Corporate credit granting is a key commercial activity of financial institutions nowadays. A critical first step in the credit granting process usually involves a careful financial analysis of the creditworthiness of the potential client. Wrong decisions result either in foregoing valuable clients or, more severely, in substantial capital losses if the client subsequently defaults. It is thus of crucial importance to develop models that estimate the probability of corporate bankruptcy with a high degree of accuracy. Many studies focused on the use of financial ratios in linear statistical models, such as linear discriminant analysis and logistic regression. However, the obtained error rates are often high. In this paper, Least Squares Support Vector Machine (LS-SVM) classifiers, also known as kernel Fisher discriminant analysis, are applied within the Bayesian evidence framework in order to automatically infer and analyze the creditworthiness of potential corporate clients. The inferred posterior class probabilities of bankruptcy are then used to analyze the sensitivity of the classifier output with respect to the given inputs and to assist in the credit assignment decision making process. The suggested nonlinear kernel based classifiers yield better performances than linear discriminant analysis and logistic regression when applied to a real-life data set concerning commercial credit granting to mid-cap Belgian and Dutch firms.
Download Info
To download:
If you experience problems downloading a file, check if you have the
proper application to
view it first. Information about this may be contained
in the File-Format links below. In case of further problems read
the IDEAS help
page. Note that these files are not on the IDEAS
site. Please be patient as the files may be large.
References listed on IDEAS Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.: