Estimating Probabilities of Default using Support Vector Machines

Abstract

Optimizing capital allocation by better estimating probability of default requires generally new model selection. An analysis of German solvent and default companies was performed using the promising Support Vector Machines (SVM) methodology. The analysis shows good performance of the SVM compared to the Logit model with respect to the accuracy indicators. Also, the SVM scores enable the estimation of probabilities of default for new companies.