International Journal of Intelligent Information Systems
Volume 2, Issue 2, April 2013, Pages: 34-39
Received: Apr. 24, 2013;
Published: May 30, 2013
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Z. Kirori, School of Pure & Applied Sciences, Karatina University, Karatina, Kenya
J. Ogutu, School of Computing & Informatics, University of Nairobi, Nairobi, Kenya
Mitigation of credit risk is a key aspect of portfolio management in any financial institution. This is primarily due to difficulties in uncovering uncertainties in information provided by credit applicants and also due to lack of reliable automated techniques that would improve the efficiency of manual underwriting procedures. In this paper, we report on the results of a MSc. Thesis1 in the application of an ensemble learning algorithm in development of a computer program that can greatly enhance the underwriting process. The implementation was based on the java netbeans development platform to create an interface that was used to train a model and its subsequent use in predicting credit decisions. The results obtained proved that such a mechanism can be applied to augment manual credit appraising processes, especially where large volumes of applications are to be processed within limited timeframes.
An Application of the Logitboost Ensemble Algorithm in Loan Appraisals, International Journal of Intelligent Information Systems.
Vol. 2, No. 2,
2013, pp. 34-39.
Qiwei G., Binjie L. (2008). Identifying Potential Default Loan Applicants - A Case Study of Consumer Credit Decision for Chinese Commercial Bank. Southwestern University of Finance and Economics, Chengdu, Sichuan, China.
Witten, I. H., and Frank, E. (2008). Data Mining Practical Machine Learning Tools and Techniques. ACM SIGKDD Explorations Newsletter Volume 11 Issue 1, June 2009 Pages 10-18.
Veronica S. M. (2003). Towards the use of C4.5 algorithm for classifying banking dataset. Integral, Vol. 8 No. 2. Pages 105-116
Martin, S. (2008). Ensemble Learning. UCL Department of Computer Science. Accessed from: http://machine-learning.martinsewell.com/ensembles/ensemble-learning.pdf.
Holmes, G., Pfahringer, B., Kirkby, R., Eibe, F., and Hall, M. (2003). Multiclass Alternating Decision Trees. Accessed from: www.cs.waikato.ac.nz/~mhall/pubs.html.
Bauer, E., Kohavi, R. (2006). An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants. Machine Learning, vv, 1-38
Friedman, J., Hastie, T., and Tibshirani, R. (2000). Additive logistic regression: a statistical view of boosting. Accessed from: http://www.stanford.edu/~hastie/Papers/AdditiveLogisticRegression/alr.pdf. Date:
Agresti A. (2007). Building and applying logistic regression models. An Introduction to Categorical Data Analysis. Hoboken, New Jersey: Wiley. Accessed from: http://onlinelibrary.wiley.com/doi/10.1002/9780470114759.ch5/summary
Shorouq, F. E., Saad, Ghaleb, Y., Ghaleb, A. E. (2010.). Applying Neural Networks for Loan Decisions in the JordanianCommercial Banking System. IJCSNS International Journal of Computer Science and Network Security, VOL.10 No.1
Liang-Hsuan C., and Tai-Wei C. (1999). A fuzzy credit-rating approach for commercial loans: a Taiwan case. Omega, International Journal of Management. Science. Vol 27, 407-419