journal of Finance and accounting, 5, 14, 67-72.
[15]	Asia, et al (2013). International Journal of Applied Information Systems (IJAIS) - ISSN: 2249-0868. Foundation of Computer Science. FCS, New Year, USA. www.ijais.org.
[16]	Altman, E. (1968). Financial Ratios, Discrimination Analysis and the Prediction of Corporate Bankruptcy. Journal of Finance, Vol. 23, No. pp. 589-609.

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Margaret Ose Asika. (2021). Knowing Ahead Mathematical Determinant of Bank Customers Credit Worthiness: A Safe Strategy for Funding Loan in a Critical Economy. Mathematics and Computer Science, 6(1), 16-23. https://doi.org/10.11648/j.mcs.20210601.13

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Margaret Ose Asika. Knowing Ahead Mathematical Determinant of Bank Customers Credit Worthiness: A Safe Strategy for Funding Loan in a Critical Economy. Math. Comput. Sci. 2021, 6(1), 16-23. doi: 10.11648/j.mcs.20210601.13

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AMA Style

Margaret Ose Asika. Knowing Ahead Mathematical Determinant of Bank Customers Credit Worthiness: A Safe Strategy for Funding Loan in a Critical Economy. Math Comput Sci. 2021;6(1):16-23. doi: 10.11648/j.mcs.20210601.13

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@article{10.11648/j.mcs.20210601.13,
  author = {Margaret Ose Asika},
  title = {Knowing Ahead Mathematical Determinant of Bank Customers Credit Worthiness: A Safe Strategy for Funding Loan in a Critical Economy},
  journal = {Mathematics and Computer Science},
  volume = {6},
  number = {1},
  pages = {16-23},
  doi = {10.11648/j.mcs.20210601.13},
  url = {https://doi.org/10.11648/j.mcs.20210601.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mcs.20210601.13},
  abstract = {The study was carried out to identify relevant attributes that signals the capacity of borrower to pay back the loan and determine the fit of mathematical scoring model to evaluate credit worthiness of a potential borrower. The data was taken from primary and secondary sources which was through the use of questionnaires (primary source) while the secondary source was collection of data from all the financial statements of selected business owners in Ekpoma, Edo State credits history of these business owners as well. The descriptive research and the explanatory research designs were employed in this study. Two research questions were raised while one hypothesis was formulated to guide the study. Thirty five (35) business owners were randomly selected from Ekpoma metropolis of Edo state for this study based on loan applications and business capacity. The data collected were analyzed using Altman Z-scores, frequencies and percentages while the Pearson Product Moment Correlation Co-efficient was used to determine the relationship between Mathematical Scoring model and credits worthiness. The result showed that credit scores developed from borrower financial and non-financial records and history such as turnover, assets, previous loan repayment rate and trading capital perfectly classified them into five risk classes of A (Worthy and very able to payback), B (worthy and less able to pay back) and D (not worthy at all). The result revealed that credit score can safe award banks and creditors against credit risk default and loss of money. It was therefore recommended among others, that banks and credit facilities handlers should adopt mathematical credit scoring techniques to avoid loss of their money.},
 year = {2021}
}

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TY  - JOUR
T1  - Knowing Ahead Mathematical Determinant of Bank Customers Credit Worthiness: A Safe Strategy for Funding Loan in a Critical Economy
AU  - Margaret Ose Asika
Y1  - 2021/03/10
PY  - 2021
N1  - https://doi.org/10.11648/j.mcs.20210601.13
DO  - 10.11648/j.mcs.20210601.13
T2  - Mathematics and Computer Science
JF  - Mathematics and Computer Science
JO  - Mathematics and Computer Science
SP  - 16
EP  - 23
PB  - Science Publishing Group
SN  - 2575-6028
UR  - https://doi.org/10.11648/j.mcs.20210601.13
AB  - The study was carried out to identify relevant attributes that signals the capacity of borrower to pay back the loan and determine the fit of mathematical scoring model to evaluate credit worthiness of a potential borrower. The data was taken from primary and secondary sources which was through the use of questionnaires (primary source) while the secondary source was collection of data from all the financial statements of selected business owners in Ekpoma, Edo State credits history of these business owners as well. The descriptive research and the explanatory research designs were employed in this study. Two research questions were raised while one hypothesis was formulated to guide the study. Thirty five (35) business owners were randomly selected from Ekpoma metropolis of Edo state for this study based on loan applications and business capacity. The data collected were analyzed using Altman Z-scores, frequencies and percentages while the Pearson Product Moment Correlation Co-efficient was used to determine the relationship between Mathematical Scoring model and credits worthiness. The result showed that credit scores developed from borrower financial and non-financial records and history such as turnover, assets, previous loan repayment rate and trading capital perfectly classified them into five risk classes of A (Worthy and very able to payback), B (worthy and less able to pay back) and D (not worthy at all). The result revealed that credit score can safe award banks and creditors against credit risk default and loss of money. It was therefore recommended among others, that banks and credit facilities handlers should adopt mathematical credit scoring techniques to avoid loss of their money.
VL  - 6
IS  - 1
ER  -

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Author Information

Margaret Ose Asika

Department of Curriculum and Instruction, Faculty of Education, Ambrose Alli University, Ekpoma, Nigeria

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APA Style

Margaret Ose Asika. (2021). Knowing Ahead Mathematical Determinant of Bank Customers Credit Worthiness: A Safe Strategy for Funding Loan in a Critical Economy. Mathematics and Computer Science, 6(1), 16-23. https://doi.org/10.11648/j.mcs.20210601.13

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ACS Style

Margaret Ose Asika. Knowing Ahead Mathematical Determinant of Bank Customers Credit Worthiness: A Safe Strategy for Funding Loan in a Critical Economy. Math. Comput. Sci. 2021, 6(1), 16-23. doi: 10.11648/j.mcs.20210601.13

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AMA Style

Margaret Ose Asika. Knowing Ahead Mathematical Determinant of Bank Customers Credit Worthiness: A Safe Strategy for Funding Loan in a Critical Economy. Math Comput Sci. 2021;6(1):16-23. doi: 10.11648/j.mcs.20210601.13

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@article{10.11648/j.mcs.20210601.13,
  author = {Margaret Ose Asika},
  title = {Knowing Ahead Mathematical Determinant of Bank Customers Credit Worthiness: A Safe Strategy for Funding Loan in a Critical Economy},
  journal = {Mathematics and Computer Science},
  volume = {6},
  number = {1},
  pages = {16-23},
  doi = {10.11648/j.mcs.20210601.13},
  url = {https://doi.org/10.11648/j.mcs.20210601.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mcs.20210601.13},
  abstract = {The study was carried out to identify relevant attributes that signals the capacity of borrower to pay back the loan and determine the fit of mathematical scoring model to evaluate credit worthiness of a potential borrower. The data was taken from primary and secondary sources which was through the use of questionnaires (primary source) while the secondary source was collection of data from all the financial statements of selected business owners in Ekpoma, Edo State credits history of these business owners as well. The descriptive research and the explanatory research designs were employed in this study. Two research questions were raised while one hypothesis was formulated to guide the study. Thirty five (35) business owners were randomly selected from Ekpoma metropolis of Edo state for this study based on loan applications and business capacity. The data collected were analyzed using Altman Z-scores, frequencies and percentages while the Pearson Product Moment Correlation Co-efficient was used to determine the relationship between Mathematical Scoring model and credits worthiness. The result showed that credit scores developed from borrower financial and non-financial records and history such as turnover, assets, previous loan repayment rate and trading capital perfectly classified them into five risk classes of A (Worthy and very able to payback), B (worthy and less able to pay back) and D (not worthy at all). The result revealed that credit score can safe award banks and creditors against credit risk default and loss of money. It was therefore recommended among others, that banks and credit facilities handlers should adopt mathematical credit scoring techniques to avoid loss of their money.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - Knowing Ahead Mathematical Determinant of Bank Customers Credit Worthiness: A Safe Strategy for Funding Loan in a Critical Economy
AU  - Margaret Ose Asika
Y1  - 2021/03/10
PY  - 2021
N1  - https://doi.org/10.11648/j.mcs.20210601.13
DO  - 10.11648/j.mcs.20210601.13
T2  - Mathematics and Computer Science
JF  - Mathematics and Computer Science
JO  - Mathematics and Computer Science
SP  - 16
EP  - 23
PB  - Science Publishing Group
SN  - 2575-6028
UR  - https://doi.org/10.11648/j.mcs.20210601.13
AB  - The study was carried out to identify relevant attributes that signals the capacity of borrower to pay back the loan and determine the fit of mathematical scoring model to evaluate credit worthiness of a potential borrower. The data was taken from primary and secondary sources which was through the use of questionnaires (primary source) while the secondary source was collection of data from all the financial statements of selected business owners in Ekpoma, Edo State credits history of these business owners as well. The descriptive research and the explanatory research designs were employed in this study. Two research questions were raised while one hypothesis was formulated to guide the study. Thirty five (35) business owners were randomly selected from Ekpoma metropolis of Edo state for this study based on loan applications and business capacity. The data collected were analyzed using Altman Z-scores, frequencies and percentages while the Pearson Product Moment Correlation Co-efficient was used to determine the relationship between Mathematical Scoring model and credits worthiness. The result showed that credit scores developed from borrower financial and non-financial records and history such as turnover, assets, previous loan repayment rate and trading capital perfectly classified them into five risk classes of A (Worthy and very able to payback), B (worthy and less able to pay back) and D (not worthy at all). The result revealed that credit score can safe award banks and creditors against credit risk default and loss of money. It was therefore recommended among others, that banks and credit facilities handlers should adopt mathematical credit scoring techniques to avoid loss of their money.
VL  - 6
IS  - 1
ER  -

Copy | Download