International Journal of Mathematics Trends and Technology
Volume 65 | Issue 7 | Year 2019 | Article Id. IJMTT-V65I7P505 | DOI : https://doi.org/10.14445/22315373/IJMTT-V65I7P505
Credit scoring has been regarded as a main tool of different companies or banks during the last few decades and has been widely investigated in different areas, such as finance and accounting. Different scoring techniques are being used in areas of classification and prediction, where statistical techniques have conventionally been used. We used ACC rate, which we believed was an important criterion, especially for new applications of credit scoring, because it highlighted the accuracy of the predictions. Confirmation of these values was done by the AUROC. Different Models were examined and the result showed that using Logistic Regression approach, 19.4% of the applicants were predicted false and 80.6% of them correct, which is relatively high compared to the other models, with the highest sensitivity and the lowest Type II error. That is to say, if we were credit officers, we would conclude that the model at hand, predicts 8 out of 10, the true status of each loan candidate.
[1] Abdou, H., Pointon, J. Credit scoring and decision-making in Egyptian public sector banks. International Journal of Managerial Finance 5 (4): 391-406., 2009.
[2] Al Amari, A. The credit evaluation process and the role of credit scoring: A case study of Qatar. Ph.D. Thesis, University College Dublin., 2002.
[3] Arnur, I.E., Credit Risk Modeling: Journal of Banking and Finance, 77(4), 122-126,, 2013.
[4] Bierens, H., The Logit Model: Estimation, Testing and Interpretation: The Pennsylvania State University, retrieved 18/8/2007,, http:// econ.la.psu.edu/, 2004.
[5] Boyle, M., Crook, J., Hamilton, R., and Thomas, L., Methods for credit scoring applied to slow payers. In: Thomas, L., Crook, J., and Edelman, D. (Eds),r, Credit scoring and credit control, Oxford University Press, Oxford, 75-90., 1992.
[6] Chang, E., Developing and validating credit scoring model for Taiwan’s enterprises., Paper presented to the 13 th Annual Conference on Pacific Basin Finance, Economics, and Accounting, Livingston Student Centre and Janice H. Levin Building Livingston Campus Rutgers University at New Brunswick, New Jersey, 2005.
[7] Cramer, J. S., Scoring bank loans that may go wrong:A case study. Statistica Neerlandica, 2004, 58(3), 365 -380.
[8] Crook, J. Credit constraints and US households. Applied Financial Economics, 6(6), 477-485, 1996.
[9] David Bigman., Globalization and the Developing Countries: Emerging Strategies for Rural Development and Poverty Alleviation. CABI. p. 136. ISBN 978-0-85199-575-5, 2002.
[10] Fernandes., F. L.. Classification methods applied to credit scoring: A systematic review and overall comparison., 2016.
[11] Gietzen, T. Credit Scoring vs. Expert Judgment, A Randomized Controlled Trial. St. Gallen., 2017.
[12] Greene, W., Sample selection in credit-scoring models., Japan and the World Economy, 10(3), 299-316, 1998.
[13] Hand, D., and Henley, W., Statistical classification methods in consumer credit coring: A review. Journal of the Royal Statistical Society, 160(3), 523, 541, 1997.
[14] Kern, A. M. Credit Scoring Analysis, Springer, 2017.
[15] Koehler, G., and Erenguc, S., Minimizing misclassifications in linear discriminant analysis: Decision Sciences, 21(1), 63-85, 2017.
[16] Srinivasan, V., and Kim, Y., Credit granting: A comparative analysis of classification procedures: The Journal of Finance, 42(3), 665-681, 1987.
[17] Thomas, L., Thomas, S., Tang, L., and Gwilym, O., The impact of demographic and economic variables on financial policy purchase timing decisions: Journal of the Operational Research Society, 56(9), 1051-1062, 2005.
[18] Thompson, P. Bank Lending and the Environment: Policies and Opportunities. International Journal of Bank Marketing 16 (6): 243-252., 1998.
[19] West, D., Dellana, S., Qian, J. Neural network ensemble strategies for financial decision applications. Computers & Operations Research 32 (10): 2543-2559., 2010.
[20] Yu, L., Wang S., Lai, K. An intelligent-agent-based fuzzy group decision making model for financial multicriteria decision support: the case of credit scoring. European Journal of Operational Research 195 (3): 942-959., 2009.
Bakker Daniel K, Odundo F, Nyakinda J, Paul Samuel F, "Performance Evaluation Criteria of Credit Scoring Models for Commercial Lenders," International Journal of Mathematics Trends and Technology (IJMTT), vol. 65, no. 7, pp. 23-30, 2019. Crossref, https://doi.org/10.14445/22315373/IJMTT-V65I7P505
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