European Journal Of Advances In Engineering And Technology
Published October 31, 2021 | Version v1
Journal article Open

Improving Credit Scoring Classification Performance using Self Organizing Map-Based Machine Learning Techniques

Authors/Creators

  • 1. Department of Mathematical Sciences, Federal University Dutsin–Ma, Katsina State, Nigeria
  • 2. Department of Mathematics, Adeyemi College of Education, Ondo, Ondo State, Nigeria
  • 3. Department of Mathematics and Statistics, Umaru Musa YarAdua University Katsina, Katsina State, Nigeria
  • 4. Department of Mathematics UsmanuDanfodiyo University, Sokoto, Sokoto State, Nigeria
  • 5. Department of Science, Mathematics Unit, School of Basic and Remedial Studies, Sokoto, Sokoto State, Nigeria

Description

ABSTRACT

This research usesself-organizing maps (SOM) in order improve the ability of the pattern recognition techniques including neural networks and K-nearest neighbour used to forecast the credit risk of borrowers from Bank of Agriculture (BOA) Sokoto. In this work, a hybrid approach to building the credit scoring model was proposed using the unsupervised learning based on self-organizing map (SOM) to specifically improve the discriminant capabilities of K-nearest neighbour and Neural networks. Within the two-stage scheme, the knowledge (i.e., prototypes of clusters) found by SOM is considered as input to the subsequent pattern recognition models. The results from BOA, Sokoto data indicate that the two-stage models improved the performances of Neural Network and K-nearest neighbour from 96.3% and 95.7% to 97.3% and 96.3% respectively.

Files

EJAET-8-10-28-35.pdf

Files (624.6 kB)

Name Size Download all
md5:fda7abab1b261fbdac2a7b596ff10b6d
624.6 kB Preview Download

Additional details

References

  • [1]. Abdou, H. & Pointon, J. (2011) "Credit scoring, statistical techniques and evaluation criteria: a review of the literature". Intelligent Systems in Accounting, Finance & Management, 18 (2-3), pp. 59-88.
  • [2]. Fritz, V. (2016). From form to function to sustainable solutions? Reforming public sectors in low income countries: New approaches and available evidence of "what works". Public Administration and Development, 36(5), 299-312.
  • [3]. Dahiya S., Handa S.S., and Singh N.P. (2015). "Credit Scoring Using Ensemble of Various Classifiers on Reduced Feature Set". Manav Rachna International University (MRIU), Department of Computer Science and Engineering., Faridabad, India.
  • [4]. Kohonen T (1982). "Self-organized formation of topologically correct feature maps". Biological Cybernetics 43:59–69.
  • [5]. Asan U. and Ercan S. (2012). "An Introduction to Self-Organizing Maps". Department of Industrial Engineering, Istanbul Technical University, 34357, Macka, Istanbul, Turkey.
  • [6]. W.E. Henley and D.J. Hand "A k-nearest neighbour classifier for assessing consumer credit risk". Royal Statistical Society. The Statistician, Volume 45, Issue 1 (1996), 77-95.
  • [7]. Priyanka D., Mishika S., Gopal P. & Amit H. (2020). "Credit Scoring: A Comparison between Random Forest Classifier and K- Nearest Neighbours for Credit Defaulters Prediction". International Research Journal of Engineering and Technology (IRJET).
  • [8]. S. Suleiman and S. Badamsi (2019) "Effect of Multicollinearity in Predicting Diabetes Mellitus Using Statistical Neural Networks". European Journal of Advances in Engineering and Technology, 2019, 6(6):30-38.
  • [9]. S.U. Gulumbe, S. Suleiman, S. Badamasi, A.Y. Tambuwal and Umar Usman (2019). "Predicting Diabetes Mellitus Using Artificial Neural Network through a Simulation Study". Machine Learning Research. Vol. 4, No. 2, 2019, pp. 33-38.
  • [10]. Ali A., Ning C. and Bernardete R. (2016) "Improve credit scoring using transfer of learned knowledge from self-organizing map", The Natural Computing Applications Forum 2016.
  • [11]. A.A. Sawant & P.M. Chawan (2013). "Study of Data Mining Techniques used for Financial Data Analysis". International Journal of Engineering Science and Innovative Technology, Vol. 3, Issue 3, May 2013.
  • [12]. S. Suleiman, S.U. Gulumbe and N. Shehu (2012) "Default predictors in Agricultural Credit Scoring: Evidence from Bank of Agriculture (BOA) Data". Nigerian Statistical Association (NSA) 2012 Conference Proceedings on the Theme Statistics: A Tool for National Transformation Held at Royal Choice In. Limited, Makurdi, benue State p32-43".