Credit Prediction Using Transfer of Learning via Self-Organizing Maps to Neural Networks

  • Ali AghaeiRadEmail author
  • Bernardete Ribeiro
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 517)


For financial institutions, the ability to predict or forecast business failures is crucial, as incorrect decisions can have direct financial consequences. Credit prediction and credit scoring are the two major research problems in the accounting and finance domain. A variety of pattern recognition techniques including neural networks, decision trees and support vector machines have been applied to predict whether borrowers are in danger of bankruptcy and whether they should be considered a good or bad credit risk.

In this paper a clustering and unsupervised method named Self Organizing Map (SOM) is used. We propose to label each cluster with voted method and improve labeling process by training a feedforward Neural Network (NN). The approach uses transfer of learning via SOM to the NN, is tested on the Australian Credit Approval financial data set. We compare both approaches and we will discuss which one is the best prediction model for financial data.


Credit scoring Self-organizing map Neural networks Financial risk analysis Transfer learning 


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  1. 1.
    Kohonen, T.: Self-Organized Formation of Topologically Correct Feature Maps. Biological Cybernetics 43, 59–69 (1982)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Greene, W.H.: A statistical model for credit scoring. NYU Working Paper No. EC-92-29 (1992)Google Scholar
  3. 3.
    Davidian, D.: Feed forward neural network. Google Patents, US Patent 5, 438, 646 (1995)Google Scholar
  4. 4.
    Dietterich, T.G., Bakiri, G.: Solving multiclass learning problems via error-correcting output codes. Journal of Artificial Intelligence Research, 263–286 (1995)Google Scholar
  5. 5.
    Vapnik, V.N.: Statistical Learning Theory. John Wiley & Sons, Inc. (1998)Google Scholar
  6. 6.
    Thomas, L.C.: A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers. International Journal of Forecasting 16(2), 149–172 (2000)CrossRefGoogle Scholar
  7. 7.
    Japkowicz, N.: Supervised learning with unsupervised output separation. In: International Conference on Artificial Intelligence and Soft Computing, vol. 3, pp. 321–325 (2002)Google Scholar
  8. 8.
    Merkevičius, E., Garšva, G., Simutis, R.: Forecasting of credit classes with the self-organizing maps. Informaciens Technologijos 4(33), Valdymas (2004)Google Scholar
  9. 9.
    Yu, L., Wang, S., Lai, K.K.: Credit risk assessment with a multistage neural network ensemble learning approach. Expert Systems with Applications 34(2), 1434–1444 (2008)CrossRefGoogle Scholar
  10. 10.
    Khashman, A.: Neural networks for credit risk evaluation: Investigation of different neural models and learning schemes. Expert Systems with Applications 37(9), 6233–6239 (2010)CrossRefGoogle Scholar
  11. 11.
    Yap, B.W., Ong, S.H., Husain, N.H.M.: Using data mining to improve assessment of credit worthiness via credit scoring models. Expert Systems with Applications 38(10), 13274–13283 (2011)CrossRefGoogle Scholar
  12. 12.
    Chen, N., Ribeiro, B., Vieira, A.S., Duarte, J., Neves, J.C.: A genetic algorithm-based approach to cost-sensitive bankruptcy prediction. Expert Systems with Applications 38(10), 12939–12945 (2011)CrossRefGoogle Scholar
  13. 13.
    Karem, F., Dhibi, M., Martin, A.: Combination of supervised and unsupervised classification using the theory of belief functions. In: Denoeux, T., Masson, M.-H. (eds.) Belief Functions: Theory and Applications. AISC, vol. 164, pp. 85–92. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  14. 14.
    Chen, N., Ribeiro, B.: A consensus approach for combining multiple classifiers in cost-sensitive bankruptcy prediction. In: Tomassini, M., Antonioni, A., Daolio, F., Buesser, P. (eds.) ICANNGA 2013. LNCS, vol. 7824, pp. 266–276. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  15. 15.
    Merkevičius, E., Garšva, G., Simutis, R.: Forecasting of credit classes with the Self-Organizing Maps. Information Technology and Control 33(4) (2015)Google Scholar
  16. 16.
  17. 17.

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.CISUC, Department of Informatics EngineeringUniversity of CoimbraCoimbraPortugal

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