Credit Risk

This entry is part 4 of 4 in the series Risk of Bankruptcy

Other Methods

A number of other statistical methods have also been used to predict future company failure and credit risk, see: (Atiya, 2001), (Chandra, Ravi, Bose, 2009) and (Bastos, 2008). A recent study (Boguslauskas, Mileris , 2009) analyzed 30 scientific publications comprising 77 models:

  1. 63% used artificial neural networks (ANN)
  2. 53% used logistic regression (LR)
  3. 37% used discriminant analysis (DA)
  4. 23% used decision trees and (DT)
  5. 33% used various other methods

The general accuracy of the different models was evaluated: the proportion of companies correctly classified (figure 3 in the article):


The box and whisker plot above shows that logistic regression (87%) and artificial neural networks (87%) gives almost the same accuracy while decision trees (83%) and discriminant analysis (77%) seems to be less reliable methods.

However from the boxes it is evident that decision trees as a method have a much larger variance in classification accuracy than the others and that artificial neural network have the lowest variance. For logistic regression and discriminant analysis the variance is approximately the same.

Comparing methods based on different data sets can easily be misleading. Accurate parameter estimation relies heavily on available data and their usability for that particular method.


Atiya, Amir F. (2001). Bankruptcy prediction for credit risk using neural networks: a survey and new results. IEEE TRANSACTIONS ON NEURAL NETWORKS, 12(4), Retrieved from

Bastos, Joao. (2008, April 01). Credit scoring with boosted decision trees. Retrieved from

Boguslauskas, Vytautas. Mileris, Ricardas. (2009). Estimation of credit risk by artificial neural networks models. ECONOMICS OF ENGINEERING DECISIONS, 4(64), Retrieved from

Chandra, D. K., Ravi, V., Bose, I. (2009). Failure prediction of dotcom companies using hybrid intelligent techniques. Expert Systems with Applications, (36), 4830–4837.

Series Navigation<< The Probability of Bankruptcy
Print Friendly, PDF & Email


About the Author

S@R develops models for support of decision making under uncertainty. Taking advantage of recognized financial and economic theory, we customize simulation models to fit specific industries, situations and needs.

Post a Reply